Archive for the ‘posts’ Category
Hunter Walk shared some thoughts on notifications and the challenges he and (certainly through twitter) many people see. Many of the companies I’ve met with see design challenges in how much and when to offer up notifications. There’s a long history of trying different approaches and modalities to notifications and so it seems worth some additional perspective for those familiar with what we see today on modern mobile platforms.
Notifications are one of those features where everyone has an opinion, and rightfully so. The feature is so visible and for just about everyone seems so close to being helpful but yet always off by just a little. There’s a general UX principle that is worth considering, which is anytime you push some feature on your customer you really want it to be right (correct, useful, helpful) for him/her 100% of the time. If not, chances are your customer will recall the negatives of the feature far more than the positives. This applies to notifications, autocorrect, form completion, and more. If you find yourself putting a lot of design energy into how your customer can undo or dismiss your best guess at what was intended, then you’re probably being too aggressive.
Anytime you push some feature on your customer you really want it to be right for him/her 100% of the time.
In some ways, today’s Notification Centers are the extreme case of “we’re collectively going to be wrong so often that we’re just going to put stuff in one place” or “there’s just so much we app developers have to tell you that the platforms are squeezing it all into one place to avoid cluttering up the platform itself”.
It is as if it isn’t enough we have to manage all of our apps, bookmarks, and preferences, we now have to manage all the ways apps tell us stuff we might want to know. Hunter’s raised the complaint (to a chorus of agreement) that after a two weeks, you have to go in and turn off notifications on a new app. Of course this improves battery life, reduces chatter, and then as some noted causes you to start to ignore the app.
What’s an app developer to do?
What to do?
In the PC era a lot of effort went into honing the design of verbs and interaction. It took a decade to develop the right approaches to menus, toolbars, status bars, panes, and more. That’s because many apps were essentially a large set of verbs this was the big design challenge. The rough equivalent to this design challenge is the role of notifications in mobile. That’s because in a mobile world many apps exist to be essentially a stream of information.
Notifications suffer a clear tension between platform pm and the app pm.
Platform pm wants to contain apps to the app experience, extending the walled-garden such that apps don’t interfere with other apps. By definition a notification is a way for one app to interfere with other apps. Platform pm sees notifications as necessary far less frequently than app pm might. This leads to a set of APIs that offer a clear, albeit limited, view of what a notification is and what it can do. This seems reasonable and we all want the platform folks maintaining a global view of consistency in approach and control.
App pm sees the world through the lens of their app. The assumption is that someone downloading and using an app has made a choice to count on that app for the purpose it was designed, and so whether it is an airline app alerting you to a flight (or a potential discount on a future flight), a bank with a balance alert (or advertising a new bank feature), or a communication tool letting you know of some inbound message (or alerting you that a friend is now on line) the app pm sees any of these as worthwhile reasons to interfere with your flow or context. On all platforms, apps often design their own type of notifications that get used while you are using the app (for in app purchase or feature advertising) because the platform is not rich enough. All this seems legitimate, and certainly at the time of initial designs.
Over time the initial designs from both parties tend to lead to an expansion in the ability to interrupt you. Each subsequent release of a platform almost always adds more capabilities to enhance and customize notifications in an effort to offer more while also trying to keep the noise in the system manageable. Each app adds more and more notifications in an effort to more deeply engage with customers and likely to encourage customers to use more surface area of the app.
Many who use iOS 7 have spent quite a bit of time mired notification customization. Here is an overview of the iOS 7 features, both the notifications and notification center, worth a look if you’re on Android or not sure of the impressive depth that Apple designed for notification. Android is probably not quite at the same level of consistency and control, though as you might expect there are several apps that can help you customize notifications of other apps.
At the extreme we end up with two core design challenges.
Notification spam. This one is easy. Too many apps just think too much of what is going on is important to you. Like too much of any design the burden falls to app product managers to just be more thoughtful. Like so many elements of any platform, when there is a view that making money depends on getting folks to use more of a product or spend more time in a product, the platform starts to look a bit like “surface area to be exploited”. Like the old Start Menu and desktop in Windows, the more places an app can “infuse” itself and invade your space the better. On Android we see this in the share item menu as another bit of surface area to be gamed or exploited.
Notification action. The most common issue with notification is that your flow is interrupted and then you seem to be pulled into a state of distraction until you deal with the inbound notice. We each have our own human based algorithms for how to cope. We always jump on SMS. We (almost) always ignore a ringing phone. We wish we could find a way for some app or another to stop bugging us so we uninstall it.
On iOS there is very little you can do to a notification other than dismiss it or just jump directly to the app or place in the app that generated the notification. Modal or must-act notifications are generally discouraged. The resulting notification center then turns into a list to read that spans a bunch of apps and for some ends up to be a list of stuff you’ve already seen popup or in context or just a reminder to get to the app.
On Android, the design takes a different approach which is to enable notifications that can take actions. This is where the elegance of notifications is really stretched, but in a way that many find appealing. For example, when you receive a new mail message the gmail notification lets you archive it based on reading the initial content or various SMS clients offer you the ability to reply.
On Windows Phone, one has the additional option of pivoting notifications by person on your home screen so you can glance and see that there is activity by person. This has a natural appeal when there are a small set of folks you care deeply about but as a general purpose mechanism it might not scale particularly well.
The core challenge with offering verbs with notifications is almost “classic” in that one can never off the right set of verbs because eventually the design turns into attempting to implement the a substantial number of features of the app in the notification. Mail is a great challenge: delete, file, reply, flag, etc. all become possible verbs. Each usage pattern in aggregate leads to the whole mail experience. The more users you have the larger the group of customers that don’t like the subset of verbs you picked.
Ultimately taking action based on a notification turns into a bit of a frustration in that the notification centers essentially offer a new way to launch all your apps. What was a nice feature turns into a level of indirection almost all the time.
Therein is the opportunity. In a world where many people are almost constantly glancing at their phones and wanting to know more about what is going on in their digital lives and a world where almost every app represents an endless stream of information along with in-app notifications, it seems that notifications need a different level of semantics.
For example, with just a few friends Facebook always has something new to see so why notify you of the obvious. For many, Twitter is essentially a notification engine. Mail certainly is a constant stream, arguably of decreasing importance. In other words, it isn’t even clear what makes sense to notify you about when the natural behavior is to periodically launch apps to see what’s new within the app context and apps are generating new information all the time.
Similarly, if most everyone knows that when you are talking to another human you both have to turn your phones upside down to avoid being distracted (or sharing private information), then there’s a good chance we’ve collectively missed the mark notifications. The iOS “do not disturb” is an awesome feature but yet it seems to undo all the work in both the notification center and in the apps.
My view is that a feature that requires us to customize it before it becomes useful or less annoying is defaulted the wrong way. Of course this is literally impossible with a product used by more than a few people, since any design at all will have both critics and shortcomings. However, it is possible to default to “out of the way” and then provide a mechanism for people to decide what they might want to be notified about once a usage pattern is established.
For example, I might assert that for an app like mail, sms, Facebook, or Twitter the simple iOS badge is enough. We are all in and out of these apps enough during the day that a specific notification is redundant with the in-app notifications already there.
Each app can almost certainly step back and either know a priori or offer a mechanism that puts people in control of their experience with notifications. It is almost certainly the case that if we’re bouncing in and out of apps all the time but really do want to know if SMS comes from a loved one amongst the 100′s of SMS many get each day, that is likely the way to design a feature.
It is easy to imagine using more context (loved the twitter suggestion to not notify while driving/moving fast). It is easy to imagine more machine learning applied to notifications. But I think we can start from a fresh perspective that the mechanisms provided are just being over-used to begin with when we look at modern usage patterns.
–Steven Sinofsky (@stevesi)
How often do you hear things like “let’s ban email”, “no more attachments”, “death to PowerPoint decks”, “we’re going paperless”, “meeting free friday” or one of dozens of “bans” designed to do away with something that has become annoying or inefficient in the workplace? If you’re around long enough you can see just about anything cross over from innovative new tool to candidate to be banned. The problem is that banning a tool (or process) in an attempt at simplification never solves the problem. Rather, one should to look at a different approach, an approach that focuses on the work not the tool or process.
What’s the problem?
It is well understood that new technologies go through an adoption curve. In the classic sense it is a normal distribution as described by researchers in the 1950′s. More recently and generally cited in the software world is Geoffrey Moore’s Crossing the Chasm which describes a slightly different path. These models all share a common view of a group of early adopters followed by a growing base of users of a technology.
While adoption is great, we are all too used to experiencing excess enthusiasm for new technologies. As a technology spreads, so does the enthusiasm. Invariably some folks use the technology to the the point of abusing it. From reply all to massive attachments to elaborate scorecards with more dimensions than anyone can understand, the well-intentioned enthusiastic user turns a game-changing tool into a distraction or worse.
Just as with adoption curves, once can create a conceptual “irritation curve” and overlay it with adoption. Of course what is pictured below is not based on any data or specific to any technology, but consistent with our collective anecdotal point of view.
The key is that at some point the adoption of a new product crosses the chasm and becomes widely used within a company. While there is a time delay, sometimes years, at some point the perceived “abuse” of the technology causes a cross-over where for some set of people the irritation outpaces the utility. Just as there are early adopters, there are also irritation canaries who are the first to feel the utility of the new technology declining with increased usage.
We see this same dynamic not just for tools, but for business processes as well. That status report, dashboard, or checkin mail all start off as well-intentioned and then after some period of time the “just one more thing” or spreading over-usage at all levels of a team turn a positive into a burden.
Then at some point people start to reject the tool or process. Some even call for an outright ban or elimination.
What’s the solution?
The way to break the cycle is to dive into the actual work and not the tool. Historically, tools fade away when the work process changes.
It is tough to find examples of popular tools and processes that were simply banned that did not make a comeback. Companies that ban meetings or email on fridays just have more meetings and email on monday-thursday. I’ve personally seen far too many examples of too much information crammed on to a page (smaller fonts or margins anyone) or slides that need to be printed rather than projected in an effort to squeeze more on a page when there are forced limits on story-telling.
On the other hand, from voice mail to fax machines to pagers to typewriters to voice calls we have examples of tools that achieve high and subsequently irritating usage levels can and do go away because new tools take over. If you were around for any of those then you know that people called for them to be banned and yet they continued, until one day we all just stopped using them.
A favorite historical example is a company that told me they removed all the typewriters when PCs were introduced. The company was trying to save time because typewriters were much more difficult to use than PCs with printers (of course!). The problem was immediately seen by those responsible for the workflows in the company–all of a sudden no one could fill out an expense report, transfer to another department, or pay an invoice. All of these work processes, the blizzard of paperwork that folks thought were caused by typewriters, were rendered inoperable. These processes all required a typewriter to fill out the form and the word processors had no way of navigating pre-printed forms in triplicate. Of course what needed to happen was not a pre-printed form that worked in a word processor (what the administrative folks asked for), but a rethinking of the workflow that could be enabled by new tools (what management needed to do).
This sort of rethinking of work is what is so exciting right now. It is fair to say that the established, and overloaded, desktop work-processes and tools of the past 20 years are being disrupted by a new generation of tools. In addition to re-imagining how work can be done to avoid the problems of the past, these tools are built on a modern, mobile, cloud, and social infrastructure.
For example, Tom Preston-Werner, co-founder of GitHub, tells a great story about the motivations for GitHub that echoes my own personal experience. As software projects grew the communication of code changes/checkins generate an overwhelming blizzard of mail. Rather than just shut down these notifications and hope for the best, what was needed was a better tool so he invented one.
At Asana, Dustin Moskovitz, tells of their goal to eliminate email for a whole set of tracking and task management efforts. We’ve all seen examples of the collaborative process playing out poorly by using email. There’s too much email and no ability to track and manage the overall work using the tool. Despite calls to ban the process, what is really needed is a new tool. So Asana is one of many companies working to build tools that are better suited to the work than one we currently all collectively seem to complain about.
Just because a tool is broadly deployed doesn’t mean it is the right or best way to work.
We’re seeing new tools that are designed from the ground up to enable new ways of working and these are based on the learning from the past two decades of tool abuse.
What are some warning signs for teams and managers?
It is easy to complain about a tool. Sometimes the complaints are about the work itself and the tool is just the scapegoat. There’s value in looking at tool usage or process creation from a team or management perspective. My own experience is that the clarion calls to ban a tool or process have some common warning signs that are worth keeping an eye out for as the team might avoid the jump to banning something, which we know won’t work.
- Who is setting expectations for work product / process? If management is mandating the use of a tool the odds of a rebellion against it go up. As a general rule, the more management frames the outcome and the less the mechanism for the outcome the more tolerance there will be for the tool. Conversely, if the team comes up with a way of working that is hard for outsiders to follow or understand, it is likely to see pushback from partners or management. However, if it is working and the goal is properly framed then it seems harmless to keep using a tool. Teams should be allowed to use or abuse tools as they see fit so long as the work is getting done, no matter how things might look from outside.
- Does the work product benefit the team doing the work or the person asking? A corollary to above is the tool or process that is mandated but seems to have no obvious benefit is usually a rebellion in-waiting. Document production is notorious for this. From status reports to slides to spreadsheets, the specification by management to create ever more elaborate “work products” for the benefit of management invariably lead to a distaste for the tool. It is always a good idea for management to reduce the need to create work, tools, and processes where the benefit accrues to management exclusively. Once again, the members of the team will likely start to feel like banning the use of the tool is the only way to ease the overload or tax.
- Do people get evaluated (explicitly or implicitly) on the quality of the work product/process or the end-result? A sure-fire warning sign to the looming distaste of a tool or process is when a given work product becomes a goal or is itself measured. Are people measured by the completion of a report? Does someone look at how many email notifications get generated by someone? Does someone get kudos for completing a template about the group’s progress? All of these are tools that might be considered valuable in the course of achieving the actual goals of the team, but are themselves the path along the way. Are your status reports getting progressively more elaborate? Are people creating email rules to shunt email notifications to a folder? Are people starting to say “gosh I must have missed that”? All of those are warning signs that there is an impending pushback against the tool or process.
- What doesn’t get done if you just stop? The ultimate indicator for a need to change a tool or process is to play out what would happen if you really did ban it. We all know that banning email is really impractical. There are simply too many exceptions and that is exactly the point. Many tools can have a role in the modern workplace. Banning a tool in isolation of the work never works. Taking a systematic look at the work required that uses a tool, those that use the tool, and those that benefit from the output is the best way to approach the desire to use the most appropriate toolset in the workplace.
What tools need to change in your organization? What work needs to change so that the team doesn’t need to rely on inappropriate or inefficient tools?
PS: As I finished writing this post, this Forrester report came across twitter: Reality Check: Enterprise Social Does Not Stem Email Overload.
For me, 1984 was the year of Van Halen’s wonderful [sic] album, The Right Stuff, and my second semester of college. It would also prove to be a time of enlightenment for me and computing. On this 30th anniversary of the Apple Macintosh on January 25 and the Superbowl commercial on January 22. I wanted to share my own story of the way the introduction of the Macintosh profoundly changed my path in life.
Perhaps a bit indulgent, bit it seemed worth a little backstory. I think everyone from back then is feeling a bit of nostalgia over the anniversary of the commercial, the product, and what was created.
High School, pre-Macintosh
Like many Dungeons and Dragons players my age, my first exposure to post-Pong computing was an Atari 800 that my best friend was lucky enough to have (our high school was not one to have an Apple ][ which hadn’t really made it to suburban Orlando). While my friends were busy listening to the Talking Heads, Police, and B-52s, I was busy teaching myself to program on the Atari. Even though it had the 8K BASIC cartridge it lacked tape storage. Every time I went over to use the computer I had to start over. Thinking about business at an early age (I suppose) I would continue to code and refine what I thought would be a useful program for our family business, the ability to compute sales tax on purchases from different states. Enter the total sale, compute the sales tax for a state by looking up the rate in a table.
