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Posts Tagged ‘tension

Everyone starts with simplicity, no-one ends there and that’s OK

simplicityDesigning a user experience for many millions of people is a unique job that a relatively small number of people practice. The responsibility of such an undertaking is immense, stressful, and one that can be all-consuming. Cold sweats, sick to your stomach, and a constant feeling of messing up are the norm for those that take on these challenges.

But someone has to do it!

This week brought quite a few big design changes that have folks talking including twitter adding mute, gmail testing out a major revamp, and iOS 8 bringing “Surface split-screen to iPad”.

Everyone starts with simplicity, then what?

At introduction almost every successful product champions simplicity as a design and execution goal. Products are declared simple, minimal, and tailored to specific uses. Almost no one argues against these attributes and when marketing goes to position a tech product, invariably these attributes bubble up to the top of the favorable list. That’s because of the inherent and expected complexity of tech products as a starting point.

At introduction almost every successful product champions simplicity as a design and execution goal.

But where to go next? Tech products that are simple can start off well, but three things exist immediately after launch.

  1. A customer need to address feedback and “fix” things that might be simple but are not quite there yet.
  2. A product need to remain competitive with the products that follow your introduction touting the same simplicity but also do a few more things (reading the reviews of your product will always demonstrate examples of wish lists)
  3. A business need to develop new products that can enhance revenue, margins, or maintain price points in the face of commoditization.

Tech products, particularly software products, are unique in that there is an almost natural tendency to organically add or to absorb features from competitive or adjacent products. Unlike physical hardware products that have COGS and BOM challenges, the incremental cost of software is simply limited to R&D (and operational costs for SaaS). That means when faced with the above existential properties, tech products will get new features pretty rapidly.

These new features will do constant battle with the simplicity of the initial release. Some argue that this is just bloat and invariably ruins products. Certainly from a design perspective this is a massive challenge. It takes enormous discipline. On the other hand, there are very few examples of software-based products that remain static. To remain static in features is to open yourself up to commoditization or disruption—a static target is an easy target.

Example: Palm Pilot

The introduction of the Palm Pilot (http://en.wikipedia.org/wiki/PalmPilot) is a fascinating historic example of simplicity leading to isolation and expiration of a product. The designers of the product did an amazing job building an amazing product. All day battery life, simplicity, specific and purpose-built as the first truly modern and truly mobile productivity tool used by the masses.

I remember in 1998 the Palm Pilot was standard issue for all new MBA students when I taught. Shortly after that time, I recall a panel discussion with one of the original designers of the product. At the time the pitch was overarching simplicity and ease of use. Everyone agreed. Then there was an audience question that changed the dynamic.

Most leading edge folks at this time were carrying Motorola flip phones along with the calendar/notes/contacts in their Palm Pilot. The problem was every time they wanted to make a call, it was a multi-step process that involved looking up a number on the Palm Pilot and juggling the two devices while typing into the phone. While this was vastly easier than going back to your desktop or attempting to pull an 8lb laptop out of your bag, it was a usability disaster.

The question was simply—when could I have all the Palm Pilot functionality on my phone? Lots of words about how you could sync (with a cable, not the cloud that didn’t yet exist), but a hardcore answer about how adding a fifth function to the Palm, a phone, would overload the functionality and make the product too complex and unusable. So the phone would never converge with things like your contacts and calendar.

Honest, that was the answer.

The problem was I was sitting there with my pre-production blackberry merrily connecting in real-time to my calendar, contacts, and email on my Exchange server. It was incredibly clear that the need of a non-converged device with a static copy of some of the important mobile tasks was no longer useful.

A pattern for how things evolve in practice

This challenge in software product design happens time and time again. It is the very nature of disruption. The new product does some things brilliantly well and simply, but is “missing” features people value from an existing product or an adjacent product.

Designers face the choice of adding new capabilities and potentially challenging the beauty of the initial release or facing competition and disruption from new competitors without that same strongly held belief. Marketing, channel, business and pricing can defend against these for a while but ultimately the ease and costs of just adding features in software will win.

The tension between user interface design and the realities and capabilities of software leads to a fairly predictable pattern for how tech/software products evolve. We can think of this pattern as evolution in five stages:

  • Introduce
  • Optimize
  • Deliberate
  • Succumb
  • Mature or Renew

Introduce. First you introduce a new product. In your view it is a thing of beauty. Whether you spent 3 years or 3 months, you are convinced it has exactly the right features done exactly the right way, though you know there are ton of things on your “to do” list. Even if you are practicing lean methodologies you are pretty sure you got it right in your heart even though there is a lot of learning to follow. Your design embodies simplicity in design and messaging. Once your product starts to get used and you have the luxury of people relying on your work you begin to see the holes and maybe even misfires in your experience design. Optimize. You have a lot of work to do to reconcile your “to do” list with what actual people using your product. It turns out that what you thought the product was missing is pretty different than what everyone else thought the product was missing. You shrug this off and take the feedback seriously because you have real-world people using your product. Quite often the innovations introduced at this stage are formalizations for how people were using your product. Add-ins, customizations, or just conventions that enhanced usage become the sorts of things you formalize in the product. You very quickly iterate and get to a much more robust, reliable, stable, and usable version of what you had originally envisioned. This becomes the foundation of your product.

Deliberate. Evolving your product at this stage is very fun. While you believe you have a product that embodies your vision, with usage you begin to see broader usage and scenarios as part of your product. There might be third parties that do similar things as you but with a slightly different or much improved take on a specific mechanism you have in your product. Because you have become a leader with your product in “the way” things are done, when you decide to introduce an innovation it comes as a deliberate and thoughtful extension of your experience. Rarely do you see pushback from a broad base of customers when something new is introduced at this stage in your product’s evolution. In fact you often are seen as taking the product to a new level and providing a broader context in which your whole category or class of products should evolve. You are basking in the glow of innovating in the user experience of your space—you have come to define the category and now you’re defining the category to include new elements of user-experience.

Succumb. The feedback your product is receiving is growing, both positive and negative. As your product is used more and more, the usage scenarios and skill-levels of your customers change dramatically. Your product is used in ways you could never imagine and customers are asking for your product to do things you would never have imagined they would ask. If your product becomes essential for some scenario, then people will ask for your product to take on attributes and features of other familiar products (if you share photos, then you’re likely to be asked for photo editing for example; if you communicate, then before long you will be asked for rules and filters; if you type then you will be asked for more and more formatting, spelling, and entry features). If during the previous stage you really believed you had achieved a level of almost Bauhaus minimalism about your product, this is the stage when you feel a relentless pressure to add more. You’re hearing from customers, pundits, press and more about the must-haves and must-dos. This is by far the most stressful time in product development—you can’t just step back and not change things, but you constantly feel like changes are all part of a slippery slope. You constantly find yourself struggling between the minimalist view of the product you have been perfecting and the need from different types of customers for seemingly contradictory types of features. It is why at this stage as a designer you feel like you are succumbing to feedback and introducing features that you know some people will value and others will see the other way or maybe just not even notice—you feel like you’re bloating your simple product. These are the hardest decisions to make and are the price of success. If you try to hang on to simplicity, you will see competitors pass you by or you’ll see engagement stagnate.