My father, an entrepreneur but hardly a technologist, was looking to buy a computer to “automate” our family business. In 1981, he characteristically dove head first into computing and bought an Osborne I. For a significant amount of money ($1,795, or $4,600 today) we owned an 8 bit CPU and two 90K floppy drives and all (five) of the business programs one could ever need.
I started to write a whole business suite for the business (inventory, customers, orders) in BASIC which is what my father had hoped I would conjure up (in between SATs and college prep). Well that was a lot harder than I thought it would be (so were the SATs). Then I discovered dBase II and something called a “database” that made little sense to me in the abstract (and would only come to mean something much later in my education). In a short time I was able to create a character-based system that would be used to run the family business.
To go to college I had a matching Osborne I with a 300b modem so I could do updates and bug fixes (darn that shipping company–they changed the rate on COD shipments right during midterms which I had hard-coded!).
College Fall Semester
I loaded up the Osborne I and my Royal typewriter/daisy wheel/parallel port “letter quality” printer and was off to sunny Ithaca.
Computer savvy Cornell issued us our “BITNET electronic mail accounts”, mine was TGUJ@CORNELLA.EDU. Equal parts friendly, memorable, and useful and no one knew what to do with them. The best part was email ID came printed on a punch card. As a user of an elite Osborne I felt I went back in time when I had to log on to the mainframe from a VT100 terminal. The only time I ever really used TGUJ was to apply for a job with Computer Services.
I got a job working for the computer services group as a Student Terminal Operator (STO). I had two 4 hour shifts. One was in the main computer science major “terminal room” in Upson Hall featuring dozens of VT100 terminals. The other shift was Friday night (yes, you read that correctly) at the advanced “lab” featuring SGI graphics workstations, IBM PC XTs, an Apple Lisa, peripherals like punch card machines, and a 5′ tall high-speed printer. For the latter, I was responsible for changing the ribbon, a task that required me to put on a mask and plastic arm-length gloves.
It turned out that Friday night was all about people coming in to write papers on the few IBM/MS-DOS PCs using WordPerfect. These were among the few PCs available for general purpose use. I spent most of the time dealing with graduate students writing dissertations. My primary job was keeping track of the keyboard templates that were absolutely required to use WordPerfect. This experience would later make me appreciate the Mac that much more.
In the computer science department I had a chance to work on a Xerox Star and Alto (see below) along with Sun Workstations, microVAX mini, and so on. The resources available were an incredible blessing to the curious. The computing world was a cacophony of tools and platforms with the vast majority of campus not yet tapping into the power of computing and those that did were using what was most readily accessible. Cornell was awash in the sea of different computing platforms, and to my context that just seemed normal, like there were a lot of different types of cars. This was especially apparent from my vantage point in the computer facilities.
One experience with a new, top-secret, computer was about to change all that.
I ended up getting to use a new computer from an unidentified company. One night after my shift, a fellow STO dragged me back to Upson Hall and took me into a locked room in the basement. There I was able to see and use a new computer. It was a wooden box attached to a wall with an actual chain. It had a mouse, which used on the Xerox and Sun workstations. It had a bitmap screen like a workstation. It had an “interface” like the Xerox. There was a menu bar across the top and a desktop of files and folders. It seemed small and much more quiet than the dorm-refrigerator sized units I was used to hearing.
What was really magical about it was that it had a really easy to use painting program that we all just loved. It had a “word processor”. It was much easier to use than the Xerox which had special keys and a somewhat overloaded desktop metaphor. It crashed a lot even after a short time using it. It also started up pretty quickly. Most everything we did with it felt new and different compared to all the other computers we used.
The end of the semester and exams approached. The few times and couple of hours I had to play with this computer were exciting. In the sea of computing options, it was definitely the most exciting thing I had experienced. Perhaps being chained to the wall added to the excitement, but there was something that really resonated with us. When I try to remember the specifics, I mostly recall an emotional buzz.
My computing world was filled with diversity, and complexity, which left me unprepared for the way the world was going to change in just the next six weeks.
To think about Apple’s commercial, one really has think about the context of the start of the year 1984. The Orwellian dialog was omnipresent. Of course as freshman in college we had just finished our obligatory compare/contrast the dystopian messages in Animal Farm, Brave New World, and 1984 not to mention the Cold War as front and center dialog at every turn. The country emerging from recession gave us all a contrasting optimism.
At the same time, IBM was omnipresent. IBM was synonymous with computing. Sure the Charlie Chaplin ads were great, but the image of computing to almost everyone was that of the IBM mainframe (CORNELLA was located out by the Ithaca airport). While IBM was almost literally the pillar of innovation (just a couple of years later, scientists at IBM would spell IBM with Xenon atoms), there was also great deal of distrust given the tenor of the time. The thought of a globally dominant company, a computer company, was uncomfortable to those familiar with fellow Cornellian Kurt Vonnegut’s omnipresent RAMJAC.
Then the Apple commercial ran. It was truly mesmerizing (far more so to me than the Superbowl). It took me about one second to stitch together all that was going on right before my eyes.
Apple was introducing a new computer.
It was going to be a lot different from the IBM PC.
The world was not going to be like 1984.
And most importantly, the computer I had just been playing with weeks earlier was, in fact, the Apple Macintosh.
I was so excited to head back to the terminal rooms and talk about this with my fellow STOs and to use the new Apple Macintosh.
Upon returning to the terminal room in Upson, Macs had already started to replace VT100s. First just a couple and then over time, terminal access moved to an emulation program on Macs (rumor had it that the Macs were actually cheaper than terminals!).
My Friday night shift was transformed. Several Macs were added to the lab. I had to institute a waiting list. Soon only the stalwarts were using the PCs. I started to see a whole new crowd on those lonely computer nights.
I saw seniors in Arts & Sciences preparing resumes and printing them on the ImageWriter (note, significantly easier to change the ribbon, which I had to do quite often every night). Those in the Greek System came by for help making signs for parties. Students discovered their talent with MacPaint pixel art and fat bits. All over campus signs changed overnight from misaligned stencils to ImageWriter printouts testing the limits of font faces per page.
I have to admit, however, I spent an inordinate amount of time attempting to recover documents that were lost to memory corruption bugs on the original MacWrite. The STOs all developed a great trouble shooting script and signs were posted with all sorts of guesses (no more than 4 fonts per document, keep documents under 5 pages, don’t use too many carriage returns). We anxiously awaited updates and students would often wait in line to update their “MacWrite disks” when word spread of an update (hey, there was no Internet download).
In short order, Macintosh swept across campus. Cornell along with many schools was part of Apple’s genius campaign on campuses. While I still had my Osborne, I was using Macintosh more often than not.
The next couple of years saw an explosion of use of Macintosh across campus. The next incoming class saw many students purchasing a Mac at the start of college. Research funds were buying Macs. Everywhere you looked they were popping up on desks. There was even a dedicated store just off campus that sold and serviced Macs. People were changing their office furniture and layout to support using a mouse. Computer labs were being rearranged to support local printers and mice. The campus store started stocking floppy disks, which became a requirement for most every class.
Document creation had moved from typewriters and limited use of WordPerfect to near ubiquitous use of MacWrite practically by final exams that Spring. Later, Microsoft Mac Word, which proved far more robust became the standard.
The Hotel School’s business students were using Microsoft Mac Excel almost immediately.
The Chemistry department made a wholesale switch to Macintosh. The software was a huge driver of this. It is hard to explain how difficult it was to prepare a chemistry journal article before Macintosh (the department employed a full time molecular draftsman to prepare manuscripts). The introduction of ChemDraw was a turning point for publishing chemists (half my major was chemistry).
It was in the Chemistry department where I found a home for my fondness of Macintosh and an incredibly supportive faculty (especially Jon Clardy). The research group had a little of everything, including MS-DOS PCs with mice which were quite a novelty. There were also Macs with external hard drives.
I also had access to MacApp and the tools (LightSpeed Pascal) to write my own Mac software. Until then all my programming had been on PCs (and mainframes, and Unix). I had spent two summers as an intern (at Martin Marietta, the same company dBase programmer Wayne Ratliff worked!) hacking around MS-DOS writing utilities to do things that were as easy as drag and drop on a Mac or just worked with MacWrite and Mac Excel. As fun as learning K&R, C, and INT 21h was, the Macintosh was calling.
My first project was porting a giant Fortran program (Molecular Mechanics) to the Mac. Surprisingly it worked (perhaps today, equally surprising was the existence of a Fortran compiler). It cemented the lab’s view that the Macs could also be for work, not just document creation. Next up I just started exploring the visualizations available on the Mac. Programming graphics was all new to me. Programming an object-oriented event loop seemed mysterious and indirect to me compared to INT 21h or stdio.
But within a few hacking sessions (fairly novel to the chemistry department) the whole thing came together. Unlike all of the previous systems I used, the elegance of the Mac was special. I felt like the more I used it the more it all made sense. When I would bury myself in Unix systems programming it seemed more like a series of things, tricks, you needed to know. Macintosh felt like a system. As I learned more I felt like I was able to guess how new things would work. I felt like the bugs in my programs were more my bugs and not things I misunderstood.
The proof of this was that through the Spring semester my senior year I was able to write a program that visualized the periodic table of the elements using dozens of different variables. It was a way to explore periodicity of the elements. I wrote routines for an X-Y plot, bar charts, text tables, and the pièce de résistance was a 2.5-dimensional perspective of the periodic table showing a single property (commonly used to illustrate the periodic nature of electron affinity). I had to ask a lot of friends who were taking computer graphics on SGIs for help! Still, not only had I just been able to program another new OS (by then this was my 5th or 6th) but I was able to program a graphical user interface for the first time.
MacMendeleev was born.
The geek in all of us has that special moment when at once you feel empowered and marvel at a system. That day in the spring of 1987 when I rendered a perspective drawing from my own code on a system that I had seen go from a chained down plywood box to ubiquity across campus was magical. Even my final report for the project was, to me, a work of art.
The geek in all of us has that special moment when at once you feel empowered and marvel at a system.
It wasn’t just the programming that was possible. It wasn’t just the elegance and learnability of the system. It wasn’t even the ubiquity that the Macintosh achieved on campus. It was all of those. Most of all it represented a tool that allowed me to realize some of my own potential. I was awful at Chemistry. Yet with Macintosh I was able to contribute to the department and probably showed a professor or two that in spite of my lack of actual chemistry aptitude I could do something (and dang, my lab reports looked amazing!). I was, arguably, able to learn some chemistry.
I achieved with Macintosh what became one of the most important building blocks in my education.
I’m forever thankful for the empowerment that came from using a “bicycle of the mind”.
I’m forever thankful for the empowerment that came from using a “bicycle of the mind”.
What came next
Graduate school diverged in terms of computing. We used DEC VMS, though SmallTalk was our research platform. So much of the elegance of the Macintosh OS (MacApp and Lisa before that) was much clearer to me as I studied the nuances of object-oriented programming.
I used my Macintosh II to write papers, make diagrams, and remote into the microVAX at my desk. I also used Macintosh to create a resume for Microsoft with a copy of Microsoft Word I won at an ACM conference for my work on MacMendeleev.
I also used Macintosh to create a resume for Microsoft with a copy of Microsoft Word…
When I made it to Microsoft I found a great many shared the same experience. I met folks who worked on Mac Excel and also had Macs in boxes chained to tables. I met folks who wrote some of those Macintosh programs I used in college. So many of the folks in the “Apps” team I was hired into that year grew up on that unique mixture of Mac and Unix (Microsoft used Xenix back then). We all became more than converts to MS-DOS and Windows (3.0 was being developed when I landed at Microsoft).
There’s no doubt our collective experiences contribute to the products we each work on. Wikipedia even documents the influence of MacApp on MFC (my first product contribution), which was by design (and also by design was where not to be influenced). It is wonderful to think that through tools like MFC and Visual Basic along with ubiquitous computing, Windows brought to so many young programmers that same feeling of mastery and empowerment that I felt when I first used Macintosh.
Fast-forwarding, I can’t help but think about today’s college students having grown up hacking the web but recently exposed as programmers to mobile platforms. The web to them is like the Atari was to me—programmable, understandable, and fun. The ability to take your ideas, connect them to the Internet, touch your creation, and make your own experience must feel like building a Macintosh program from scratch felt like to me. The unique combination of mastery of the system, elegance of design, and empowerment is what separates a technology from a movement.
Macintosh certainly changed my path in life…
For me, Macintosh was an early contributor to my learning, skills, and ultimately my self-confidence. Macintosh certainly changed my professional path in life. For sure, 1984 was not at all like 1984 for me.
Yes, of course I’m a PC (and definitely a Surface). Nothing contributed more to my professional life than the PC!
PS: How far have we come? Check out this Computer Chronicles from 1985 where the new Macintosh is discussed.
I was talking with a founder/CEO of an enterprise startup about what it is like to disrupt a sizable incumbent. In the case we were talking about the disrupting technology was losing traction and the incumbent was regaining control of the situation, back off their heels, and generally felt like they had fended off the “attack” on a core business. This causes a lot of consternation at the disrupting startup as deals aren’t won, reviews and analyst reports swing the wrong way, and folks start to question the direction. If there really is a product/market fit, then hold on and persevere because almost always the disruption is still going to happen. Let’s look at why.
The most important thing to realize about a large successful company reacting to a disruptive market entry is that every element of the company just wants to return to “normal” as quickly as possible. It is that simple.
Every action about being disrupted is dictated by a desire to avoid changing things and to maintain the status quo.
If the disruption is a product feature, the motion is figuring out how to tell customers the feature isn’t that important (best case) or how to quickly add something along the lines of the feature and move on (worst case). If the disruption is a pricing change then every effort is about how to “manage customers” without actually changing the price. If the disruption is a new and seemingly important adjacent product, then the actions focus on how to point out that such a product isn’t really necessary. Across the spectrum of potential activities, it is why the early competitive responses are often dismissive or outwardly ignore the challenger. Aside from the normal desire to avoid validating a new market entry by commenting, it takes a lot of time for a large enterprise to go through the work to formulate a response and gain consensus. Therefore an articulate way of changing very little has a lot of appeal.
Status quo is the ultimate goal of the incumbent.
Once a disruptive product gains enough traction that a more robust response is required, the course of action is almost always one that is designed to reduce changes to plans, minimize effort overall, and to do just enough to “tie”. Why is that? Because in a big company “versus” a small company, enterprise customers tend to see “a tie as a win to the incumbent”. Customers have similar views about having their infrastructure disrupted and wish to minimize change, so goals are aligned. The idea of being able to check off that a given scenario is handled by what you already own makes things much easier.
Keep in mind that in any organization, large or small, everyone is at or beyond capacity. There’s no bench, no free cycles. So any change in immediate work necessarily means something isn’t going to get done. In a large organization these challenges are multiplied by scale. People worry about their performance reviews; managers worry about the commitments to other groups; sales people worry about quarterly quotas. All of these worries are extremely difficult to mitigate because they cross layers of managers and functions.
As much as a large team or leader would like to “focus” or “wave a wand” to get folks to see the importance of a crisis, the reality of doing so is itself a massive change effort that takes a lot of time.
This means that the actions taken often follow a known pattern:
- Campaign. The first thing that takes place is a campaign of words and positioning. The checklist of features, the benefits of the existing product, the breadth of features of the incumbent compared to the new product, and so on. If the new product is cheaper, then the focus turns to value. Almost always the campaign emphasizes the depth, breadth, reliability, and comfort of the incumbent’s offer. A campaign might also be quite negative and focus on a fear, compatibility with existing infrastructure, or conventional wisdom weakness of a disruptor, or the might introduce a pretty big leap of repositioning of the incumbent product. A good example of this is how on-premises server products have competed with SaaS by highlighting the lack of flexibility or potential security issues around the cloud. This approach is quick to wind up and easy to wind down. Once it starts to work you roll it out all over the world and execute. Once the deals are won back then the small tiger team that created the campaign goes back to articulating the product as originally intended, aka normal.
- Partnership. Quite often there can be a competitive response of best practices or a third-party tool/add-on that appears to provide some similar functionality. The basic idea is to use someone else to offer the benefit articulated by a disruptive product. Early in the SaaS competition, the on-premises companies were somewhat quick to partner with “hosting” companies who would simply build out a dedicated rack of servers and run the traditional software “as a service”. This repotting plants approach to SaaS has the benefit that once the immediate crisis is mitigated, either the need to actually offer and support the partnership ends or the company just becomes committed to this new sales channel for existing products. Again, everything else continues as it was.
- Special effort. Every once in a while the pressure is so great internally to compete that the engineering team signs up for a “one off” product change or special feature. Because the engineering team was already booked, a special effort is often something carefully negotiated and minimized in scope and effort. Engineering minimizes it internally to avoid messing up dependencies and other features. Sales will be specific in what they expect the result to do because while the commitment is being made they will likely begin to articulate this to red-hot customer situations. At the extreme, it is not uncommon for the engineering team to suggest to the sales organization that a consultant or third-party can use some form of extensibility in the product to implement something that looks like the missing work. The implications of doing enterprise work in a way that minimizes impact is that, well, the impact is minimized. Without the proper architecture or an implementation at the right level in the stack, the effort ultimately looks incomplete or like a one-off. Almost all the on-premise products attempting to morph into cloud products exhibit this in the form of features that used to be there simply not being available in the “SaaS version”. With enough wins, it is almost likely that the special effort feature doesn’t ever get used. Again, the customer is just as likely to be happy with the status quo.
All of these typical responses have the attribute that they can be ignored by the vast majority of resources on a business. Almost no one has to change what they are doing while the business is responding to a disruptive force. Large incumbents love when they can fend off competitors with minimal change.
Large incumbents love when they can fend off competitors with minimal change.
Once the initial wave of competitive wins settles in and the disruptive products lose, there is much rejoicing. The teams just get back to what they were doing and declare victory. Since most of the team didn’t change anything, folks just assume that this was just another competitor with inferior products, technology, approaches that their superior product fended off. Existing customers are happy. All is good.
Or is it?
This is exactly where the biggest opportunity exists for a disruptive market entry. The level of complacency that settles into an incumbent after the first round of victories is astounding. There’s essentially a reinforcing feedback loop because there was little or no dip in revenue (in fact if revenue was growing before then it still is), product usage is still there, customers go back to asking for features the same as they were before, sales people are making quota, and so on. Things went back to normal for the incumbent.
In fact, just about every disruption happens this way–the first round or first approaches don’t quite take hold.
Why is this?
- Product readiness can improve. Obviously the most common is that the disruptive product simply isn’t ready. The feature set, scale, enterprise controls, or other attributes are deficient. A well-run new product will have done extensive early customer work knowing what is missing and will balance launching with these deficiencies and with the ability to continue to develop the product. In a startup environment, a single company rarely gets a second shot with customers so calibrating readiness is critical. Relative to the broader category of disruption, the harsh reality is that if the disruptor’s idea or approach is the right one but the entry into the market was premature, the learning will apply to the next entry. That’s why the opportunity for disruption is still there. It is why time to market is not always the advantage and being able to apply learning from failures (your own or another entry) can be so valuable.
- Missing ingredient gets added. Often a disruptive product makes a forward-looking bet on some level of enterprise infrastructure or capability as a requirement for the new product to take hold. The incumbent latches on to this missing ingredient and uses it to create an overall state of lack of readiness. If there’s one thing that disruptors know, it is not to bet against Moore’s law. If your product takes more compute, more storage, or more bandwidth, these are most definitely short-term issues. Obviously there’s no room for sloppy work, but by and large time is on your side. So much of the disruption around mobile computing was slowed down by the enterprise issues around managing budgets and allocation of “mobile phones”. Companies did not see it as likely that even better phones would become essential for life outside of work and overwhelm the managed phone process. Similarly, the lack of high-speed mobile networks was seen as a barrier, but all the while the telcos are spending billions to build them out.
- Conventional wisdom will change. One of the most fragile elements of change are the mindsets of those that need to change. This is even more true in enterprise computing. In a world where the average tenure of a CIO is constantly under pressure, where budgets are always viewed with skepticism, and where the immediate needs far exceed resources and time, making the wrong choice can be very costly. Thus the conventional wisdom plays an important part in the timeline for a disruption taking hold. From the PC to the GUI to client/server, to the web, to the cloud, to acceptance of open source each of these went through a period where conventional wisdom was that these were inappropriate for the enterprise. Then one day we all wake up to a world where the approach is required for the enterprise. The new products that are forward-looking and weather the negatives wishing to maintain the status quo get richly rewarded when the conventional wisdom changes.
- Legacy products can’t change. Ultimately the best reason to persevere is because the technology products you’re disrupting simply aren’t going to be suited to the new world (new approach, new scenarios, new technologies). When you re-imagine how something should be, you have an inherent advantage. The very foundation of technology disruption continues to point out that incumbents with the most to lose have the biggest challenges leading through generational changes. Many say the enterprise software world, broadly speaking, is testing these challenges today.
All of these are why disruption has the characteristic of seeming to take a much longer time to take hold than expected, but when it does take hold it happens very rapidly. One day a product is ready for primetime. One day a missing ingredient is ubiquitous. One day conventional wisdom just changes. And legacy products really struggle to change enough (sometimes in business or sometimes in technology) to be “all in” players in the new world.
Of course all this hinges on an idea plus execution of a disruptive idea. All the academic theory and role-playing in the world cannot offer wisdom on knowing if you’re on to something. That’s where the team and entrepreneur’s intuition, perseverance, and adaptability to new data are the most valuable assets.
The opportunity and ability to disrupt the enterprise takes patience and more often than not several attempts, by one or more players learning and adjusting the overall approach. The intrinsic strengths of the incumbent means that new products can usually be defended against for a short time. At the same time the organization and operation of a large and successful company also means that there is near certainty that a subsequent wave of disruption will be stronger, better, and more likely to take hold simply because of the desire for the incumbent to get back to “normal”.
–Steven Sinofsky (@stevesi)
I love the Consumer Electronics Show. Maybe I’m numb from decades of attending it. Maybe I’m just too much of a fan of watching stuff get made. Maybe I just like long lines and the potential for airborne illness. Really what I love is the technology industry and that every year we get together and demo new products, share works in progress, and take chances on offering products people don’t yet (or ever) know they want. CES 2014 was an exceptionally unique year and one that I think will be remembered as the start of a new era, much how the 1970 show changed TV with the introduction of the VCR or the 1981 show changed music with the CD player.
CES 2014 was an exceptionally unique year and one that I think will be remembered as the start of a new era.
But wait, you ask “What product was launched at CES 2014?” The answer is “None”. Instead, this is a year in which every product is about software, and every product assumed that the computer involved would be based on modern mobile platforms, and most everything connected to a cloud service. As an industry we’re not there yet, as we will talk about below, some offerings still cling to previous models of accessing computing and we’re likely to see much changing of the guard as breakthrough products emerge.
The ubiquity of the modern mobile platform, smartphones and tablets, might seem obvious to all of us in computing proper, but it took the better part of a decade for it to go from a section of the show to a big presence to woven into the fabric of every exhibitor. Likewise, software has gone from “content” to “console games” to “pc applications that get thrown in with a device” to the raison d’être or differentiation of consumer electronics.
So put aside the lines, the endless sameness of non-differentiated products, the puzzling keynotes, or even the absence of Apple and Google, and consider the over 3200 companies of all sizes showing off products of all kinds. For me, I think back to when I was a kid and the excitement around what was next came at the yearly Auto Show or reading about the historical World’s Fair Expos. It is hard to avoid concluding that CES is our era’s expression of the future—transportation, healthcare, communication, entertainment, and more are represented by the innovation on display at CES.
It is hard to avoid concluding that CES is our era’s expression of the future—transportation, healthcare, communication, entertainment, and more are represented by the innovation on display at CES.
Me, I’m just excited to get to go to the show and systematically walk up and down every aisle exploring what is there to see. My one set of eyes and one post can’t compete with the likes of the professional tech press that push out hundreds of posts during the week or with the amazingly thorough coverage of “best of” done by many.
Instead, I offer these observations or themes from a product development perspective—what would I be looking at as a product manager or engineer. As I’ve said in this blog many times, learning comes from observing and sharing. Product plans come from many points of view and sources coming together in the context of a company. I cover a lot, but there is more. It was a great show for learning and thinking about the next phase of our industry.
This report looks at themes covering embedded smarts, healthcare devices, communication wearables, screens (4K, curved, skinny), less futzing, and overall trends up/down.
First, a bit of humility
One thing required when looking at new products and technologies is humility. Even though many would like to differ, CES is not a shopping mall where you go to find the new big thing to buy or use right away. This is counter-intuitive because a lot of the products at CES are new and for sale. But in practice, they have not been used and many times not even released to reviewers yet. So you want to step back as you read about the products and not look through the lens of “would I buy and use this today” and instead think about the context overall. As part of that I like to remind myself of a few things about what we see:
- Companies aren’t dumb. A lot of times when a product is first seen something jumps out at you as totally wrong. Keep in mind many of the products are not about the use cases for today, but for use cases yet to be seen. The most classic example is the Walkman— a “tape recorder” that didn’t record. Or more recently, a digital camera that is bigger, heavier, costlier, and worse than a film camera. Sometimes the new use cases aren’t even obvious to the companies yet, and this is even more true today as many “hardware” companies are moving forward rapidly with hardware or the supply chain is making available new components because it can, neither really having software that can implement new use cases.
- Limitations seen in less than one minute are known by the product people. Every product has issues, limitation, constraints. Walking up to a brand new product and thinking you’re the first person to notice such is usually a mistake. While the person at the booth might know the FAQ, it is a good idea to assume the product folks back at HQ actually know the limitations. I can’t count how many times people commented on the battery life of one of the wearables with screens—as though the people developing them would not like to have a month of battery life or were not aware of the trade-off between weight and battery life.
- Iteration is baked into the product you are seeing. Even though the product is for sale, it might not be done yet. It will get smaller, faster, cheaper, power efficient, lighter, and more feature rich. It will do so quickly. Many of those plans are in place. Because so much of the hardware is now subject to Moore’s Law, it is already happening and you can just wait—the price of 4K displays will drop rapidly and because of 1080P volume the price is already spectacular compared to what we’ve come to expect based on previous generations. For software, we all know updates and features are part of the plan. There’s no guarantee things will go in the “right” direction for every product but iteration will happen. Because there are many players, keep in mind that iteration by one player becomes learning for another player so there is ample opportunity for changes in leadership. We all know in technology, first mover advantage is not necessarily an advantage. Multi-party, iteration is the reason.
- Core competency matters. With so many devices doing so many things and so many products incorporating features from other products for differentiation, it is important to focus on the core competency of a product. There’s a good chance a product will try to do too much or for that matter all the products will try to differentiate themselves based on some peripheral features. Don’t lose sight that TVs should have a good picture, fitness bands should measure your fitness well, scales should be fast and easy to read, speakers should sound good and so on.
- Everything has depth and experts. Every year I get surprised by some product that I never thought of and think how amazing that idea is, and then I see 3 more of them on the show floor. It is easy to forget that inside the CE industry there are many industries. Within those industries are people who spend their careers mastering something that, to the uninitiated, might seem narrow. I saw a modern blood sugar monitor (see below) this year that was totally unique. Then I saw two more. These experts are all feeding off many of the same inputs and so one should expect some degree of convergent innovation. Said another way, in the context of a broad show like CES, something that I think is really cool might not actually be all that innovative to those in the field with some domain knowledge.
Let’s look at some themes and within them put on our product manager hats and see how what we observed might influence our own choices in products design. I’m going to take the observations from the show floor and project forward a bit as that’s what product management needs to do with the data when there are technology bets to be made, products to design, and specs to write.
Intel kicked off the show with a keynote declaring that all devices need to be smart. Walking around the show floor showed that this advice has already been taken to heart. While smart TVs are the most obviously visible (and also a holdover from the past two or so years), we also saw smart cars, smart healthcare devices, smart fitness monitors, smart watches, smart home appliances, smart projectors, and more. Smart was everywhere. Should it be?
Smart can mean anything from a touch-based user interface replacing the existing mechanical UI to taking a formerly mechanical device and embedding an entire OS with app ecosystem into the device.
Moore’s Law is an important contributor to this trend. Previous views of smart devices would have meant connecting the hardware device to a PC, with all of the costs, size, power that this entails. Home automation that used to take a PC now just connects devices with Wi-Fi to a cloud service, for example. A home blood pressure monitor would have stored some number of readings until you connected it with a serial cable to a PC and now it just sends those over Wi-Fi to a cloud service. TVs would have been connected to a PC that presented a full PC experience through an alternate user interface that today can offer this same type of functionality through an entirely embedded solution. Now it is both feasible and economic to include an ARM-based computing platform and either a Linux or Android OS driving the “smarts”.
But is this always right? The product manager view might be that it is time to look at use cases and scenarios and step back. While the hardware side is possible, the software might not be delivering the right experience. The truth is, some devices should be dumb. And that’s ok. The internet of things does not need to recreate the challenges of the internet of PCs. A single general purpose approach used everywhere might not be the best approach compared to tailored devices working with a very rich mobile device and cloud services.
The truth is, some devices should be dumb. And that’s ok.
One reason for this is that there can only be so many app ecosystems. It simply won’t be possible for apps to be delivered reliably and in a feature complete manner across all of the various smart devices. While today it might be possible for a streaming music service to be omnipresent on every possible smart device from a watch to a car to a TV to a refrigerator to a treadmill (and a phone and a tablet), down the road the user experience for that streaming app will have become rich enough that the primary use case will drive the expected experience which won’t be duplicated across devices, whether that is because the devices vary in capabilities, screen sizes, or just human interaction or just because there are too many different platforms.
Two examples help to reinforce this product challenge.
- Screens / TV. We all want lots of stuff on our big screens. We want streaming video, live broadcast television, music (maybe), and perhaps some web services like messaging. But these are all sophisticated experiences (finding the video, dealing with TV signals/guides/DVR, managing playlists, different apps), and so it means they likely demand (or will demand) a rich interaction model connected to services. Good news! We already have this interaction model on our modern mobile tablets and phones. Why try to duplicate this with the added complexity and variety of TVs? Rather a device like Chromecast or Apple TV shows how you can use the TV simply as a “dumb screen” which becomes far more manageable, the UI is far better, and is a much better overall experience. These solutions, where the screen is dumb and the mobile device serves as the gateway to the dumb screen seem to put the code in the right place and reduce complexity and increase simplicity for the use case. It is worth asking if this Twitter client on a TV will ever match what you can do on your mobile device in your hand while watching the show? That’s not to say there won’t be other use cases integrating apps into TV, but just showing subsets of the mobile apps side by side doesn’t seem right.