Mature or Renew. The natural evolution of most every product involves a fairly long period of incorporating features in the previous stage—you add some new things, incrementally change some existing things, and in general are working to find a path through the maze of contradictory feedback and complex market needs. Over time your product will develop a different personality and unique set of assets, but is going to be far from that original version. While you might have hundreds of millions of customers, at some point the experience of your product is such that the market collectively demands an overhaul. The challenge of course is that the collective market is very different than any one individual or an organization (for enterprise products). The latter two, unlike the aggregate view of the market, do not necessarily embrace change. This point in the evolution of your product is where you face disruption—the telltale signs of reduced engagement, alternative tools and experiences, or just a lack of energy in your ecosystem are signs that your product is overdue for improvements, new features and more. Software affords you the chance to reimagine your product and presents you with the opportunity at this time. Of course with hundreds of millions of customers, a very large number in absolute will not want any change at all. That’s why this stage of evolution is a choice—you can incrementally mature your product design or you can choose to renew your product design. These two are really that very rare of “either-or” choices. As a product designer, you will be faced with a big set of decisions when you have to design what comes next for a mature product. Be careful what you wish for you as your design might be so successful that one day you face the prospect of redesigning it in the context of a significant customer base.

Products reaching a mature stage face a fork in the road.

Products reaching a mature stage face a fork in the road—one where you can renew or watch your product slowly shrink in relevance. This might seem dramatic, but the velocity of change in the technology world combined with the ease of switching shows that one day what might seem like “the way things are done” will risk becoming “the way things used to be” much sooner than expected.

Disruption and technology transitions are part of the context of designing products and experiences.

From search home pages, to photo editors, word processors, operating systems, music players, and more these stages are all part of the evolution of a user experience. The beauty of software interface is that unlike the physical world you are given the chance to move things around, change, and improve the product for little to no manufacturing cost, but at each stage you have to work through the cost of change to customers.

No other product in history has had the ability to be used by so many yet be so flexible in how it is used.

Simplicity in design is what we all strive for and often how we begin a product lifecycle. With success, maintaining simplicity over time while also remaining competitive is where design and product management are really challenged.

The “soft” of software makes this challenge even more acute and the pressures to add or change a product even more difficult to resist.

–Steven Sinofsky (@stevesi)

Written by Steven Sinofsky

May 13, 2014 at 11:00 am

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Designing for BYO, a product manager view

imageMany companies I work with are creating tools to enhance workplace or personal productivity depends on the “bring your own” or BYO movement to get their product bootstrapped or to just get in the door. Once in the door, the product design challenges of BYO begin.

After those first customers they count on broader, viral, usage within a company to drive revenue growth. While they likely built your product with the notion that “customer equals purchaser” once this changes to “business equals purchaser”, you are going to get a whole different level of feedback.

My guess is most every app and service is both excited and terrified to get to the moment when there is a choice between cozying up to IT and risking alienating your newly minted enthusiasts. It is, by all accounts, a choice. Most I talk to feel like they will navigate this by focusing on customers first and hope to overwhelm the negatives often associated with IT.

Walk that fine line to enable your product to be at some state of détente with IT.

Get over it. Not entirely of course, but there’s some subtly at play. At some point you are going to face a fork in the road; navigate enterprise management or face existential challenges. You can choose to be managed without your cooperation or worse blocked and literally unable to access important assets that your product requires. Alternatively, you might also choose to walk that fine line to enable your product to be at some state of détente with IT.

I know that sounds awful and while I am sure there are some exceptions (in both organizations and products), this is by far the most normal path. It doesn’t have to be a sell-out, but when done well you can bet that you’re going to be in great position to advance the state of the art and contribute positively to enterprise infrastructure.

In fact, as I was typing this post there was this thoughtful article on putting customers first in business apps.

The essence of BYO is that one can easily acquire and begin to use a device, product or service without IT involvement of any kind. You might need to know the server name for email or maybe how to export data from a line of business system, but otherwise the device or app can tap into the necessary resources without first going through IT and/or purchasing. Even better, these tools likely make it very easy to share information with coworkers or collaborators at other organizations. All folks need is a free email account as a gateway to sharing.

Of course all this ease of use has at least two main IT downsides.

First and foremost is security of the network overall. Devices on a network, running code of unknown origin, tapping into servers is a big risk. What can be transmitted by those devices and apps concerns IT. Inbound PDF attachments or simple USB sticks seemed harmless enough at first until they became a massive vectors.

Second, the data and servers being accessed contain information that you need to use but do not own. These are corporate assets and managing and tracking those is a fiduciary responsibility for IT and in some cases such as HIPPA or SEC regulations the penalties for messing up are severe. That simple case of putting something like logmein or internet messaging can potentially become a significant liability.

My own personal experience “helps” me to see this pattern. Working on Microsoft Office in the early days, we were very clearly a “bottom up adoption” product. People were going to stores and buying the product with their own money and creating amazing looking documents they would bring into work (often on PCs bought with personal funds at those same stores). Pretty soon groups of people were using corporate expense accounts to acquire “5 packs” of Office. Then over time, Microsoft grew an enterprise sales force that could offer large deals.

That’s the sales side, but on the product side the management and deployment of the product (deployment being decidedly old school now) became unwieldy. As a result, the late 1990’s saw a movement to reduce so called “TCO” or total cost of ownership. TCO mandated a vast number of controls across the entire platform and from that grew a whole generation of features from the registry, to logon scripts, to the, now, dreaded “corporate desktop”. TCO reached an epic volume as it described owning a $1500 PC as a $20,000 per year expense to companies.

While I was dragged kicking and screaming to deliver features that I felt could be used to make the product worse, the reality was at the time this is also what grew the business.  The tradeoffs, debates, and design choices were all very real.

In a startup, these choices are much more existential than they were for us back then. Given the hurdles to overcome to become a widely used tool, there’s a good chance you might want to be more proactive about how your product fits with BYO.

As a product manager facing this decision point, you have this intense belief that IT wants to make your product worse, harder to use, and to basically ruin your good work. The fact that so few built-for-IT products have the design sense, usability, or approachability of apps and services focused on consumers only reinforces this.

While there are dozens of potential traps and pitfalls that can result in a product falling out of favor, it is a good idea to consider a few important design choices you can make now that will enable your consumer and BYO product to be viewed through a positive light. It is important that these design choices be considered product assets rather than object handlers.

Ultimately, if you design a product to be used in business where you can charge more it should be better, not worse, than a product used in the consumer space. It used to be that the business versions of products charged more so they could do less and be harder to use and acquire. The SaaS and App models invert this. Phil Libin, founder of Evernote, says it best when he says “business class means superior and we challenge ourselves to make our product better when you upgrade to the business version”.