- Autos. From Audi to Volvo we saw smarts added to cars. This was added in the form of a screen, a telemetry platform, and apps. What is different about this compared to TVs is that we don’t want cars to be dumb. We want cars to be smart about being cars (safety, maintenance, better driving and accident avoidance). Like TVs, however, it isn’t clear that we want to put the equivalent of a unique mobile platform in every car brand. Is there any chance the mapping app in a car will be on par with the mapping app in my mobile device? Wouldn’t I rather have the same ability to send my mobile screen to the car screen that I get with Chromecast or Apple TV? Perhaps having a protocol that supports touch in that scenario is very helpful too. In the meantime the smarts of the car can focus on the things the car needs to do, and perhaps even recognize the best way to have a user experience and manage those would be with an app and cloud service? Ultimately, the way cars are made means that the technology choices are out of date by the time the car makes it to market and if you own the car for 5 years then those technology choices are really dated and perhaps the overall resale value of the car declines.
Will this UX really be right today or in 8 years?
The fact that all the screens and cars are making bets on technologies that are just capable of being used helps us all—this is not the time to be cynical but the time to learn. These products are not done yet and we can’t highlight the greatness of the Lean Startup and MVP and then be critical of bringing to market products that might not quite be done—that’s where reviews, experts, and frankly store return policies can help. As a product manager you want to ask yourself about the trajectory and likelihood of success of an approach down the road when the work all comes together. These new products show exciting scenarios but maybe there are better ways to implement them.
The advances in sensors have been breathtaking thanks to technologies like MEMS and others. Combining those with the ability to embed and whole OS and connectivity to cloud services in what used to be basic diagnostic equipment is a revolution in healthcare. Here too we saw many new and breakthrough products. As a telemetry nut, obsessive compulsive, and geek these are some of the most exciting products ever. One thing that made this CES seem so new and fresh is that this feels like a renaissance in consumer electronics. Devices you buy at reasonable price points, solve specific problems like an appliance, and just work for a scenario. In most ways, these new devices are starting to deliver now.
Basic body telemetry like weight, blood pressure, composition and more can now be easily measured, tracked over time, and even shared easily with care givers or compared with a circle of friends. Stepping on a scale every morning is quietly making a bar chart, setting alerts, and trending your data. And even better, such devices are learning from past designs and becoming easier to setup and use. No longer do you need a PC, EXE, and USB cable. Instead the device is paired over Bluetooth with a dedicated app and you’re up and running with a great UI in no time. Basic scenarios like maintaining compliance with medication are made easier by smart pill boxes that alert you wirelessly on your mobile device to take medicine. Overkill? Perhaps, but compliance rates are still not where they need to be. And combine this with easy measurement of blood pressure and you can see how putting smart in the right place, cloud services and mobile apps to make things accessible can be such a huge advance.
Three healthcare products that demonstrate this include:
- Head injury. Much has been written about the rise in head injury in sports and long term risk associated with cumulative concussion, particularly football. Reebok with the Checklight is one of many companies with a product designed to measure cumulative head impact using accelerometers. The packaging is very user friendly as you can see (and it won a best of CES award). The basics of the device are cool—a red light goes off when a certain level of cumulative concussion risk has been reached. Other variants of similar devices have different form factors (helmet integrated, mouth guard) and can even report real time to a mobile app. The telemetry, use case, and execution are all coming together at CES 2014.
- UV exposure. The JUNE UV detection bracelet by netamo simply measures cumulative sun exposure and integrates with a mobile app, again with a simple UI on the device and a data connection to a mobile app. There’s a lot to like about this for folks who work or play outdoors and want to mitigate the risk of skin disease or damage. The app/service provides advice and a suggested “routine” for your skin based on data.
- Blood glucose. Those that have been touched by diabetes (perhaps one of the more insidious diseases in the developing world, costing the US an estimated $245B a year in healthcare and related costs) know the complexity and challenges of testing and managing glucose levels. The YoFi Meter, http://www.yofimeter.com/, is a very smart device (see the above discussion). It combines a glucose test strip dispenser/reader, a lancet dispenser, along with a simplified tracking interface on a touch screen and an integrated 3G connection to a cloud data service. This is a device that takes compliance to a new level. At first I might have thought this device is too smart, and then after talking to the designer I learned of many use cases where a companion mobile phone isn’t available or possible (for example, students must be tested by the nurse who doesn’t have time to call up parents with real time data and a phone might not be available in school).
Each of these three devices shows how telemetry and mobile apps/cloud services can dramatically change the basics of healthcare for a scenario.
There are challenges we will all need to deal with however. These challenges are not new to those who already work with data. Data does not always lead to actionable next steps and sometimes more data leads to more ambiguity. These three devices show how the reality is that science is not yet caught up to being able to present us with all this data.
In the case of concussion and head injury, right now the data is unclear on how much cumulative impact over what period of time is “safe”. So while it can be measured, exactly when and how to act is not clear, particularly for children. It is easy to see how the debate will quickly move to one of acceptable levels. So more studies over a longer period of time will be needed which for this type of measurement will take decades given that the measurement is just now available. Science is hard. Glucose measurement is the other end of the spectrum. For diabetics the data and management is well understood, but compliance is challenging or at least not super convenient. The advances are amazing and ready now! Sun exposure is one that becomes interesting only because the data basically says to minimize exposure as much as possible—in other words there’s not really an acceptable level of UV light (i.e. SPF 40, see http://www.aad.org/media-resources/stats-and-facts/prevention-and-care/sunscreens).
Together these show the opportunities and challenges in the healthcare telemetry space. In any product design, the ability to measure something and present the measurement to a customer is not the same as being able to provide reliable and actionable information. It is critical in the design of a product to be clear on what to do when the product tells you something, lest the dreaded “Check Engine’ syndrome.
One might even offer the world’s first connected toothbrush:
While many healthcare devices are wearable, the broader category itself exploded this year as has been well documented…everywhere. When it comes to sophisticated wearable communication devices, this is a year of learning products. Most are not ready for primetime or broad usage, though many will find niches with early adopters or enthusiasts.
This shouldn’t be news to anyone. Consider as an example post-VHS digital formats for movies forays into optical media (LaserDisc anyone), the path from first products to broadly used products is often one with many twists and turns in basic technology and scenarios. In addition that path from the first component sized DVD player to the 6″ round portable DVD player or integrated flat screen DVD player took some time. Innovation does not happen all at once, even though we often remember it as punctuated moments in time.
There’s no need to document the dozens of communication wearables on display. Most shared the same basic characteristics, with Pebble being the established player that has already earned an enthusiastic base of early adopters. These pair with a mobile device, share notifications, and permit some level of interactivity and apps/ecosystem. Some do less and trade off towards a longer battery life by doing less. Others try to subsume the mobile device entirely and act as a phone (see below).
The primary “cause” of this is that the ability to miniaturize the hardware platform and squeeze a full software platform on the device has surpassed the ability to build a software experience and use case. These devices, by and large, are currently in the “because we can” phase of innovation. That’s not bad and in fact when software meets hardware it is often a necessary ordering.
The primary challenge, at least from my perspective, is that no one has arrived at a new use case. We’re simply looking to move some use cases of the mobile device to a wrist based device. But the device on the wrist is “less of everything”. Taking a disruption point of view, this isn’t disruptive yet. As often discussed, the first PC-derived tablets were more PCs without keyboards than they were a new set of use cases (pen based drawing/notetaking notwithstanding). It wasn’t until the iPad presented a new set of mobile scenarios and capabilities and the hardware was better able to meet the scenarios that a device without a keyboard was able to define a new use case.
Absent a use case, the dialog around wearables will just bounce around the constraints of screen size and battery life. You can only do so much with a tiny screen and a wrist sized device can only operate a screen for so long. While one likes to be notified with a UX based on a glance, it turns out this is pretty much what mobile phone designers work to do all the time and with a lot more screen real estate and elaborate UX. On those platforms the debate is an endless one around how much can you do to a notification and what are the verbs that act on it. Is a new text just read, read and reply with a canned set of replies (and can/how might those be customized), or full messaging capabilities? Do those choices extend to custom messaging apps like WhatsApp or Skype? Who will write those apps? When those apps have new features do they carry over to the wrist?
Here is one example of a fitness watch that is also full smartphone on a wrist, including a pull out Bluetooth headset.
This is a complex set of questions. From a product manager perspective, they all boil down to defining use cases and scenarios for why a device should exist. Is it a companion? Is it a replacement? What does it do uniquely such that I’d be willing to forgo other functions I already have? Disruption theory says that it is totally ok for a new product to do less, so long as it is so good at something that people want more as that’s the whole point of being disruptive.
It is still early. It is too early to judge these devices as what is possible and for most of us too early to be adopting these devices. They are the stuff of Star Trek, which by that theory of innovation only means it is a matter of time.
Screens: 4K, curved, and skinny
If you’ve seen just one article on CES then it is certain you know that new TVs on display were both 4K and curved. Cutting to the chase, if you buy a TV in 2015 odds are it will be 4K. The rapid march to 4K is more massive than anything we’ve seen in screens. Moore’s Law is our friend here and at some point the entire supply chain will just convert to the mechanics of making 4K and it becomes essentially non-economic to maintain the old processes and supply chain. We’ve seen this with memory, storage, processors, and screens. Silicon based innovation lends itself to rapid and whole movement of products. That’s good for all of us.
Cutting to the chase, if you buy a TV in 2015 odds are it will be 4K.
For screens, 4K has two unique elements:
- Extremely rapid cost reduction. Competition is fierce and the economics of the processes will likely drive 4K screens to “acceptable” consumer prices much more quickly than the move to flat screen or 1080P. During the show Dell announced a 4K monitor for $699. My early adopter 15″ VGA LCD costs $1999 (and weighed more than the Dell will). Yay for consumers!
- Content will appear. While we can all bemoan the hype and failure cycle of 3D at home, which included a lack of content, there were already significant content deals for 4K, notably Netflix. While you need 15MB bandwidth for 4K the content will be there. Rest assured, the rest of the content industry heard this and so I suspect we will see brilliant 4K content of some form very rapidly. Again, yay for consumers!!
If you have any doubts, once you see a 4K screen you will want one. Put aside all the arguments about physics, optics, and more, it just feels right. In practice, what you really want are high gamut and 60fps, so let’s hope these attributes and benefits become clear to consumers. Again, this shows the value of reviews, community, and expertise in the adoption of CE.
Curved screens were somewhat of a surprise to many attendees I believe. In booth after booth people had somewhat puzzled looks at them and there seemed to be a broad effort to quiz the booth staff with “so what good is it”. Most of the time we all got the general answers about immersive experience or less reflection. Each of these to some degree are true (especially reflectivity in many situations).
Again, as product managers we see a hardware technology appear because it can but the use case hasn’t yet been determined. Like the first color computer screens that many argued against claiming software was inherently black and white, curved screens are about new use cases not just curving a football game. One view around CES and you can easily see scenarios such as signage that become incredibly cool. Today signs that are interactive are much cooler than static signs (or menus and more). Signs that need to be on curved surfaces are static and boring. Maybe curved displays will be a niche at home and find themselves useful only for commercial signs. I’m going to bet on the creativity of content and product people to develop new use cases and before we know it curved screens will be ubiquitous as “flat screens”.
Finally, this year saw a great many more wide-aspect ratio screens at 21:9. For the most part, the mass market of screens are made in a small set of aspect ratios depending on mass adoption. Like film was historically, there are both benefits to this along with those that want to experiment with alternate aspect ratios. The iOS tablet world is 4:3 and the Windows/Android/HD world is 16:9. The ultra-wide 21:9 seems rather appealing for a number of use cases, including side by side and multiple inputs. If you combine the ultra-wide screen, more pixels, and curved display you recreate a developer workstation or Bloomberg terminal but with a single screen which can mean less space, easier ergonomics, and perhaps less power. Again, it seems like the use cases are going to quickly follow the technical ability to make the screen.
The product manager view of screens is to consider what you app or content can do when being projected on or making content for these new capabilities. As we saw with Retina pixel density, these changes can happen quickly and getting left behind is not always a good spot to be.
The evolution of most CE devices is often to more features and customization, and over time this can be viewed negatively. Certainly at some point this complexity makes products unappealing for many. The industry has a great many enthusiasts who love to customize and tweak. Analogous to the auto shows, some people used to love to look under the hood and adjust the engine.
Back in the heyday of Auto Shows (they are still huge, but CES and tech have eclipsed those shows in media coverage in my biased view), the talk was about components of cars. This dialog was broad and understood. Average consumers knew about horsepower, disc brakes, electronic fuel injection. Today cars are about design, convenience, and use-centric concepts like capacity and MPG. CES, this year in particular, has transitioned to talking and showing more about use cases and less about how products are built.
In almost all devices you have to look hard to find gigahertz or megabytes. You see tasks or uses much more up front. This isn’t always the case and often the first questions are about specs. Still, I would say a lot of “progress”.
Some examples of this jumped out at me, particularly in the gaming world. The gaming world has traditionally been split between consoles representing the true CE experience and the gaming PC which defined the ultimate enthusiast experience when it came to moding your gaming PC. For gamers or those that want to just play games this is a banner time with an explosion in gaming options. Many believe the usage in phones and tablets will dominate with casual games available in app stores. The new consoles from Sony and Microsoft promise to bring gaming to new technical levels with their advanced PC componentry in a true CE package. Finally, at CES we saw the SteamOS powered devices (PCs) and an example of a more state of the art or “modern” PC.
Steam Machine. SteamOS promises to bring the simplicity of consoles with the power of PC gaming. Some critics are saying it brings neither and is in-between. But the popularity of the Steam platform is significant with millions of intensely active members. The product manager question is whether Steam disrupts PC (or console) gaming or simply extends the life of a gaming platform that while popular is being squeezed between consoles and mobile devices. Is the SteamOS powered device re-imagination of PC gaming or an appealing convergence of the PC with consoles (see http://blog.learningbyshipping.com/2014/01/07/the-four-stages-of-disruption/)?
A Steam Machine is an Intel-based device meeting a set of baseline specs, combined with the Steam Controller and SteamOS. The debate among gamers is about the specs and capabilities of the underlying hardware along with the lack of ability to mod the devices. The Steam Machines themselves represent a broad range of “sealed case” form factors, most of which existed as Windows game PCs in various forms.
Razer Project Christine. Razer introduced Christine, which is a highly stylized modular PC. While the idea behind a modular from factor has been tried several times before, the combination of hardware interop, industrial design, and openness to accessorization (my own word) are at a unique point in time. Razer has a very active customer based that thrives the combination of gaming and accessories for gaming. It might just be that this hassle-free notion of moding a gaming PC will appeal to a broad set of PC gaming customers. In many ways this is an attempt to disrupt the PC gamer, while maintaining a commitment to customization. Project Christine beat out Steam Powered for CES Best of Show in the category.
Reduced futzing is really enabled across a broad range of CE devices because of mobile apps, WiFi/Bluetooth connectivity, and cloud services. What used to be elaborate setup and configuration is now enabled via simple apps that connect to devices over wireless protocols. The rich UX afforded by devices replaces single line LED displays or embedded web server experiences. The ability to save/restore data and settings in the cloud replaces sharing via dedicated (and awkward) subset experiences for social networks. From WiFi access points to scales to cameras, we will spend (and tolerate) less time futzing and more time using CE devices.
From a product manager perspective what excites me about these two innovations and the broader theme is the move “up the stack”. Our computing industry has broadly moved to modern platforms for both hardware and software and seeing gaming move in this direction is critical to the health of this style of rich interactive gaming. It is also a natural maturing of a technology area and to resist the change essentially guarantees disruption. The market for people willing to devote time to futzing is shrinking, no matter how much we (having built more PCs than I can count) enjoyed it. There are simply too many options for how to spend more time gaming and less time futzing. Just like people want to use cars to get around, not stare under the hood and fix them before going somewhere, the move up the stack is relentless for most every consumer.
Trending up and down
To wrap up a quick look at the year over year view of CES and what is on the move taking up more floor space and mind share and what is taking less beyond the items mentioned above.
Android. There was a lot more Android this year than last year. Android of course is particularly popular among wearables and TVs where there is no third party option to use iOS. The inexpensive Android tablets we all heard about from the holidays were on display where you could see the dozens of OEMs packaging every conceivable screen size and spec into tablets. One note is that there was a complete absence of 4:3 Android devices, which I find interesting given the competitive nature of things. What this feels like is a reaction to avoid being like Apple, when in practice it might be that for some device sizes the squarer aspect ratio might be more convenient.
Chromebooks. Building off what we might have read as momentum in the US over the holidays was a broader presence of Chromebooks. This year saw an all-in-one along with several lighter and thinner (and still inexpensive) clamshell formats.
Phablets. It was interesting to see the number of show attendees using their really big phones (or small tablets). I would do a quick badge check and noted that more often than not the phablet user was not an employee of Samsung or LG, but just a user. I think this is a trend worth watching. If you have only one device the tradeoff towards a bigger screen becomes interesting.
WWAN connectivity. Looking back personally, I was totally wrong on WWAN connectivity. I did not see a market where the carriers would make it so easy and relatively inexpensive to add another device to a data plan. From the glucose meter to auto fleet tracking to wearables, SIM cards were everywhere. This in spite of the fact that the hardware costs are real and the data plan is real. Many products, particularly those using WWAN for sending telemetry to a cloud service, will include a SIM and fixed data plan as part of the device price or part of the service plan, which is super cool.
3D Printers. The 3D printers are all remarkable. It is hard to overstate how much this will disrupt so many fields. There’s a lot of talk about a drone delivering a product, but what about just downloading it and printing it at home. The use cases for 3D printers are not at the broad consumer level but soon will be.
Touch screens. All the major screen makers also showed screens that were touch capable. The most common touch detector was an IR field. All of these were hooked up to PCs. Every screen is a touchscreen or it feels broken. Also pictured below is Panasonic’s 4K portable running Windows.
PCs. While Microsoft, HP, Dell, and others having stopped exhibiting on the main show floor can easily be responsible for the lack of PCs, the reality is that PCs are part of the fabric of the technology world, but not the front and center consumer electronics device. The reality of the floor is that PCs were not part of the use cases or scenarios and the modern mobile platforms have taken over. This image below sort of summed it up. Here we had RCA showing their new PCs (fairly thick and heavy laptops) in a booth with toasters and dorm refrigerators. Yet we all know, today, the products at the show were built using PCs, the businesses are run with PCs, and even the show itself would not be possible without PCs. The focus is just changing.
Discs. There are no optical discs to be found, for storage or content. Content comes from the cloud. Content is stored in the cloud. This was the last show with discs, and they were practically not there. One might say the same for spinning media and even the satellite tuners were offering solid state storage options to reduce noise, size, and thermals.
Projectors. Another reminder of Moore’s Law as applied to glass is that absence of projectors. While there are quite a few very tiny portable projectors, by and large projectors have been replaced by simpler and brighter on wall displays (soon to be curved and 4K).
Wires. There’s almost no wire at CES anymore. Even the Monster cable booth was mostly about cleaning screens and wireless headsets more than wire. As a person who has no wired communications at home, I can relate.
Home Theater. After a decade or more of “home theater”, the drive to simplicity, the role of streaming content, unified HDMI, and the preference for mobile, has all but eliminated the idea of a complex, multi-component, theater. Today’s theater experience is so much different than emulating a movie theater in a dedicated room at home using a projector and stack of 1000 watt components. People want to watch TV but also interact at the same time and that leads to a very different environment. With the ability to watch anything instantly on a tablet, a home theater has some strong competition for attention and use cases. The lack of a need for a home media library of discs or drives also alters the need for a dedicated room. Live events and film fans still will have their dedicated experiences, but the equipment is drastically simpler.
Remote controls. The world of complex infrared controls at home is being disrupted by mobile devices. Even infrared, while simple and low cost, feels like it will be disrupted by Bluetooth or even Wi-Fi.
For those in technology, CES is really the greatest show on earth. We’re all privileged to be part of the technology industry in such a pivotal time. Next year is going to be even better. I’m counting on it.
—Steven Sinofsky (@stevesi)
Tiny laptop level power supply that works by the magic of physics from Finsix in Menlo Park, CA:
A few hundred thousand dollars worth of add-ons to convert a Canon C500 into a studio broadcast camera:
Sony’s full family of Xperia Android devices is also water-safe. These are all worth a second look I believe.
Example use case for 4K screens with 4 up 1080P security cameras (also on the floor, 4K recording cameras):
Case for iPhone that embeds the ability to take infrared images. Another example of “possible, but still developing the core use case”. This was developed by FLIR, an existing maker of IR cameras:
Bluetooth headphones plus earmuffs in one.
Example use case for curved signage. Wouldn’t this be a more interesting booth with a moving sign:
Innovation and disruption are the hallmarks of the technology world, and hardly a moment passes when we are not thinking, doing, or talking about these topics. While I was speaking with some entrepreneurs recently on the topic, the question kept coming up: “If we’re so aware of disruption, then why do successful products (or companies) keep getting disrupted?”
Good question, and here’s how I think about answering it.
As far back as 1962, Everett Rogers began his groundbreaking work defining the process and diffusion of innovation. Rogers defined the spread of innovation in the stages of knowledge, persuasion, decision, implementation and confirmation.
Those powerful concepts, however, do not fully describe disruptive technologies and products, and the impact on the existing technology base or companies that built it. Disruption is a critical element of the evolution of technology — from the positive and negative aspects of disruption a typical pattern emerges, as new technologies come to market and subsequently take hold.
A central question to disruption is whether it is inevitable or preventable. History would tend toward inevitable, but an engineer’s optimism might describe the disruption that a new technology can bring more as a problem to be solved.
Four Stages of Disruption
For incumbents, the stages of innovation for a technology product that ultimately disrupt follow a pattern that is fairly well known. While that doesn’t grant us the predictive powers to know whether an innovation will ultimately disrupt, we can use a model to understand what design choices to prioritize, and when. In other words, the pattern is likely necessary, but not sufficient to fend off disruption. Value exists in identifying the response and emotions surrounding each stage of the innovation pattern, because, as with disruption itself, the actions/reactions of incumbents and innovators play important roles in how parties progress through innovation. In some ways, the response and emotions to undergoing disruption are analogous to the classic stages of grieving.
Rather than the five stages of grief, we can describe four stages that comprise theinnovation pattern for technology products: Disruption of incumbent; rapid and linear evolution; appealing convergence; and complete reimagination. Any product line or technology can be placed in this sequence at a given time.
The pattern of disruption can be thought of as follows, keeping in mind that at any given time for any given category, different products and companies are likely at different stages relative to some local “end point” of innovation.
Stage One: Disruption of Incumbent
A moment of disruption is where the conversation about disruption often begins, even though determining that moment is entirely hindsight. (For example, when did BlackBerry get disrupted by the iPhone, film by digital imaging or bookstores by Amazon?) A new technology, product or service is available, and it seems to some to be a limited, but different, replacement for some existing, widely used and satisfactory solution. Most everyone is familiar with this stage of innovation. In fact, it could be argued that most are so familiar with this aspect that collectively our industry cries “disruption” far more often than is actually the case.
From a product development perspective, choosing whether a technology is disruptive at a potential moment is key. If you are making a new product, then you’re “betting the business” on a new technology — and doing so will be counterintuitive to many around you. If you have already built a business around a successful existing product, then your “bet the business” choice is whether or not to redirect efforts to a new technology. While difficult to prove, most would generally assert that new technologies that are subsequently disruptive are bet on by new companies first. The very nature of disruption is such that existing enterprises see more downside risk in betting the company than they see upside return in a new technology. This is the innovator’s dilemma.
The incumbent’s reactions to potential technology disruptions are practically cliche. New technologies are inferior. New products do not do all the things existing products do, or are inefficient. New services fail to address existing needs as well as what is already in place. Disruption can seem more expensive because the technologies have not yet scaled, or can seem cheaper because they simply do less. Of course, the new products are usually viewed as minimalist or as toys, and often unrelated to the core business. Additionally, business-model disruption has similar analogues relative to margins, channels, partners, revenue approaches and more.
The primary incumbent reaction during this stage is to essentially ignore the product or technology — not every individual in an organization, but the organization as a whole often enters this state of denial. One of the historical realities of disruption is uncovering the “told you so” evidence, which is always there, because no matter what happens, someone always said it would. The larger the organization, the more individuals probably sent mail or had potential early-stage work that could have avoided disruption, at least in their views (see “Disruption and Woulda, Coulda, Shoulda” and the case of BlackBerry). One of the key roles of a company is to make choices, and choosing change to a more risky course versus defense of the current approaches are the very choices that hamstring an organization.
There are dozens of examples of disruptive technologies and products. And the reactions (or inactions) of incumbents are legendary. One example that illustrates this point would be the introduction of the “PC as a server.” This has all of the hallmarks of disruption. The first customers to begin to use PCs as servers — for application workloads such as file sharing, or early client/server development — ran into incredible challenges relative to the mini/mainframe computing model. While new PCs were far more flexible and less expensive, they lacked the reliability, horsepower and tooling to supplant existing models. Those in the mini/mainframe world could remain comfortable observing the lack of those traits, almost dismissing PC servers as not “real servers,” while they continued on their path further distancing themselves from the capabilities of PC servers, refining their products and businesses for a growing base of customers. PCs as servers were simply toys.
At the same time, PC servers began to evolve and demonstrate richer models for application development (rich client front-ends), lower cost and scalable databases, and better economics for new application development. With the rapidly increasing demand for computing solutions to business problems, this wave of PC servers fit the bill. Soon the number of new applications written in this new way began to dwarf development on “real servers,” and the once-important servers became legacy relative to PC-based servers for those making the bet or shift. PC servers would soon begin to transition from disruption to broad adoption, but first the value proposition needed to be completed.
Stage Two: Rapid Linear Evolution
Once an innovative product or technology begins rapid adoption, the focus becomes “filling out” the product. In this stage, the product creators are still disruptors, innovating along the trajectory they set for themselves, with a strong focus on early-adopter customers, themselves disruptors. The disruptors are following their vision. The incumbents continue along their existing and successful trajectory, unknowingly sealing their fate.
This stage is critically important to understand from a product-development perspective. As a disruptive force, new products bring to the table a new way of looking at things — a counterculture, a revolution, an insurgency. The heroic efforts to bring a product or service to market (and the associated business models) leave a lot of room left to improve, often referred to as “low-hanging fruit.” The path from where one is today to the next six, 12, 18 months is well understood. You draw from the cutting-room floor of ideas that got you to where you are. Moving forward might even mean fixing or redoing some of the earlier decisions made with less information, or out of urgency.
Generally, your business approach follows the initial plan, as well, and has analogous elements of insurgency. Innovation proceeds rapidly in this point. Your focus is on the adopters of your product — your fellow disruptors (disrupting within their context). You are adding features critical to completing the scenario you set out to develop.
To the incumbent leaders, you look like you are digging in your heels for a losing battle. In their view, your vector points in the wrong direction, and you’re throwing good money after bad. This only further reinforces the view of disruptors that they are heading in the right direction. The previous generals are fighting the last war, and the disruptors have opened up a new front. And yet, the traction in the disruptor camp becomes undeniable. The incumbent begins to mount a response. That response is somewhere between dismissive and negative, and focuses on comparing the products by using the existing criteria established by the incumbent. The net effect of this effort is to validate the insurgency.
Stage Three: Appealing Convergence
As the market redefinition proceeds, the category of a new product starts to undergo a subtle redefinition. No longer is it enough to do new things well; the market begins to call for the replacement of the incumbent technology with the new technology. In this stage, the entire market begins to “wake up” to the capabilities of the new product.
As the disruptive product rapidly evolves, the initial vision becomes relatively complete (realizing that nothing is ever finished, but the scenarios overall start to fill in). The treadmill of rapidly evolving features begins to feel somewhat incremental, and relatively known to the team. The business starts to feel saturated. Overall, the early adopters are now a maturing group, and a sense of stability develops.
Looking broadly at the landscape, it is clear that the next battleground is to go after the incumbent customers who have not made the move. In other words, once you’ve conquered the greenfield you created, you check your rearview mirror and look to pick up the broad base of customers who did not see your product as market-ready or scenario-complete. To accomplish this, you look differently at your own product and see what is missing relative to the competition you just left in the dust. You begin to realize that all those things your competitor had that you don’t may not be such bad ideas after all. Maybe those folks you disrupted knew something, and had some insights that your market category could benefit from putting to work.
In looking at many disruptive technologies and disruptors, the pattern of looking back to move forward is typical. One can almost think of this as a natural maturing; you promise never to do some of the things your parents did, until one day you find yourself thinking, “Oh my, I’ve become my parents.” The reason that products are destined to converge along these lines is simply practical engineering. Even when technologies are disrupted, the older technologies evolved for a reason, and those reasons are often still valid. The disruptors have the advantage of looking at those problems and solving them in their newly defined context, which can often lead to improved solutions (easier to deploy, cheaper, etc.) At the same time, there is also a risk of second-system syndrome that must be carefully monitored. It is not uncommon for the renegade disruptors, fresh off the success they have been seeing, to come to believe in broader theories of unification or architecture and simply try to get too much done, or to lose the elegance of the newly defined solution.
Stage Four: Complete Reimagination
The last stage of technology disruption is when a category or technology is reimagined from the ground up. While one can consider this just another disruption, it is a unique stage in this taxonomy because of the responses from both the legacy incumbent and the disruptor.
Reimagining a technology or product is a return to first principles. It is about looking at the underlying assumptions and essentially rethinking all of them at once. What does it mean to capture an image,provide transportation, share computation, search the Web, and more? The reimagined technology often has little resemblance to the legacy, and often has the appearance of even making the previous disruptive technology appear to be legacy. The melding of old and new into a completely different solution often creates whole new categories of products and services, built upon a base of technology that appears completely different.
To those who have been through the first disruption, their knowledge or reference frame seems dated. There is also a feeling of being unable to keep up. The words are known, but the specifics seem like rocket science. Where there was comfort in the convergence of ideas, the newly reimagined world seems like a whole new generation, and so much more than a competitor.
In software, one way to think of this is generational. The disruptors studied the incumbents in university, and then went on to use that knowledge to build a better mousetrap. Those in university while the new mousetrap was being built benefited from learning from both a legacy and new perspective, thus seeing again how to disrupt. It is often this fortuitous timing that defines generations in technologies.
Reimagining is important because the breakthroughs so clearly subsume all that came before. What characterizes a reimagination most is that it renders the criteria used to evaluate the previous products irrelevant. Often there are orders of magnitude difference in cost, performance, reliability, service and features. Things are just wildly better. That’s why some have referred to this as the innovator’s curse. There’s no time to bask in the glory of the previous success, as there’s a disruptor following right up on your heels.
A recent example is cloud computing. Cloud computing is a reimagination ofboth the mini/mainframe and PC-server models. By some accounts, it is a hybrid of those two, taking the commodity hardware of the PC world and the thin client/data center view of the mainframe world. One would really have to squint in order to claim it is just that, however, as the fundamental innovation in cloud computing delivers entirely new scale, reliability and flexibility, at a cost that upends both of those models. Literally every assumption of the mainframe and client/server computing was revisited, intentionally or not, in building modern cloud systems.