Business class means superior and we challenge ourselves to make our product better when you upgrade to the business version. — Phil Libin, Evernote

The following are five product areas to consider when it comes to making a product business ready:

  • Identity and authentication. The first thing a business needs from a product is that employees should sign into the product using the business-owned credentials (such as Active Directory). This allows IT to send a clear message to the individual that they are operating in a business context. This needs to include authentication mechanisms used at the organization and enforce associated password policy and security. At the same time, you owe it to your own ease of use that stand-alone credentials can be used, especially for collaboration. How you manage the bridge and the commingling of credentials depends on the flow of assets through your product.
  • Network usage. IT organizations guard their network across several dimensions. Platform providers make it possible to use VPN (secured with enterprise credentials) or other access methods for WiFi. Your product should use well-known/documented ports and be clear with IT about what travels over the wire and in what volumes. Techniques like polling, using obscure ports, and more will only hinder your product usage.
  • Changes related to re-orgs. In an organization of any size employees quit, vendors are fired, or staffing on a project just changes. If your product is used across a group of people then IT will want to be there to assist in supporting these changes within your product. How can content remaining on devices be recalled or how can a person lose permissions to content are important design choices you can make in building a product that it BYO friendly.
  • Content “ownership”. If your product creates or consumes content then your product owes it to IT to participate in the content management responsibility of the organization. At one extreme, the clipboard exists on IT apps and in every other app so you can dodge this question by saying it isn’t your exclusive responsibility. On the other hand, by having mechanisms for IT to have some telemetry and actions on content then you invite your product to be desired by IT, not just challenged. More than any other area this is where many potential solutions exist and many possible ways to make the product worse or upgrade to business class.
  • Features. Products are more than editors and tools for sharing, so there are going to be unique features in your product. Some of those unique features will intersect in ways that might run counter to a business policy. Sometimes this could be simple such as an ability to generate email notifications which might be frowned upon. Other times it might be complex as a feature runs directly afoul of regulatory compliance. At some level there are going to be features you give IT permission to enable/disable. No area is more challenging of course and thinking hard about the design tradeoffs when a feature might not be there is important. A feature like password protection might be great for consumers but becomes a huge problem for IT when personnel change. Alternatively, you might have a feature that becomes a “must use” and if that’s the case you want to consider how something you might have thought of as optional becomes permanent. For example, you might optionally support a confirmation email when adding new people to a project and IT might require that email be sent to produce a record of access changes.

There are many other avenues to consider. I think it is possible to make a product better when enabled for business even if you start from the very solid business and design foundation of customer first.

The modern mechanisms for administering IT control are vastly superior to the PC era mechanisms. The idea of running arbitrary code, tweaking every aspect of the UI, or installing add-ins that alter base functionality of a product are long gone. These approaches showed how great products can be made unfamiliar, hard to use, and less robust even with the best intentions Worse, the mechanisms developed to enable these approaches proved to be vectors for security problems, performance challenges, and in general sources of unpredictability and unreliability.

Today’s devices support state-based management, app stores, and security contexts that greatly improve the ability to deliver upgraded business features. To many, these tools are not yet enough. The platform vendors are carefully balancing the approaches they introduce with the known downsides by the old approaches.

There’s a disruption in the way devices, apps, and information are managed, but that does not necessarily mean an elimination.

–Steven Sinofsky

Written by Steven Sinofsky

May 1, 2014 at 3:00 pm

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The Four Stages of Disruption

iexpectyoutodiemrbondInnovation 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.

disruption

Stage One: Disruption of Incumbent

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 transportationshare computationsearch 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:

Stage Disruptor Incumbent
 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.

– Steven Sinofsky (@stevesi). This story originally appeared on Recode.

Written by Steven Sinofsky

January 7, 2014 at 9:00 pm

8 steps for engineering leaders to keep the peace

Tug of warWhen starting a new product there’s always so much more you want to do than can be done.  In early days this is where a ton of energy comes from in a new company—the feeling of whitespace and opportunity.  Pretty soon though the need for prioritized lists and realities of resource/time constraints become all too real.  Naturally the founder(s) (or your manager in a larger organization) and others push for more.  And just as naturally, the engineering leader starts to feel the pressure and pushes back.  All at once there is a push to do more and a pull to prioritize.  What happens when “an unstoppable force meets an immovable object”, when the boss is pushing for more and the engineering leader is trying to prioritize?

I had a chance to talk to a couple of folks facing this challenge within early stage companies where a pattern emerges.  The engineering leader is trying hard to build out the platform, improve quality, and focus more on details of design.  The product-focused founder (or manager) is pushing to add features, change designs, and do that all sooner.  There’s pushback between folks.  The engineering leader was starting to worry if pushing back was good.  The founder was starting to wonder if too much was being asked for.  Some say this is a “natural” tension, but my feeling is tension is almost always counter-productive or at least unnecessary.

There’s no precise way to know the level of push or pushback as it isn’t something you can quantify.  But it is critically important to avoid a situation that can result in a clash down the road, a loss of faith in leadership, or a let down by engineering.

As with any challenge that boils down to people, communication is the tool that is readily available to anyone. But not every communication style will work.  Engineers and other analytical types fall into some common traps when trying to cope with the immense pressure of feeling accountable to get the right things done and meet shared goals:

  • Setting expectations by always repeating “some of this won’t get done”.  This doesn’t help because it doesn’t add anything to the dialog as it is essentially a truism of any plan.
  • Debating each idea aggressively.  This breaks down the collaborative nature of the relationship and can get in the way, even though analytical folks like to make sure important topics are debated.
  • Acting in a passive aggressive manner and just tabling some inbound requests. This is almost always a reaction to “overflow” like too much sand poured in a funnel—the challenge is just managing all the inbound requests.  This doesn’t usually work because most ideas keep coming back.

What you can do is get ahead of the situation and be honest.  A suggested approach is all about defining the characteristics of the role you each have and the potential points of “failure” in the relationship.

As the engineering leader, sit down with the founder (or your manager) and kick off a discussion that goes something like this as said from the perspective of the accountable engineering leader:

  1. We both want the best product we can build, as fast as we can.
  2. I share your enthusiasm for the creativity and contributions from you and everyone else.
  3. My role is to provide an engineering cadence that delivers as much as we can, as soon as we can, with the level of quality and polish we can all be proud of.
  4. We’ll work from a transparent plan and a process that decides what to get done.
  5. As part of doing that, I’m going to sometimes feel like I end up saying “no” pretty often.
  6. And even with that, you’re going to push to change or add more.  And almost always we’ll agree that absent constraints those are good pushes.  But I’m not working without constraints.
  7. But what I worry about is that one day when things are not going perfectly (with the builds or sales), you’ll start to worry that I’m an obstacle to getting more done sooner.
  8. So right then and there, I’d like to come back to this conversation and make sure to walk through where we are and what we’re doing to recalibrate.  I don’t want you to feel like I’m being too conservative or that our work to decide what to do in what order isn’t in sync with you.