For the previous incumbent, it is too late. There’s no way to sprinkle some reimagination on your product. The logical path, and the one most frequently taken, is to “mine the installed base,” and work hard to keep those customers happy and minimize the mass defections from your product. The question then becomes one of building an entirely new product that meets these new criteria, but from within the existing enterprise. The number of times this has been successfully accomplished is diminishingly small, but there will always be exceptions to the rule.
For the previous disruptor and new leader, there is a decision point that is almost unexpected. One might consider the drastic — simply learn from what you previously did, and essentially abandon your work and start over using what you learned. Or you could be more incremental, and get straight to the convergence stage with the latest technologies. It feels like the ground is moving beneath you. Can you converge rapidly, perhaps revisiting more assumptions, and show more flexibility to abandon some things while doing new things? Will your product architecture and technologies sustain this type of rethinking? Your customer base is relatively new, and was just feeling pretty good about winning, so the pressure to keep winning will be high. Will you do more than try to recast your work in this new light?
The relentless march of technology change comes faster than you think.
So What Can You Do?
Some sincerely believe that products, and thus companies, disrupt and then are doomed to be disrupted. Like a Starship captain when the shields are down, you simply tell all hands to brace themselves, and then see what’s left after the attack. Business and product development, however, are social sciences. There are no laws of nature, and nothing is certain to happen. There are patterns, which can be helpful signposts, or can blind you to potential actions. This is what makes the technology industry, and the changes technology bring to other industries, so exciting and interesting.
The following table summarizes the stages of disruption and the typical actions and reactions at each stage:
|Disruption of Incumbent||Introduces new product with a distinct point of view, knowing it does not solve all the needs of the entire existing market, but advances the state of the art in technology and/or business.||New product or service is not relevant to existing customers or market, a.k.a. “deny.”|
|Rapid linear evolution||Proceeds to rapidly add features/capabilities, filling out the value proposition after initial traction with select early adopters.||Begins to compare full-featured product to new product and show deficiencies, a.k.a. “validate.”|
|Appealing Convergence||Sees opportunity to acquire broader customer base by appealing to slow movers. Sees limitations of own new product and learns from what was done in the past, reflected in a new way. Potential risk is being leapfrogged by even newer technologies and business models as focus turns to “installed base” of incumbent.||Considers cramming some element of disruptive features to existing product line to sufficiently demonstrate attention to future trends while minimizing interruption of existing customers, a.k.a. “compete.” Potential risk is failing to see the true value or capabilities of disruptive products relative to the limitations of existing products.|
|Complete Reimagining||Approaches a decision point because new entrants to the market can benefit from all your product has demonstrated, without embracing the legacy customers as done previously. Embrace legacy market more, or keep pushing forward?||Arguably too late to respond, and begins to define the new product as part of a new market, and existing product part of a larger, existing market, a.k.a. “retreat.”|
Considering these stages and reactions, there are really two key decision points to be tuned-in to:
When you’re the incumbent, your key decision is to choose carefully what you view as disruptive or not. It is to the benefit of every competitor to claim they are disrupting your products and business. Creating this sort of chaos is something that causes untold consternation in a large organization. Unfortunately, there are no magic answers for the incumbent.
The business team needs to develop a keen understanding of the dynamics of competitive offerings, and know when a new model can offer more to customers and partners in a different way. More importantly, it must avoid an excess attachment to today’s measures of success.
The technology and product team needs to maintain a clinical detachment from the existing body of work to evaluate if something new is better, while also avoiding the more common technology trap of being attracted to the next shiny object.
When you’re the disruptor, your key decision point is really when and if to embrace convergence. Once you make the choices — in terms of business model or product offering — to embrace the point of view of the incumbent, you stand to gain from the bridge to the existing base of customers.
Alternatively, you create the potential to lose big to the next disruptor who takes the best of what you offer and leapfrogs the convergence stage with a truly reimagined product. By bridging to the legacy, you also run the risk of focusing your business and product plans on the customers least likely to keep pushing you forward, or those least likely to be aggressive and growing organizations. You run the risk of looking backward more than forward.
For everyone, timing is everything. We often look at disruption in hindsight, and choose disruptive moments based on product availability (or lack thereof). In practice, products require time to conceive, iterate and execute, and different companies will work on these at different paces. Apple famously talked about the 10-year project that was the iPhone, with many gaps, and while the iPad appears a quick successor, it, too, was part of that odyssey. Sometimes a new product appears to be a response to a new entry, but in reality it was under development for perhaps the same amount of time as another entry.
There are many examples of this path to disruption in technology businesses. While many seem “classic” today, the players at the time more often than not exhibited the actions and reactions described here.
As a social science, business does not lend itself to provable operational rules. As appealing as disruption theory might be, the context and actions of many parties create unique circumstances each and every time. There is no guarantee that new technologies and products will disrupt incumbents, just as there is no certainty that existing companies must be disrupted. Instead, product leaders look to patterns, and model their choices in an effort to create a new path.
Stages of Disruption In Practice
Digital imaging. Mobile imaging reimagined a category that disrupted film (always available, low-quality versus film), while converging on the historic form factors and usage of film cameras. In parallel, there is a wave of reimagination of digital imaging taking place that fundamentally changes imaging using light field technology, setting the stage for a potential leapfrog scenario.
- Retail purchasing. Web retailers disrupted physical retailers with selection, convenience, community, etc., ultimatelyconverging on many elements of traditional retailers (physical retail presence, logistics, house brands).
- Travel booking. Online travel booking is disrupting travel agents, then converging on historic models of aggregation and package deals.
- Portable music. From the Sony Walkman as a disruptor to the iPod and MP3 players, to mobile phones subsuming this functionality, and now to streaming playback, portable music has seen the disruptors get disrupted and incumbents seemingly stopped in their tracks over several generations. The change in scenarios enabled by changing technology infrastructure (increased storage, increased bandwidth, mobile bandwidth and more) have made this a very dynamic space.
- Urban transport. Ride sharing, car sharing, and more disruptive traditional ownership of vehicles or taxi services are in the process of converging models (such as Uber adding UberX.
- Productivity. Tools such as Quip, Box, Haiku Deck, Lucidchart, and more are being pulled by customers beyond early adopters to be compatible with existing tools and usage patterns. In practice, these tools are currently iterating very rapidly along their self-defined disruptive path. Some might suggest that previous disruptors in the space (OpenOffice, Zoho, perhaps even Google Docs) chose to converge with the existing market too soon, as a strategic misstep.
- Movie viewing. Netflix and others, as part of cord-cutting, with disruptive, low-priced, all-you-can-consume on-demand plans and producing their own content. Previous disruptors such as HBO are working to provide streaming and similar services, while constrained by existing business models and relationships.
- Messaging/communications apps. SMS, which many consider disruptive to 2000-era IM, is being challenged by much richer interactions that disrupt the business models of carrier SMS and feature sets afforded by SMS.
- Network infrastructure. Software-defined networking and cloud computing are reimagining the category of networking infrastructure, with incumbent players attempting to benefit from these shifts in the needs of customers. Incumbents at different levels are looking to adopt the model, while some providers see it as fundamentally questioning their assumptions.
Rather than predict anything that will suddenly appear at the end of 2014, this post offers some trends that are likely to double by some measure this next year.
This will turn out to be an exponential year in many technologies and what seems far-fetched could very easily be trends that are doubling in relatively short periods of time. We humans generally have a tough time modeling things doubling (why so many companies and products did not figure out how to embrace Moore’s law or the rise of mobile).
To fully embrace exponential change means looking at the assumptions in product development and considering how optimizations for the near term might prove to be futile when facing significant change. Within each trend, design or product choices are offered that might be worth considering in light of the trend.
- Low-cost/high-function devices. The seemingly endless march of the exponential Moore’s law will continue but include more than compute. Devices will put transistors to work for sensors, rich graphics, and discrete processors. These devices will continue to drop precipitously in price to what seem today like ridiculous levels such as we’ve seen at discount super stores this holiday shopping season in the US. If automobiles are any indication, we should not assume low price is equivalent to low quality for the long-term, as manufacturing becomes more capable of delivering quality at low price. The desire to aspire an even higher level of quality will remain for many and continue to support many price points and volumes. At the same time, the usage patterns across price points will vary dramatically and we will continue to see exponential growth in-depth usage as we have this holiday season with high-value devices. This makes for a fairly dramatic split and leaves a lot open to interpretation when it comes to market share in devices. Design: First, it is worth considering target customers with more granularity when looking at share, as the pure number of devices might not determine how much your service will get used, at least in the near term. Second, don’t expect differences in capability across price points to last very long as the pace of pulling capabilities from higher price points to lower will be relentless.
- Cloud productivity. Cloud (SaaS) productivity tools will routinely see exponential growth in active users. Tools that enable continuous productivity will rapidly expand beyond tech early adopters as viral effects of collaboration kick in. Products such as Asana, Quip, Paper, Mixpanel, Lucidchart, Haikudeck or others will see viral expansion kick-in. Established tools such as Evernote, Box, Dropbox, WhatsApp, and more with high active usage will see major increases in cross-organization work as they grow to become essential tools for whole organizations. Design: Don’t assume traditional productivity tools and assume new employees, vendors, and recent grads will default to cloud-first productivity.
- Cloud first becomes cloud-only. Enterprise software in 2013 was a dialog about on-premises or cloud. In 2014, the call for on-premises will rapidly shift to a footnote in the evolution of cloud. The capabilities of cloud-based services will have grown to such a degree, particularly in terms of collaboration and sharing, that they will dwarf anything that can be done within the confines of a single enterprise. Enterprises will look at the exponential growth in scale of multi-tenant systems and see these as assets that cannot be duplicated by even the largest enterprises. Design: Don’t distract with attempting to architect or committing to on-premises.
- WWAN communication tools. WWAN/4G messaging will come to dominate in usage by direct or integrated tools (WhatsApp, WeChat, iMessage, and more) relative to email and SMS. Email will increasingly be viewed as “fax” and SMS will be used for “official” communications and “form letters” as person to person begins to use much richer and more expressive (fun) tools. This shift contributes to the ability to switch to data-only larger screen devices. Design: Skip email notifications, rely on SMS only when critical (security and verification), and assume heterogeneity for messaging choices. Expect to see more tools building in messaging capabilities with context scoped to the app.
- Cross-platform challenge. This is the year that cross-platform development for the major modern platforms will become increasingly challenging and products will need to be developed with this in mind. It will become increasingly unwieldy to develop for both iOS and Android and natively integrate effectively and competitively with the platform. Visual changes and integration functionality will be such that “cross-platform layers” might appear to be a good choice today only to prove to be short-lived and obstacles to rapid and competitive development. New apps that are cross-platform “today” will see increasing gaps between releases on each platform and will see functionality not quite “right” for platforms. Ultimately, developers will need to pick their lead platforms or have substantial code bases across platforms and face the challenge of keeping functionality in sync. Design: Avoid attempting to abstract platforms as these are moving targets, and assume dual-platform is nearly 2X the work of a single platform for any amount of user experience and platform integration.
- Small screen/big screen divergence. With increasing use of cloud productivity, more products will arrive that are designed exclusively for larger screen devices. Platform creators will increasingly face challenges of maintaining the identical user experience for “phones” and for phablets and larger. Larger screen tablets will be more able to work with keyboard accessories that will further drive a desire for apps tuned along these lines along with changes to underlying platforms to more fully leverage more screen real estate. The converse will be that scenarios around larger screen tablets will shift away from apps designed for small screen phones–thus resetting the way apps are counted and valued. Design: Productivity scenarios should be considering committing to large screen design and leave room for potential of keyboard or other input peripherals.
- Urban living is digital living. With demographic shifts in urban living and new influx of urban residents, we will see a rapid rise in digital-only lives. Mobile platforms will be part of nearly every purchase or transaction. Anything requiring reservations, tickets, physical resources, delivery, or scheduling will only win the hearts and minds of the new urban if available via mobile. While today it seems inconvenient if one needs to resort to “analog” to use a service, 2014 is a year in which every service has a choice and those that don’t exist in a mobile world won’t be picked. Design: Consumer products and services will only exist if they can be acquired via mobile.
- Sharing becomes normal. With the resources available for sharing exceeding those available in traditional ways, 2014 will be the year in which sharing becomes normal and preferred for assets that are infrequently used and/or expensive. Government and corporate structures will be re-evaluated relative to sharing from autos to office space and more. Budget pressures, rapid increase in software capabilities, and environmental impact all contribute to this change. Design: Can your business share resources? What are you using that could be shared? Is the asset you sell or rent something that runs the risk of aggregation and sharing by a new entry?
- Phablets are normal. Today’s phablets seem like a tweener or oddity to some–between a large phone and a small tablet. In practice the desire to have one device serve as both your legacy phone (voice and SMS) as well as your main “goto” device for productivity and communication will become increasingly important. The reduction in the need for legacy communication will fuel the need to pivot closer to a larger screen all the time. Improvements in voice input and collaboration tools will make this scenario even more practical. In the short-term, the ability to pair a larger screen tablet with your phone-sized device for voice or SMS may arise in an effort to always use one device, and similarly smaller tablets will be able to assume phone functionality. Design: Don’t ignore the potential of this screen size combined with full connectivity as the single device, particularly in mobile first markets where this form has early traction.
- Storage quotas go away. While for most any uses today this is true in practice, 2014 will be a year in which any individual will see alternatives for unlimited cloud storage. Email, files, photos, applications, mobile backup and more will be embedded in the price of devices or services with additional capabilities beyond gigabytes. Design: Design for disk space usage in the cloud as you do on a mobile client, which is to say worry much more about battery life and user experience than saving a megabyte.
Amara’s Law states “we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run”. We will see 2014 not as one year of progress, but as the culmination of the past 15 years of development of the consumer internet as “it all comes together” with incredibly rapid adoption of products and technologies that at once become more affordable, more ubiquitous, and more necessary for our work and personal lives.
It looks like 2014 is shaping up to be the long-term of 2000 that we might have underestimated.
Stay tuned and Happy New Year!
I love visiting Tokyo and have been lucky enough to visit dozens of times over many years. The consumer electronics industry has certainly had ups and downs recently, but a constant has been the leading edge consumer and business adoption of new technologies. From PCs in the workplace to broadband at home and smartphones (a subject of many humorous team meetings back pre-bubble when I clearly didn’t get it and was content with the magic of my BB 850!) Japan has always had a leading adoption curve even when not necessarily producing the products used globally.
This visit was about visiting the University of Tokyo and meeting with some entrepreneurs. That, however, doesn’t stop me from spending time observing what CE is being used in the workplace, on the subway, and most importantly for sale in the big stores such as Yodobashi, Bic, and Labi and of course the traditional stalls at Akihabara. The rapid adoption, market size, and proximity to Korea and China often mean many of the products seen are not yet widely available in the US/Europe or are just making their way over. There’s a good chance what is emphasized in the (really) big retail space is often a leading indicator for what will show up at CES in January.
If you’re not familiar with Yodobashi, here’s the flagship store in Akihabara – over 250,000 sq ft and visited by 10′s of millions of people every year. I was once fortunate enough to visit the underground operations center, and as a kid who grew up in Orlando it sure feels a lot like the secret underground tunnels of the Magic Kingdom!
With that in mind here are 10 observations (all on a single page). This is not statistical in any way, just what caught my eye.
- Ishikawa Oku lab. The main focus of the trip was to visit University of Tokyo. Included in that was a wonderful visit with Professor Ishikawa-san and his lab which conducts research on exploring parallel, high-speed, and real-time operations for sensory information processing. What is so amazing about this work is that it has been going on for 20 years starting with very small and very slow digital sensors and now with Moore’s law applied to image capture along with parallel processing amazing things are possible such as can be seen in some of these Youtube videos (with > 5 million views), see http://www.youtube.com/ishikawalab. More about the lab http://www.k2.t.u-tokyo.ac.jp/index-e.html.
- 4K Displays. Upon stepping off the escalator on the video floor, one is confronted with massive numbers of massive 4K displays. Every manufacturer has displays and touts 4K resolution along with their requisite tricks at upscaling. The prices are still relatively high but the selection is much broader than readily seen in the US. Last year 4K was new at CES and it seems reasonable to suspect that the show floor will be all 4K. As a footnote relative to last year, 3D was downplayed significantly. In addition, there are numerous 4K cameras on sale now, more so than the US.
- Digital still. The Fuji X and Leica rangefinder digital cameras are getting a lot of floorspace and it was not uncommon to see tourists snapping photos (for example in Meiji Garden). The point and shoot displays feature far fewer models with an emphasis on attributes that differentiate them from phones such as waterproof or ruggedized. There’s an element of nostalgia, in Japan in particular, driving a renewed popularity in this form factor.
- Nikon Df. This is a “new” DSLR with the same sensor as the D-800/D4 that is packaged in a retro form factor. The Nikon Df is definitely only for collectors but there was a lot of excitement for the availability on November 21. It further emphasized the nostalgia elements of photography as the form factor has so dramatically shifted to mobile phones.
- Apple presence in store. The Apple presence in the main stores was almost overwhelming. Much of the first floor and the strategic main entry of Yodobashi were occupied by the Apple store-within-a-store. There were large crowds and as you often see with fans of products, they are shopping the very products they own and are holding in their hands. There has always been a fairly consistent appreciation of the Apple design aesthetic and overall quality of hardware but the widespread usage did not seem to follow. To be balanced, one would have to take note of the substantial presence of the Nexus 5 in the stores, which was substantially and well-visited.
- PCs. The size of the PC display area, relative to mobile and iOS accessories, definitely increased over the past 7 months since I last visited. There were quite a large number of All-In-One designs (which have always been popular in Japan, yet somehow could never quite leap across the Pacific until Windows 8). There were a lot of very new Ultrabooks running Haswell chips from all the major vendors in the US, Japan, and China. Surface was prominently displayed.
- iPhone popularity. There was a ubiquity of the iPhone that is new. Android had gained a very strong foothold over the national brands that came with the transition to nationwide LTE. Last year there was a large Android footprint through Samsung handsets that was fairly visible on display and in use. While the Android footprint is clearly there, the very fast rise of iPhone, particularly the easily spotted iPhone 5s was impressive. The vast expanse of iPhone accessories for sale nearly everywhere supports the opportunity. A driver for this is that the leading carrier (DoCoMo) is now an iPhone supplier. Returning from town, I saw this article speaking to the rise of iOS in Japan recently, iPhone 5S/C made up 76% of new smartphone sales in Japan this October.
- Samsung Galaxy J. Aside from the Nexus 5, the Android phone being pushed quite a bit was the Samsung Galaxy J. This is a model only in Asia right now. It was quite nice. It sports an ID more iPhone-like (squared edges), available in 5c-like colors, along with the latest QC processor, 5″ HD display, and so on. It is still not running Kitkat of course. For me in the store, it felt better than a Galaxy S. Given the intricacies of the US market, I don’t know if we’ll see this one any time soon. The Galaxy Note can be seen “in the wild” quite often and there seems to be quite a lot of interest based on what devices on display people would stop and interact with.
- Tablets. Tablets were omnipresent. They were signage in stores, menus in restaurants, in use on the subway, and in use at every place where people were sitting down and eating/drinking/talking. While in the US we are used to asking “where are all the Android tablets”, I saw a lot of 7″ Android tablets in use in all of those places. One wouldn’t expect the low-priced import models to be visible but there are many Japan OEMs selling Android tablets that could be spotted. I also saw quite a few iPad Minis in use, particularly among students on the trains.
- Digital video. As with compact digital cameras, there was a rather extreme reduction in the number of dedicated video recorders. That said, GoPro cameras had a lot of retail space and accessories were well placed. For example, there were GoPros connected to all sorts of gear/showing off all sorts of accessories at Tokyu Hand (the world’s most amazing store, imho). Professional HD and UHD cameras are on display in stores which is cool to see, for example Red and Arri. One of the neatest uses of video which is available stateside but I had not seen is the Sony DEV-50 binoculars/camera. It is pricey (USD$2000) but also pretty cool if you’ve got the need for it. They have reasonable sensors, support 3D, and more. The only challenge is stability which make sense given the equivalent focal length, but there is image stabilization which helps quite a bit in most circumstances.
There were many other exciting and interesting products one could see in this most wired and gadget friendly city. One always is on the lookout for that unique gift this holiday season, so I found my stocking-stuffer. Below you can see a very effective EMF shielding baseball hat (note, only 90% effective). As a backup stocking-stuffer, all gloves purchased in Japan appear to be designed with resistive touch screens in mind :-)
PS: Here’s me with some super fun students in a class on Entrepreneurship and Innovation at the University of Tokyo.
Much has been written recently about performance ratings and management at some large and successful companies. Amazon has surfaced as a company implementing OLRs, organization and leadership reviews, which target the least effective 10% of an organization for appropriate action. Yahoo recently implemented QPRs, quarterly performance reviews, which rates people as “misses” or “occasionally misses” among other ratings. And just so we don’t think this is something unique to tech, every year about this time Wall St firms begin the annual bonus process which is filled with any number of legendary dysfunctions given the massive sums of money in play. Even the Air Force has a legendary process for feedback and appraisal.
This essay looks at the challenges of performance review in a large organization. The primary purpose is to help share the realities that designing and implementing a system for such an incredibly sensitive topic is a monumental challenge when viewed in isolation. If you overlay the environment of an organization (stock price, public perception, revenue or profit, local competition for talent, etc.) then any system at all can seem anywhere from tyrannical to fair to kick-ass for some period of time, and then swing the other way when the context changes. For as much as we think of performance management as numeric and thus perfectly quantifiable, it is as much a product of context and social science as the products we design and develop. We want quantitative certainty and simplicity, but context is crucial and fluid, and qualitative. We desire fair, which is a relative term, but insist on the truth, which is absolute.
While there is an endless quest for simplicity, much as with airline tickets, car prices, or tax codes it is naive to believe that simplicity can truly be achieved or maintained over time. The challenge doesn’t change the universally shared goal of simplicity (believe me HR people do not like complex systems any more than everyone else does) but as a practical matter such purity is unattainable. Therefore comparing any system to some ideal (“flat tax”, “fixed pricing”) only serves to widen the gap between desire and implementation and thus increases the frustration and even fear of a system.
If this topic were simple there would not be over 25,000 books listed on Amazon’s US book site for the query “performance review”. Worse, the top selling books are about how to write your review, game the system, impress your boss, or tell employees they are doing well when they really aren’t doing well. You get pretty far down the list before you get to books that actually try to define a system and even then those books are filled with caveats. My own view is that the best book on the topic is Measuring and Managing Performance in Organizations. It is not about the perfect review system but about the traps and pitfalls of just measuring stuff in general. I love this book because it is a reminder of everything you know about measuring, from “measure twice, cut once” to “measure what you can change” to “if you measure something it goes up” and so on.
Notes. I am not an HR professional and don’t get wrapped up in the nuances of terminology between “performance review” or “performance management” or “performance rating”. I recognize the differences and respect them but will tend to intermix the terms for the purposes of discussion. I also recognize that for the most part, people executing such a system generally don’t see the subtle distinctions in these words as much as they might mean something within the academy. I am also not a lawyer, so what I say here may or may not be legally permitted in your place of doing business (geography, company size, sector). Finally, this post is not about any specific company practice past or present and any similarity is unintended coincidence.
This post will say some things that are likely controversial or appear plain wrong to some. I’ll be following this on twitter to see what transpires, @stevesi.
5 Essential Realities
There are several essential realities to performance reviews:
- Performance systems conflate performance and compensation with organizational budgets. No matter how you look at it, one person cannot be evaluated and paid in isolation of budgets. The company as a whole has a budget for how much to pay people (salary, bonus, stock, etc.) No matter what an individual’s compensation is part of a system that ultimately has a budget. The vast majority of mechanical or quantitative effort in the system is not about one person’s performance but about determining how to pay everyone within the budget. While it is desirable to distinguish between professional development and compensation, that will almost certainly get lost once a person sees their compensation or once a manager has to assign a rating. Any suggestion as to how to be more fair, allow for more flexibility, provide more absolute ratings, or otherwise separate performance from compensation must still come up with a way to stick to a budget. The presence of a budget drives the existence of a system. There is always a budget and don’t be fooled by “found money” as that’s just a budget trick.
- In a group of any size there is a distribution of performance. At some point a group of people working towards similar goals will exhibit a distribution of performance. From our earliest days in school we see this with schoolwork. In the workplace there are infinite variables that influence the performance of any individual but the variability exists. In an ideal system one could isolate all the variables from some innate notion of “pure contribution” or “pure skill” in order to evaluate someone. But that can’t be done so the distribution one sees essentially lumps together many performance related variables.
- In a system where you have m labels for performance, people who get all but the most rewarding one believe they are “so close” to the higher one. In school, teachers have letter grades or numeric ranges that break up test scores into “buckets”. In the workplace, performance systems generally implement some notion of grades or ratings and assign distributions to each of those. Much like a forced curve in a physics test, the system says that only a certain percentage of a population can get the highest performance rating and likewise a certain percentage of the team gets the lowest rating. The result is that most everyone in the organization believes they are extremely close to the next rating much like looking at a test and thinking if you could just get that one extra point you’d get the next letter grade. Because of human nature, any such system almost certain follows the corollary that managers are likely to imply or one being managed likely to hear evidence of just how close a call their review score was. There is a corollary: “everyone believes they are above average“.
- Among any set of groups, almost all the groups think their group is delivering more and other groups are delivering less. In a company with many groups, managers generally believe their group as a whole is performing better by relevant measures and thus should not be held to the same distribution or should have a larger budget. Groups tend to believe their work is harder, more strategic, or just more valuable while underestimating those contributions from other groups. Once groups realize that there is a fixed budget, some strive to solve the overall challenges by allowing for higher budgets on some teams. In this way you could either use a different distribution of people (more at the top) or just elevate the compensation for people within a group. Any suggestion to do this would need to also provide guidance as to how groups as a whole are to be measured relative to each other (which sounds an awful lot like how individuals would be measured relative to each other).
- Measurement is not an absolute but is relative. To measure performance it must be measured relative to something. Sales is the “easiest” since if you have a sales quota then your compensation is just a measure of how much you beat the quota. Such simplicity masks the knife fight that is quota settings and the process by which a comp sheet is built out, but it is still a relative measure. Most product have squishier goals such as “finish the product” or “market the product”. The larger the company the more these goals make sense but the less any individual’s day to day actions are directly related (“If I fix this bug will the sale really close?”). Thus in a large company, goal setting becomes increasingly futile as it starts to look like “get my work done” as the interconnection between other people and their work is impossibly hard to codify. Much of the writing about performance reviews focuses on goal setting and the skill in writing goals you can always brag about, unfortunately. All of this has taken a rather dramatic turn with the focus on agility where it is almost the antithesis of well-run to state months in advance what success looks like. As a result, measuring performance relative to peers “doing their work” is far more reliable, but has the downside that the big goals all fall to the top level managers. That’s why for the most part this entire topic is a big company thing—in a startup or a small company, actions translate into sales, marketing, products, and customers all very directly.
10 Real World Attributes
Once you take these realities you realize there will in fact be some sort of system. The goal of the system is to figure out how much to pay people. For all the words about career management, feedback, and so on that is not what anyone really focuses on at the moment they check their “score”. It certainly isn’t what is going on around the table of managers trying to figure out how to fit their team within the rules of the system.
Those that look to the once a year performance rating as the place for either learning how they are doing or for sharing feedback with an employee are simply doing it wrong—there simply shouldn’t be surprises during the process. If there are surprises then that’s a mistake no system can fix. There are no substitutes for concrete, actionable, and ongoing feedback about performance. If you’re not getting that then you need to ask.
At the same time, you can’t expect to have a daily/weekly rating for how you are doing. That’s because your performance is relative to something and that something isn’t determined on a daily basis. Finding that happy place is a challenge for individuals and managers, with the burden to avoid surprises falling to both equally. As much as one expects a manager to communicate well individuals must listen well.
Putting a system in place for allocating compensation is enormously challenging. There’s simply too much at stake for the company, the team, managers, and individuals. Ironically, because so much is at stake that materially impacts the lives of people, it is not unusual for the routine implementation of the process to take months of a given year and for it to occupy far more brain cycles than the actual externally facing work of the organization. Ironically, the more you try to make the process something HR worries about the even more disconnected it becomes from work and the more stress. As a result, performance management occupies a disproportionate amount of time and energy in large organizations.
Because of this, everyone in a company has enough experience to be critical of the system and has ideas how to improve it. Much like when a company does TV advertising and everyone can offer suggestions—simply because we all watch TV and buy stuff—when it comes to performance reviews since we all do work and get reviewed we all can offer insights and perspectives on the system. Designing a system from scratch is rather different than being critical of anecdotes of an existing system.
Given that so much is at stake and everyone has ideas how to improve the system, the actual implementation is enormously complex. While one can attempt to codify a set of rules, one cannot codify the way humans will implement the rules. One can keep iterating, adding more and more rules, more checks and balances, but eventually a process that already takes too much time becomes a crushing burden. Even after all that, statistically a lot of people are not going to be happy.
Therefore the best bet with any system is to define the variables and recognize that choices are being made and that people will be working within a system that by definition is not perfect. One can view this as gaming the system, if one believes the outcome is not tilted towards goodness. Alternatively, one can view this as doing the right thing, if one believes the outcome is tilted in the direction of goodness.
My own experience, is that there are so many complexities it is pointless to attempt to fully codify a system. Rather everyone just goes in with open eyes and a realistic view of the difficulty of the challenge and iterates through the variables throughout the entire dialog. Fixating on any one to the exclusion of others is when ultimately the system breaks down.
The following are ten of the most common attributes that must be considered and balanced when developing a performance review system:
- Determining team size. There is critical mass of “like” employees (job function, seniority, familiarity, responsibility) required to make any system even possible. If you have less than about 100 people no system will really work. At the same time, at about 100 people you are absolutely assured of having a sample size large enough to see a diversity in performance. There is going to be a constant tension between employees who believe the only fair way of evaluation is to have intimate knowledge of their work and a system that needs a lot of data points. In practice, somewhere between 1 and 5 people are likely to have intimate understanding of the work of an individual, but said another way any given manager is likely to have intimate knowledge of between 5 and about 50 people. At some point the system requires every level of management to honestly assess people based on a dialog of imperfect information. Team size also matters because small “rounding” efforts become enormous. Imagine something where you need to find 10% of the population and you have a team of 15 people to do that with. You obviously pick either 1 or 2 people (1 if the 10% is “bad”, 2 if it is “good”). Then imagine this rolls up to 15,000 people. Rather than 1500, you have either 1000 or 2000 people in that 10%. That’s either very depressing or very expensive relative to the budget. Best practice: Implement a system in groups of about 100 in seniority and role.
- Conflating seniority, job function, and projects does not create a peer group. Attempting to define relative contribution of a college new hire and a 10 year industry vet, or a designer and a QA engineer, manager or not, or a front-end v. ops tools are all impossibly difficult. The dysfunction is one where invariably as the process moves up the management chain there will be a bias that builds—the most visible people, the highest paid people or jobs, scarcest talent, the work that is understood and so on will become the things that get airtime in dialogs. There’s nothing inherently evil about this but it can get very tricky very quickly if those dialogs lead to higher ratings/compensation for these dimensions. This can get challenging if these groups are not sized as above and so you’ll find it a necessary balancing act. Best practice: Define peer groups based on seniority and job function within project teams as best you can.