That’s the basic idea.  To get ahead of what is almost certainly to be a conversation down the road and to set up a framework to talk about the challenge that all engineering efforts have—getting enough done, soon enough.

Why is this so critical?  Because if you’re not talking to each other, there’s a risk you’re talking about each other.

We all know that in a healthy organization bad news travels fast. Unfortunately, when the pressure is on or there’s a shared feeling of missing expectations often the first thing to go is the very communication that can help.  When communication begins to break down there’s a risk trust will suffer.

When trust is reduced and unhealthy cycle potentially starts.  The engineering leader starts to feel a bit like an obstacle and might start over-committing or just reduce the voice of pragmatic concerns.  The manager or founder might start to feel like the engineering leader is slowing progress and might start to work around him/her to influence the work list.

Regardless of how the efficacy of the relationship begins to weaken, there’s always room for adjustment and learning between the two of you.  It just needs to start from a common understanding and a baseline to talk and communicate.

This is such a common challenge, that it is worth an ounce of prevention and an occasional booster conversation.

–Steven Sinofsky

Written by Steven Sinofsky

September 11, 2013 at 6:00 am

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Conversation #38– disrupt or die

with 25 comments

thCAM164QKAnyone worth their salt in product development knows that listening to customers through any and all means possible is the means to innovation. Wait a minute, anyone worth their salt in product development knows that listening to customers leads to a faster horse.

Deciding your own product choices within these varying perspectives is perhaps the seminal challenge in product development, tech products or otherwise. This truly is a tyranny of or, but one in which changing the rules of the game is the very objective.

In this discussion, which is such a common dialog in the halls of HBS as well tech companies everywhere it should probably be a numbered conversation (for this blog let’s call this Conversation #38 for shorthand—disrupt or die).

For a recent discussion about why it is so difficult for large companies to face changes in the marketplace, see this post Why Corporate Giants Fail to Change.

“Disrupt or die” or “disrupt and die”?

Failure to evolve a product as technologies change or as customer scenarios change is sure to lead to obsolescence or elimination from the marketplace. It is difficult to go a day in tech product development without hearing about technology disruption or “innovator’s dilemma”. The biggest fear we all have in tech is failing to keep up with the changing landscape of technologies and customers, and how those intersect.

At the same time, hopefully we all get to that lucky moment when our product is being used actively by customers who are paying. We’re in that feedback loop. We are improving the product, more is being sold, and we’re on a roll.

That’s when innovation over time looks like this:

Incremental

In this case as time progresses the product improves in a fairly linear way. Listening to customers becomes a critical skill of the product team. Product improvements are touted as “listening to customers” and things seem to go well. This predictability is comforting for the business and for customers.

That is, until one day when needs change or perhaps in addition a new product from a competitor is released. Seemingly out of nowhere the great feedback loop we had looks like it won’t help. If we’re fortunate enough to be in tune to changing dynamics outside our core (and growing) customer base we have time to react and change our own product’s trajectory.

That’s when innovation looks like this:

New Product

This is a time when the market is receptive to a different point of view, and a different product — one that redefines, or reimagines, the category. Sometimes customers don’t even realize they are making a category choice, but all of a sudden they are working differently. People just have stuff to get done and find tools that help.

We’re faced with what seems like an obvious choice—adjust the product feature set and focus to keep up with the new needs of customers. Failing to do so risks losing out on new sales, depth usage, or even marginalization. Of course features/capabilities is a long list that can include price, performance, battery life, reliability, simplicity, APIs, different integration points or service connections, and any other attributes that might be used by a new entrant to deliver a unique point of view around a similar scenario.

Many folks will be quick to point out that such is only the case if a new product is a “substitute” for the product people are newly excited about. There is truth to this. But there is also a reality shown time and time again which gets to the heart of tech bets. It is almost always the case that a new product that is “adjacent” to your product has some elements of more expensive, more complex in some dimensions, less functional, or less than ideal. Then what seems like an obvious choice, which is to adjust your own product, quickly looks like a fool’s bet. Why would you chase an inferior product?  Why go after something that can’t really replace you?

The examples of this are too numerous to count. The iPhone famously sucked at making phone calls (a case where the category of “mobile phone” was under reinvention and making calls turned out to be less important). Solid State storage is famously more expensive and lower capacity than spindle drives (a case where the low power, light weight, small size are more valued in mobile devices). Of course tablets are famously unable to provide apps to replace some common professional PC experiences (a case where the value of mobility, all day battery life, always connected seem more valued than a set of platform capabilities). Even within a large organization we can see how limited feature set cloud storage products are being used actively by employees as “substitutes” for enterprise portals and file shares (a case where cross-organization sharing, available on the internet, and mobile access are more valued than the full enterprise feature set). The list goes on and on.

As product managers we all wish it was such a simple choice when we face these situations. Simply leapfrog the limited feature set product with some features on our profitable product. Unfortunately, not every new product that might compete with us is going to disrupt us. So in addition to facing the challenges of evolving the product, we also have to decide which competitors to go after. Often it takes several different attempts by competitive products to offer just enough in the way of new / different approaches to begin to impact an established product.

Consider for example of how much effort the Linux community put into desktop Linux. And while this was going on, Android and iOS were developed and offered a completely different approach that brings new scenarios to life. A good lesson is that usually a head-on alternative will quite often struggle and might even result in missing other disruptive technologies. Having a unique point of view is pretty important.

The reality of this situation is that it is only apparent in hindsight. While it is going on the changes are so small, the product features so minimal, and the base of the customers choosing a new path so narrow that you don’t realize what is going on. In fact, the new product is also on an incremental innovation path, having attained a small amount of traction, and that incremental innovation rapidly accumulates. There is a tipping point.

That is what makes acting during such a “crisis” so urgent. Since no one is first all the time (almost by definition when you’re the leader), deciding when and how to enter a space is the critical decision point. The irony is that the urgency to act comes at a time when it appears from the inside to be the least urgent.

Choosing to innovate means accepting the challenges

We’ve looked at the landscape and we’ve decided as a team that our own product needs to change course. There is a real risk that our product (business) will be marginalized by a new entry adjacent to us.

We get together and we come up with the features and design to go after these new scenarios and capabilities.

The challenge is that some of what we need to do involves changing course—this is by definition what is going on. You’re Apple and you decide that making phone calls is not the number 1 feature of your new mobile phone or your new tablet won’t run OS X apps. Those are product challenges. You also might face all sorts of challenges in pricing, positioning, and all the things that come from having a stable business model. For example, your competitor offers a free substitute for what you are selling.

The problem is your existing customers have become conditioned to expect improvements along the path we were traveling together. Worse, they are by definition not expecting an “different” product in lieu of a new version of their favorite product. These customers have built up not just expectations, but workflows, extensions, and whole jobs around your product.