- Measuring against goals. It is entirely possible to base a system of evaluation and compensation on pre-determined goals. Doing so will guarantee two things. First, however much time you think you save on the review process you will spend up front on an elaborate process of goal-setting. Second, in any effort of any complexity there is no way to have goals that are self-contained and so failure to meet goals becomes an exercise in documenting what went wrong. Once everyone realizes their compensation depends on others, the whole work process becomes clouded by constant discussion about accountability, expectation setting, and other efforts not directly related to actually working things out. And worse, management will always have the out of saying “you had the goal so you should have worked it out”. There’s nothing more challenging in the process of evaluation than actually setting goals and all of this is compounded enormously when the endeavor is a creative one where agility, pivots, and learning are part of the overall process. Best practice: let individuals and their manager arrive at goals that foster a sense of mastery of skills and success of the project, while focusing evaluation on the relative (and qualitative) contribution to the broader mission.
- Understanding cross-organization performance. Performance measurement is always relative, but determining performance across multiple organizations in a relative sense requires apples to oranges comparisons, even within similar job functions (i.e. engineering). If one team is winding down a release and another starting, or if one team is on an established business and another on a new business, or if one team has no competitors and another is in an intense battle, or if one team has a lot of sales support and another doesn’t, and so on are all situations which make it non-obvious how to “compare” multiple teams, yet this is what must happen at some level. Compounding this situation is that at some point in evaluation the basis for relative comparison might dramatically change—for example, at one level of management the accomplishment of multiple teams might be looked at through a lens that can be far removed from what members of those teams might be able to impact in their daily work. Best practice: do not pit organizations against each other by competing for rewards and foster cross-group collaboration via planning and execution of shared bets.
- Maintaining a system that both rates and rewards. Systems often have some sort of score or a grade and they also have compensation. Some think this is essential. Some think this is redundant. Some care deeply about one, but only when they are either very happy or very unhappy with the other. A system can be developed where these are perfectly correlated in which case one can claim they are redundant. A system where there is a loose correlation might as well have no correlation because both individuals and managers involved are hearing what they want to hear or saying one thing and doing another. At the same time, we’re all conditioned for a “score” and somehow a bonus of 9.67% doesn’t feel like a score because you don’t know what this means relative (so even though people want to be rated absolutely it doesn’t take long before they want to know where that stands relatively). Best practice: A clear rating that lets individuals know where they stand relative to their peer group along with compensation derived from that with the ability of a manager with the most intimate knowledge of the work to adjust compensation within some rating-defined range.
- Writing a performance appraisal is not making a case. Almost all of the books on Amazon about performance reviews focus on the art of writing reviews. Your performance review is not a trial and one can’t make or break the past year/month/quarter by an exercise in strong or creative writing. This holds for individuals hoping to make their performance shine and importantly for managers hoping to make up for their lack of feedback/action. The worst moments in a performance process are when an employee dissects a managers comments and attempts to refute them or when a manager pulls up a bunch of new examples of things that were not talked about when they were happening. Best practice: Lower the stakes for the document itself and make it clear that it is not the decision-making tool for the process.
- Ranking and calibrating are different. Much has been said about the notion of “stack rank” which often is used as a catch phrase for a process that assigns each member of a group a “one through n” score. This is always always a terrible process. There is simply no way to have the level of accuracy implied by such a system. What would one say to someone trying to explain the difference between being number 63 and number 64 on a 100 person team? The practice of calibrating is one of relative performance between members of peer groups as described above. The size and number of these groups is fixed and when done with adequate population size can with near certainty avoid endless discussion over boundary cases. Best practice: Define performance groups where members of a team fall but do not attempt to rank with more granularity or “proximity” to other groups.
- Encouraging excellent teams. Most managers believe their teams are excellent teams, and uniquely so. Strong performers have been hired to the team. Weak performers are naturally managed out if they somehow made it on to the team. Results show this. It becomes increasingly difficult to implement a performance review system because organizations become increasingly strong and effective. This is how it should be. At the same time this cannot possibly be a permanent state (even the sports teams get new players that don’t pan out over the course of a season). In a dynamic system there will be some years where a team is truly excellent and some years where it is not, but you can’t really know that in an absolute sense. In fact, the most ossifying element of performance appraisal is to assume that a given team or given person has reached a point where they are just excellent in an absolute sense and thus the system no longer applies. Whether the team is 100 engineers just crushing it or an executive team firing on all cylinders, it is very tempting to say the system doesn’t apply. But if the system doesn’t apply you don’t really have a system. Perhaps your organization will have a concept of “tenure” or you have a job function primarily compensated by quota based on quantitative measures—those are ways to have different systems. Best practice: Make a system that applies to everyone or have multiple systems and clear rules how membership in different systems is determined.
- Allowing for exceptions creates an exception-based process. When a team adds all of the potential constraints up and attempt to finally close in on performance of individuals, there is a tendency to “feel the pain” of all the rules and to create a model for exceptions. For example, you might have a 10% group but allow for up to 1% exceptions. Doing so will invariably create either the 9% or 11% group depending on if it is better to except up or down. If managers have the option of giving someone a low rating by extra money along with other people getting a high rating with less money, then invariably most people will get this mixed message. All of these exceptions quickly permeate an organization and individuals end up considering getting an exception a normal part of the process. Best practice: If there is going to be a system, then stick to it and don’t encourage exceptions.
- Embracing diversity in all dimensions. Far too often in performance appraisal and rewards, even within peer groups there is potential for the pull of sameness. This can manifest itself through any number of professional characteristics that can be viewed as either style or actual performance traits. One of the earliest stories I heard of this was about a manager that preferred people to set very aggressive goals for adding features. Unfortunately there was no measure for quality of the work. Other members of the team would focus on a combination of features and quality. Members of the latter group felt penalized relative to the person with the high bug count. At the same time, the team tended to be one that got a lot of features done early but had a much longer tail. Depending on when performance reviews got done, the story could be quite different. Perhaps both styles of work are acceptable, but not appreciating the “perceived slow and steady” is a failing of that manager to embrace styles. The same can be said for personal traits such as the always present quiet v. loud, or oral v. written, and so. Best practice: Any strong and sustainable team will be diverse in all dimensions.
Much more could be said about the way performance appraisal and reward can and should work in organizations. Far too much of what is said is negative and assumes a tone dominated by us v. them or worse a view that this is all a very straight forward process that management just consistently gets wrong. Like so many things in business there is no right answer or perfect approach. If there was, then there would be one performance system that everyone would use and it would work all the time. There is not.
Some suggest that the only way to solve this problem is to just have a compensation budget and let some level of management be responsible. That is a manager just determines compensation for each member of a team based on their own criteria. This too is a system—the absence of a system is itself a system. In fact this is not a single system but n systems, one for each manager. Every group will arrive at a way to distribute money and ratings that meets the needs of that team. There will be peanut butter teams, there will be teams that do the “big bonus”, and more. There will even be teams that use the system as a recruiting tool.
As much as any system is maligned, having a system that is visible, has some framework, and a level of cross-organization consistency provides many benefits to the organization as whole. These benefits accrue even with all the challenges that also exist.
To end this post here are three survival tips for everyone, individuals and managers, going through a performance process that seems unfair, opaque, or crazy:
- No one has all the data. Individuals love to remind some level of management that they do not have all the data about a given employee. Managers love to remind people that they see more data points than any one individual. HR loves to remind people that they have competitive salary data for the industry. Executives remind people they have data for a lot of teams. The bottom line is that no one person has a complete picture of the process. This means everyone is operating with imperfect information. But it does not follow that everyone is operating imperfectly.
- Share success, take responsibility. No matter what is happening and in what context, everyone benefits when successes are shared and responsibility is taken. Even with an imperfect system, if you do well be sure to consider how others contributed and thank them as publicly as you can. If you think you are getting a bad deal, don’t push accountability away or point fingers, but look to yourself to make things better.
- Things work out in the end. Since no system is perfect it is tempting to think that one data point of imperfection is a permanent problem. Things will go wrong. We don’t talk about it much, but some people will get a rating and pay much higher than they probably deserve at some point. And yes, some people will have a tough time that they might not really deserve in hindsight. In a knowledge economy, talent wins out over time. No manager will hold one datapoint against a talented person who gracefully recovers from a misstep. It takes discipline and effort to work within a complex and imperfect system—this is actually one of the skills required for anyone over the course of a career. Whether it is project planning, performance management, strategic choices, management processes and more all of these are social science and all subject to context, error rates, and most importantly learning and iteration.
—Steven Sinofsky (@stevesi)
I’ve been surprised at the “feedback” I receive when I talk about products that compete with those made by Microsoft. While I spent a lot of time there, one thing I learned was just how important it is to immerse yourself in competitive products to gain their perspective. It helps in so many ways (see http://blog.learningbyshipping.com/2013/01/14/learning-from-competition/).
Dave Winer (@davewiner) wrote a thoughtful post on How the Times reviews tech today. As I reflected on the post, it seemed worth considering why this challenge might be unique to tech and how it relates to the use of competitive products.
When considering creative works, it takes ~two hours to see a film or slightly more for other productions. Even a day or two for a book. After which you can collect your thoughts and analysis and offer a review. Your collected experience in the art form is relatively easily recalled and put to good use in a thoughtful review.
When talking about technology products, the same approach might hold for casually used services or content consumption services. In considering tools for “intellectual work” as Winer described (loved that phrase), things start to look significantly different.Software tools (for “intellectual work”) are complex because they do complex things. In order to accomplish something you need to first have something to accomplish and then accomplish it. It is akin to reviewing the latest cameras for making films or the latest cookware for making food. While you can shoot a few frames or make a single meal, tools like these require many hours and different tasks. You shouldn’t “try” them as much as “use” them for something that really matters. Only then can you collect your thoughts and analysis.Because tools of depth offer many paths and ways to use them there is an implicit “model” to how they are used. Models take a time to adapt to. A cinematographer that uses film shouldn’t judge a digital camera after a few test frames and maybe not even after the first completed work.
The tools for writing, thinking, creating that exist today present models for usage. Whether it is a smartphone, a tablet, a “word processor”, or a photo editor these devices and accompanying software define models for usage that are sophisticated in how they are approached, the flow of control, and points of entry. They are hard to use because they do hard things.
The fact that many of those that write reviews rely on an existing set of tools, software, devices to for their intellectual pursuits implies that conceptual models they know and love are baked into their perspective. It means tools that come along and present a new way of working or seeing the technology space must first find a way to get a clean perspective.
This of course is not possible. One can’t unlearn something. We all know that reviewers are professionals and just as we expect a journalist covering national policy debates must not let their bias show, tech reviewers must do the same. This implicit “model bias” is much more difficult to overcome because it simply takes longer to see and use a product than it does to learn about and understand (but not necessarily practice) a point of view in a policy debate. The tell-tale sign of “this review composed on the new…” is great, but we also know right after the review the writer has the option of returning to their favorite way of working.
As an example, I recall the tremendous difficulty in the early days of graphical user interface word processors. The incumbent WordPerfect was a character based word processor that was the very definition of a word processor. The one feature that we heard relentlessly was called reveal codes which was a way of essentially seeing the formatting of the document as codes surrounding text (well today we think of that as HTML). Word for Windows was a WYSIWYG word processor in Windows and so you just formatted things directly. If it was bold on screen then it was implicitly surrounded by <B> and </B> (not literally but conceptually those codes).
Reviewers (and customers) time and time again felt Word needed reveal codes. That was the model for usage of a “word processor”. It was an uphill battle to move the overall usage of the product to a new level of abstraction. There were things that were more difficult in Word and many things much easier, but reveal codes was simply a model and not the answer to the challenges. The tech world is seeing this again with the rise of new productivity tools such as Quip, Box Notes, Evernote, and more. They don’t do the same things and they do many things differently. They have different models for usage.
At the business level this is the chasm challenge for new products. But at the reviewer level this is a challenge because it simply takes time to either understand or appreciate a new product. Not every new product, or even most, changes the rules of the predecessor successfully. But some do. The initial reaction to the iPhone’s lack of keyboard or even de-emphasizing voice calls shows how quickly everyone jumped to the then current definition of smartphone as the evaluation criteria.Unfortunately all of this is incompatible with the news cycle for the onslaught of new products or the desire to have a collective judgement by the time the event is over (or even before it starts).This is a difficult proposition. It starts to sound like blaming politicians for not discussing the issues. Or blaming the networks for airing too much reality tv. Isn’t is just as much what peole will click through as it is what reviewers would write about. Would anyone be interested in reading a Samsung review or pulling another ios 7 review after the 8 weeks of usage that the product deserves?
The focus on youth and new users as the baseline for review is simply because they do not have the “baggage” or “legacy” when it comes to appreciating a new product. The disconnect we see in excitement and usage is because new to the category users do not need to spend time mapping their model and just dive in and start to use something for what it was supposed to do. Youth just represents a target audience for early adopters and the fastest path to crossing the chasm.
Here are a few things on my to-do list for how to evaluate a new product. The reason I use things for a long time is because I think in our world with so many different models
- Use defaults. Quite a few times when you first approach a product you want to immediately customize it to make it seem like what you’re familiar with. While many products have customization, stick with the defaults as long as possible. Don’t like where the browser launching button is, leave there anyway. There’s almost always a reason. I find the changes in the default layout of iOS 6 v. 7 interesting enough to see what the shift in priorities means for how you use the product.
- Don’t fight the system. When using a new product, if something seems hard that used to seem easy then take a deep breath and decide it probably isn’t the way the product was meant to do that thing. It might even mean that the thing you’re trying to do isn’t necessarily something you need to do with the new product. In DOS WordPerfect people would use tables to create columns of text. But in Word there was a columns feature and using a table for a newsletter layout was not the best way to do that. Sure there needed to be “Help” to do this, but then again someone had to figure that out in WordPerfect too.
- Don’t jump to doing the complex task you already figured out in the old tool. Often as a torture test, upon first look at a product you might try to do the thing you know is very difficult–that side by side chart, reducing overexposed highlights, or some complex formatting. Your natural tendency will be to use the same model and steps to figure this out. I got used to one complicated way of using levels to reduce underexposed faces in photos and completely missed out on the “fill flash” command in a photo editor.
- Don’t do things the way you are used to. Related to this is tendency to use one device the way you were used to. For example, you might be used to going to the camera app and taking a picture then choosing email. But the new phone “prefers” to be in email and insert an image (new or just taken) into a message. It might seem inconvenient (or even wrong) at first, but over time this difference will go away. This is just like learning gear shift patterns or even the layout of a new grocery store perhaps.
- Don’t assume the designers were dumb and missed the obvious. Often connected to trying to do something the way you are used to is the reality that something might just seem impossible and thus the designers obviously missed something or worse. There is always a (good) chance something is poorly done or missing, but that shouldn’t be the first conclusion.
But most of all, give it time. It often takes 4-8 weeks to really adjust to a new system and the more expert you are the more time it takes. I’ve been using Macs on and off since before the product was released to the public, but even today it has taken me the better part of six months to feel “native”. It took me about 3 months of Android usage before I stopped thinking like an iPhone user. You might say I am wired too much or you might conclude it really does take a long time to appreciate a design for what it is supposed to do. I chuckle at the things that used to frustrate me and think about how silly my concerns were at day 0, day 7, and even day 30–where the volume button was, the charger orientation, the way the PIN worked, going backwards, and more.