But this is not about your existing and best customers, no matter how many, it is about the foundation of your product shifting and you’re seeing new customers use a new product or existing customers use your product less and less.

Moving forward the product gets built and it is time to get it into market for some testing or maybe you just release it.

BOOM!

All that work your marketing team has done over the years to establish what it means to “win” in the space that you were winning is now used against you. All the “criteria” you established against every competitor that came along are used to show that the new product is not a winning product. Except it is not winning in the old way. What you’ve done is become your own worst enemy.

But even then, the new way appears to be the less than optimal way—more expensive, less features, more clicks, or simply not the same at doing things the product used to do.

The early adopters or influential users (that was an old term in the literature, “IEU” or sometimes “lead user”) are immediately taken aback by the change in direction. The workflows, keystroke memory, add-ins, and more are just not the same or no longer optimal–there’s no regard for the new scenarios or capabilities when the old ones are different. Worse, they project their views across all customer segments. “I can’t figure this out, so imagine how hard it will be for my parents” or “this will never be acceptable in the enterprise” are common refrains in tech.

This happens no matter who a product is geared towards or how complex the product was in the first place. It is not how it does anything but the change in how it did things people were familiar with. This could be in user experience, pricing, performance, platform requirements or more.

You’re clearly faced with a set of choices that just don’t look good. In Lean Startup, Eric Ries talks in detail about the transition from early users of a new product to a wider audience. In this context, what happens is that the early users expect (or tolerate) a very different set of features and have very different expectations about what is difficult or easy. His conclusion is that it is painful to make the transition, but at some point your learning is complete and it is time to restart the process of learning by focusing on the broader set of customers.

In evolving an existing product, the usage of a pre-release is going to look a lot like the usage of the current release. The telemetry proves this for you, just to make this an even more brutal challenge. In addition, because of the years of effort the enthusiasts put into doing things a certain way and all that work establishing criteria for how a product should work, the obvious thing to do when testing a new release is to try everything out the old release did and compare to the old product (the one you are changing course of) and then maybe some new stuff.  This looks a lot like what Eric describes for startups. For products in market, the moment is pretty much like the startup moment since your new product is sort of a startup, but for a new trajectory.

Remember what brought us here, two things:

  • The environment of usage or business around the product was changing and a bet was made that changes were material to the team. With enough activity in the market, someone will always argue that this change is different and the old and new will coexist and not cannibalize each other (tell that to PalmPilot owners who swore phones would be separate from calendar and contacts, or GPS makers who believe in stand-alone units, or…).
  • A reminder that if Henry Ford had asked customers what they wanted from a car they would have said a faster horse. The market was conditioned to ask for and/or expect improvements along a certain trajectory and no matter what you are changing that trajectory.

All the data is flowing in that shows the new product is not the old product on the old path. Not every customer is interested in doing new things, especially the influential testers who generally focus on the existing ways of doing things, have domain expertise, and are often the most connected to the existing product and all that it encompasses. There is an irony in that for tech these customers are also the most tech-savvy.

Pretty quickly, listening to customers is looking exceedingly difficult.

If you listen to customers (and vector back to the previous path in some way: undo, product modes, multiple products/SKUs, etc.) you will probably cede the market to the new entrants or at least give them more precious time. If technology product history is any guide, pundits will declare you will be roadkill in fairly short order as you lack a strategic response. There’s a good chance your influential customers will rejoice as they can go back and do what they always did.  You will then be left without an answer for what comes next for your declining usage patterns.

If you don’t listen to customers (and stick to your guns) you are going to “alienate” folks and cede the market to someone who listens. If technology product history is any guide, pundits will declare that your new product is not resonating with the core audience. Pundits will also declare that you are stubborn and not listening to customers.

All of this is monumentally difficult simply because you had a successful product. Such is the price of success. Disrupting is never easy, but it is easier if you have nothing to lose.

Many folks will be quick to say that new products are fine but they should just have the old product’s way of doing things. This can seem like asking for a Prius with a switch to turn off the battery (my 2002 Prius came with a training DVD, parking attendant reference card, and more!). There are many challenges with the “side by side” approach. The most apparent is that it only delays the change (meaning delays your entry into the new market or meeting of new scenarios). Perhaps in a world of cloud-services this is more routine where you have less of a “choice” in the change, but the operational costs are real. In client code/apps the challenge becomes very quickly doing things twice. The more complex the changes are the more costly this becomes. In software nothing is free.

Product development is a social science.

People and time

In this numbered conversation, “disrupt or die” there are a few factors that are not often discussed in detail when all the debates happen.

First, people adapt. The assumption, especially about complex tech products, is that people have difficulty or lack of desire to change. While you can always overshoot the learning people can or are willing to do, people are the most adaptable part of a system. One way to think about this is that every successful product in use today, those that we all take for granted, were introduced to a customer base that had to change behavior.  We would not be where we are today without changing and adapting.  If one reflects, the suboptimal change (whether for the people that are customers or the people running a business) is apparent with every transition we have made.  Even today’s tablets are evidence of this.  Some say they are still for “media consumption” and others say they are “productivity tools”.  But behind the scenes, people (and developers) are rapidly and actively changing and adapting to the capabilities of tablets because the value proposition is so significantly improved in some dimensions.

Second, time matters. Change is only relative to knowledge people have at a moment in time and the customers you have at the moment. New people are entering the customer base all the time and there is a renewal in skills, scenarios, and usage patterns. Five years ago almost no one used a touch screen for very much.  Today, touch is a universally accepted (and expected) input method.  The customer base has adapted and also renewed around touch.  Universities are the world’s experts at understanding this notion of renewal. They know that any change to policy at a university is met with student resistance (especially in the spring). They also know that next year, 25% of the “customer base” will be replaced. And in 3 summers all the students on campus will only know the new way. One could call that cynical. One could also call that practical.

Finally time means that major product change, disruption, is always a multi-step process. Whether you make a bet to build a new product that disrupts the market dynamics or change an existing product that disrupts your own product, it rarely happens in one step.  Phones added copy/paste and APIs and even got better at the basics.  The pivot is the tool of the new endeavor until there is some traction.  Feedback, refinement, and balancing the need to move to a new space with the need to satisfy the installed base are the tools of the established product “pivoting” in response to a changed world.  It takes time and iteration–just the same way it took time and iteration to get to the first summit.  Never lose sight of the fact that disrupting is also product development and all the challenges that come from that remain–just because you’re disrupting does not mean what you do will be perfect–but that’s a given we all work with all the time.  We always operate knowing there is more change to come, improvements and fixes, as we all to learn by shipping.

Part of these factors almost always demonstrate, at least in the medium term, that disruption is not synonymous with elimination.  Those championing disruption often over-estimate progress towards elimination in the short term.  Though history has shown the long term to be fairly predictable.  Black cars are still popular.  They just aren’t the only cars.

Product development choices are based on social science. There is never a right answer. Context is everything.  You cannot A/B test your way to big bets or decisions about technology disruption. That’s what makes all of this so fun!!

Go change the rules of the game!

–Steven Sinofsky

Note.  I believe “disrupt or die” is the name of a highly-regarded management class at General Electric’s management school.

Written by Steven Sinofsky

May 8, 2013 at 1:00 pm

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Learning by slipping

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Countdown“Slipping” or missing the intended completion or milestone date of software projects is as old as software itself. There’s a rich history of our industry tracking intended v. actual ship dates and speculating as to the length of the slip and the cause.  Even with all this history, slipping is a complex and nuanced topic worth a bit of discussion about slipping as an engineering concept.

Slipping

I’ve certainly had my fair share of experience slipping.  Projects I’ve worked on have run the full spectrum from landing exactly on time to slipping 20-30% from the original date.  There’s never a nice or positive way to look at slipping since as an engineer you’re only as good as your word.  So you can bet the end of every project includes a healthy amount of introspection about the slip.

Big software projects are pretty unique.  The biggest challenge is that large scale projects are rarely “repeated” so the ability to get better through iteration keeping some things constant is limited.  This is different than building a bridge or a road where many of the steps and processes can be improvements from previous projects.  In large scale software you rarely do the same thing with the same approach a second or third time.

While software is everywhere, software engineering is still a very young discipline that rapidly changes.  The tools and techniques are wildly different today than they were just a few years ago.  Whether you think about the languages, the operating systems, or the user experience so much of what is new software today is architected and implemented in totally new ways.

Whenever one talks about slipping, at some basic level there is a target date and a reality and slipping just means that the two are not the same (Note:  I’ve yet to see a software project truly finish early).  There’s so much more to slipping than that.

What’s a ship date

In order to slip you need to know the ship date.  For many large scale projects the actual date is speculation and of course there are complexities such as the release date and the availability date to “confuse” people.  This means that discussions about slipping might themselves be built on a foundation of speculation.

The first order of business is that a ship date is in fact a single date.  When people talk about projects shipping “first quarter” that is about 90 different dates and so that leaves everyone (on the team and elsewhere) guessing what the ship date might be.  A date is a date.  All projects should have a date.  While software itself is not launching to hit a Mars orbit, it is important that everyone agree on a single date.  Whether that date is public or not is a different question.

In the world of continuously shipping, there’s even more of a challenge in understanding a slip.  Some argue that “shipping” itself is not really a concept as code flows to servers all the time.  In reality, the developers on the team are working to a date—they know that one day they come to work and their code is live which is a decidedly different state than the day before.  That is shipping.

Interestingly, the error rate in short-term, continuous projects can often (in my experience) be much higher.  The view of continuously shipping can lead to a “project” lasting only a month or two.  The brain doesn’t think much of missing by a week or two, but that can be a 25 – 50% error rate.  On a 12 month project that can mean it would stretch to 15-18 months, which does sound like a disaster.

There’s nothing about having a ship date that says it needs to be far off.  Everything about having a date and hitting it or slipping can apply to an 8 week sprint or a 3 year trek.  Small errors are a bigger part of a short project but small errors can be amplified over a long schedule.  Slipping is a potential reality regardless of the length of the schedule.

The key thing from the team’s perspective about a ship date is that there is one and everyone agrees.  The date is supported by the evidence of a plan, specifications, and the tools and resources to support the plan.  As with almost all of engineering, errors early in the process get magnified as time goes by.  So if the schedule is not believable or credible up front, things will only get worse.

On the other hand, a powerful tool for the team is everyone working towards this date.  This is especially true for collaboration across multiple parts of the team or across different teams in a very large organization.  When everyone has the same date in mind then everyone is doing the same sorts of work at the same time, making the same sorts of choices, using the same sorts of criteria.  Agreeing on a ship date is one of the most potent cross-group collaboration tools I know.

Reasons to slip

Even with a great plan, a team on the same page, and a well-known date, stuff can happen.  When stuff happens, the schedule pressure grows.  What are some of the reasons for slipping?

  • Too much work, aka “we picked too much stuff”.  The most common reason for slipping is that the team signed up to do more work than could be done.  The most obvious solution is to do less stuff.  In practice it is almost impossible to do less once you start (have you ever tried to cut the budget on a kitchen remodel once it starts?  You cut and cut and end up saving no money but costing a lot of time.) The challenge is the inter-connected nature of work.  You might want to cut a feature, but more often than not it connected to another feature either upstream or downstream.
  • Some stuff isn’t working, aka “we picked the wrong architecture”.  This causal factor comes from realizing that the approach that is halfway done just won’t work, but to redo things will take more time than is available.  Most architecturally oriented developers in this position point to a lack of time up front thinking about the best approach.  More agile minded developers assume this is a normal part of “throw away the first version” for implementing new areas.  In all cases, there’s not much you can do but stick with what you have or spend the time you don’t have (i.e. slipping).
  • Didn’t know what you know now, aka “we picked the wrong stuff”.  No matter how long or short a project, you’re learning along the way.  You’re learning about how good your ideas were or what your competitors are doing, for example.  Sometimes that learning tells you that what you’re doing just won’t fly.  The implications for this can run from minimal (if the area is not key) to fairly significant (if the area is a core part of the value).  The result in the latter case can be a big impact on the date.
  • Change management, aka “we changed too much stuff”.  As the project moves forward, things are changing from the initial plans.  Features are being added or removed or reworked, for example.  This is all normal and expected.  But at some point you can get into a position where there’s simply been too much change and the time to get to a known or pre-determined is more than the available time.

The specifics of any slip can also be a combination of these and it should be clear how these are all interrelated.  In practice, once the project is not on a schedule all of these reasons for slipping begin to surface.  Pretty soon it just looks like there’s too much stuff, too much is changing, and too many things aren’t “right”.

That is the nature of slipping.  It is no one single thing or one part of a project.  The interrelationships across people, code, and goals mean that a slip is almost always a systemic problem. Recognizing the nature of slipping leads to a better understanding of project realities.

Reality

In reality, slips are what they are and you just have to deal with them.  In software, as in most other forms of engineering, once you get in the position of missing your date things get pretty deterministic pretty quickly.

In the collective memories of most large projects that slipped are the heroes or heroic work that saved a project.  That could very well happen and does, but from a reliable or repeatable engineering perspective these events are circumstantial and hard to reproduce project over project.  Thus the reality of slipping is that you just have to deal with it.

The most famous description of project scheduling comes from Frederic P. Brooks who authored “The Mythical Man-Month” in 1975.  While his domain was the mainframe, the ideas and even the metrics are just as relevant almost 40 years later.  His most famous aphorism about trying to solve a late project by adding resources is:

When a task cannot be partitioned because of sequential constraints, the application of more effort has no effect on schedule.  The bearing of a child takes nine months, no matter how many women are assigned.

Software projects are generally poorly partitioned engineering – much like doing a remodel in a tiny place you just can’t have all the different contractors in a tiny place.

There are phases and parts of a project in large scale software that are very amenable to scale with more resources, particularly in testing and code coverage work, for example.  Adding resources to make code changes runs right up against the classic man-month reality. Most experienced folks refer to this as “physics” implying that these are relatively immutable laws.  Of course as with everything we do, context matters (unlike physics) and so there are ways to make things work and that’s where experience in management and most importantly experience as a team working together on the code matters.

The triad of software projects can be thought of as features, quality, and schedule.  At any given point you’re just trading off against each of those.  But if it were only that easy.

Usually it is easy to add features at the start, unaware of precisely how much the schedule or quality will be impacted.  Conversely, changing features at other times becomes increasingly difficult and obviously so.  From a product management/program management perspective, this is why feature selection, feature set understanding, and so on is so critical and why this part of the team must be so crisp at the start of a project.  In reality, the features of a product are far less adaptable than one might suspect.  Products where features planned are not delivered can sometimes feel incomplete or somehow less coherent.

It is almost impossible to ever shorten a schedule.  And once you start missing dates there is almost no way to “make up for time”.  If you have an intermediate step you miss by two weeks, there’s a good chance the impact will be more than two weeks by the end of a project.  The developers/software engineers of a project are where managing this work is so critical.  Their estimates of how long things will take and dependencies across the system can make or break the understanding of reality.

Quality is the most difficult to manage and why the test leadership is such a critical part of the management structure of any project.  Quality is not something you think about at the end of the project nor is it particularly malleable.  While a great test manager knows quality is not binary at a global level, he/she knows that much like error bars in physics a little bit of sub-par quality across many parts of the project compounds and leads to a highly problematic, or buggy, product.  Quality is not just bugs but also includes scale, performance, reliability, security, and more.

Quality is difficult to manage because it is often where people want to cut corners.  A product might work for most cases but the boundary conditions or edge cases show much different results.  As we all know, you only get one chance to make a first impression.

On a project of any size there are many moving parts.  This leads to the reality that when a project is slipping, it is never one thing—one team, one feature, one discipline.  A project that is slipping is a product of all aspects of a project.  Views of what is “critical path” will need to be reconciled with reality across the whole project, taking into account many factors.  Views from other parts of the organization, the rumor mill, or just opinions of what is holding up the project are highly suspect and often as disruptive to the project as the slip itself.  That’s why when faced with a slipping project, the role of management and managing through the slip is so critical.

What to do

When faced with a slip, assuming you don’t try to toss some features off the side, throw some more resources at the code, or just settle for lower quality there are a few things to work on.

First and foremost, it is important to make sure the team is not spending energy finger pointing.  As obvious as that sounds, there’s a natural human tendency to avoid having the spotlight at moments like this.  One way to accomplish that, improperly, is to shine the light on another part of the project.  So the first rule of slipping is “we’re all slipping”.  What to do about that might be localized, but it is a team effort.

What else can be done?

  • Don’t move the goalposts (quality, features, architecture).  The first thing to do is to avoid taking drastic actions with hard to measure consequences.  Saying you’re going to settle for “lower quality” is impossible to measure.  Ripping out code that might not work but you understand has a very different risk profile than the “rewrite”.  For the most part, in the face of slipping the best thing to do is keep the goals the same and move the date to accomplish what you set out to do.
  • Think globally, act locally. Teams will often take actions that are very local at times of slipping.  They look to cut or modify features that don’t seem critical to them but have important upstream or downstream impact, sometimes not well understood on a large project.  Or feature changes that might seem small can have a big impact on planned positioning, pricing, partnerships, etc.  The approach of making sure everyone is checking/double checking on changes is a way to avoid these “surprises”.
  • Everyone focuses on being first, not avoiding being last.  When a project has more than a couple of teams contributing and is faced with a tight schedule, there’s a tendency for a team to look around to just make sure they are not the team that is worse off.  A great leader I once worked with used to take these moments to remind every part of the project to focus on being first rather than focusing on being “not last”.  That’s always good advice, especially when followed in a constructive manner.
  • Be calm, carry on. Most of all, slipping is painful and even though it is all too common in software, the most important thing to do during crunch time is to remain calm and carry on.  No one does good work in a panic and for the most part the quality of decisions and choices degrades if folks are operating under too many constraints that can’t get met.  It is always bad for business, customers, and the team to slip.  But if you are slipping you have to work with what you’ve got since most of the choices are usually even less desirable.

Managing a software project is one of the more complex engineering endeavors because of the infinite nature of change, complexity of interactions, and even the “art” that still permeates this relatively new form of engineering.  Scheduling is not yet something we have all mastered and slipping is still a part of most large projects.  The more that Software Eats the World ($), the more the challenges of software project management will be part of all product and service development.

Given that, this post tried to outline some of the causes, realities, and actions one could take in the face of learning by slipping.

–Steven Sinofsky

Written by Steven Sinofsky

May 1, 2013 at 10:00 pm

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Combining guessing and planning in product development

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Mission: Impossible movie frameAt the extremes of product development methodology, characterized by waterfall and agile approaches, are different views about planning. Many today would say that planning a product “up front” is nothing more than guessing that locks you into a guess that will be wrong. At the other end, the conventional wisdom is that you should get something to the market soon for testing as a best guess and then iterate and learn to further develop a product. Since no team really practices a method precisely, let’s look at how to get the best out of the planning aspects of your own product development process.

Challenges

Developing a product, new or an update, always faces the same challenge at the start. Using software as an example, folks want to get coding as soon as possible since not coding is clearly wasting time, but the team (and management) want to know that the code will yield the right product (useful, cool, innovative, profitable, whatever). There are usually those on the team that claim instinct tells them what to go build. There are those that are certain they can work out an answer to the right product with enough up front ideation.

To compound these broad challenges, different disciplines have different perspectives. It is likely testers will want to spend more time up front on building a baseline and foundation for the work, but a baseline relative to what? Ops needs to know answers to questions in order to scale and provide the required infrastructure, but those answers require a lot of information you probably don’t know. Designers usually want time to iterate in lower fidelity media in order to hone in on the design language and overall approach. Business folks want to know that their key problems (onboarding, churn, referrals, etc.) will be addressed.

All of these lead to the natural tension between a desire to get started and a desire to pause and make sure the start heads in the right direction.

Waterfall (using this as a positive description) methods argue that an effort should begin with work to identify the problem being solved, available technologies, and proposals for how to go forward. The basic notion is that you should spend the energy considering a wide range of alternatives before you just start down a path that might be fruitless. These days you will be hard-pressed to find proponents of waterfall approaches because of the downsides often associated with waterfall execution.

The downsides of this approach, as critics claim, is that you waste a lot of time building up robust plans that are “going to be wrong (or outdated) anyway”. This criticism essentially states the reality that most plans are still guesses. This is particularly true in a fast-changing, multi-player marketplace where you can wake up one morning to a competitive product or dramatically new approach to solving the problem you identified many moons ago. The ultimate downside of the waterfall approach is that it is viewed as stubborn—so stubborn that even in the face of awareness about a changing market, the team has no choice but to move forward. Proponents will offer tools to mitigate any of these risks and say none of the risks are intrinsic to the methods.

Agile development methods respond to these criticisms by creating an approach where the primary focus is to get enough product work done so you can learn from real customers and then iterate. At the extreme, efforts should actively strip away all non-essential elements of the product development process and focus on the essence of the idea. Agile methods focus on constant iteration, learning, and responding to usage of the product (or lack thereof). Teams focused on iteration work in a tight loop to quickly adapt to the changing landscape and competitive dynamics, in addition to learning.

The downsides of agile methods are actually trickier to pin down right now. These days being critical of agile methods labels one as somewhat of a Luddite in the world of product development. That said, there are understood challenges with agile methods. The pressure to release can often result in a quality bar that is less than customers (or your own testers) would appreciate. A focus on learning might cause you to learn that your service does not scale or scales in a cost-ineffective manner. A large project (people, code, partnerships) is challenging enough to keep coherent and multiple efforts executing in different iterative loops poses a significant architectural and communication challenge. Proponents will offer tools to mitigate any of these risks and say none of the risks are intrinsic to the methods.

It is no surprise that proponents of any method can point to successes, while critics can point to failures of methods as well. In reality, the causal relationship between a project success and the method used is weak at best. Even with examples of success, we need to keep in mind that product development projects are not traditional repeated processes and as such the ability to draw scientific conclusions from them is limited. This should be apparent from the fact that successful products come from both methods, and equally true is that products can be failures from both methods.

It is not unusual for failures in waterfall-styled projects to be ascribed to the methods, but not so with successes. Whereas failures in agile projects usually ascribed to external factors, not the methods. In today’s climate, it is not unusual for agile to be viewed as a causal factor for a successful product.

That’s really why starting the project by declaring the methodology runs the risk of missing the big picture. It runs the risk of spending finite and scarce time on abstract or “meta” concepts and not the work at hand. The best thing is to avoid going down that path in the first place and focus on getting clarity on what work will get done and when that will happen (and why!).

The truth is that starting a product is always a guess. Whether you plan every detail or just start coding, beginning a new product is a leap of faith based on intuition. To those of us that build or have built new products, this leap is the most interesting, terrifying, and rewarding part of product development. It does take a special confidence to make that leap. Planning every last detail can give you a false sense of security. Coding out of the gate can give you a false sense of progress. Guessing is guessing, whether you have 1000 pages of specs and high fidelity model or a whiteboard with a sketch and a functional prototype.

These challenges—choosing between methods and the known challenges of any methods—are real. In product development we are faced with these every time we start a new project. If you have a clear starting point, clear points in time to check on your progress, and a plan so you know what you’re working against then you’ve defined a methodology that works for you.

Where to start?

In a previous post we talked about focusing on the work and not worrying about the label of the methodology. A concrete way to realize this is to take a step back and see how to combine the elements of agile and waterfall in the right amount for your project.

An approach that scales from new projects to next iterations, and small project to big, is to plan to iterate your plans. While this sounds like an obvious cop-out to pick the best of both extremes, it is what reality tends to look like in practice. If you start out knowing you are going to commit time to up front planning, but recognize you will take points in time during development to adjust and learn, then you can mitigate the challenges of both methods.

Of course agile proponents say there are always some plans. And waterfall proponents always say there is room to adjust. Let’s just say those labels and attributes don’t matter and try to arrive at an approach of where to start.

Every project can start with a plan. Legendary products start with a sketch on the back of a placemat at a diner or an all-night coding session. The original plans for some pretty big projects were conceived and documented in pretty short, but articulate, memos or detailed sketches/prototypes. The spark for a plan can come from anywhere and different people have different ways of translating that spark into something more than one person can internalize and visualize.

While a tool like PowerPoint can communicate the gist of the plans, the details will be too open to interpretation. So write down the plan in long form—writing is thinking.

The simple act of writing down a product plan in a couple of dozen pages opens you up to have useful discussions with a broad set of people. If you’ve identified the problem being solved, competitive products/services, technology bets, and overall investments you have the basis of how to talk with marketing, design, development, testing, and product management. Everyone can look at the plan from their perspective and offer insights, advice. You can even package this up as a dialog or exercise with potential customers.

Combining this overview with a functional low-fidelity prototype is a way to visualize the plan for a broader audience. It is usually a good idea for the prototype to support the written description and to lead with the written description. You’ll want to minimize the time you spend on “don’t worry this UX (user experience) isn’t final” or responding to feature suggestions without the context of where you are heading.

This is all a plan needs to be. It is a guess. You can’t prove it is right. You can’t prove it is a good business, great product, or the right thing to do. You can criticize it. You can add more to it. You can find problems. That will be true of any plan (or frankly any product). But you now have a foundation to move forward. To build something that is a “target” that is shared by a group—a vision.

With this plan comes the first chance to iterate. What’s amazing about just writing this down is how much you’ll find you’re iterating on your own thinking. You can think of this somewhat as agile planning. This shouldn’t be new though – anyone who has “told a story” of a product or an idea knows that the story improves quite a bit as you tell it more. This is basically the same thing. Any good product plan is a story—the problem you are solving is the challenge to overcome, the competitors are the antagonists, the technology bets and investments are the plot devices.

Depending on the size of the team/project, the next steps are about the specifics of what to do when. The amount of up front work and the ordering of the tasks is really a choice the team needs to make. Being economical about what you do is of course a key part of ordering. Agile methods often say to do the least possible work to express the unique value of the product. Waterfall methods are about landing all the details early. Different projects will simply have different ideas of what to do at this point and your own intuition as to what makes a good investment of time, relative to quality and time to market, will dominate.

Iteration: Local or globally optimize?

Regardless of the specifics of your development schedule, you are going to iterate. You can choose to iterate after code is in the hands of customers (in a broad or limited way) or just self-hosting until a broader release. The key though is to iterate.

Iteration is as much of an art form as deciding how much of what investments to do up front. It is super easy to fall into a trap of iterating but not making forward progress. You can find yourself rewriting the same code, circling back to previously discarded alternatives, or just changing things but not making them better. As necessary as iteration is, simply iterating does not mean you and the project are moving forward.

The lack of iteration or iteration at only the most fine-grained levels is potentially a sign of a project that won’t learn as it goes. Consider a standard kitchen remodel, something that has been done millions of times. Architects draw up plans and pass them off to contractors. Then you run into existing conditions. You find you’re missing an electrical run or there’s a support wall where one wasn’t expected. It is time to iterate on the plans. You can hack through a solution with your contractor on site. Or you can take a step back and work with the architect to reconsider the design. Either can work depending on your constraints. When you consider the time value of choices, it becomes more interesting to think about taking a step back.

In the heat of the moment with the need to get in market or respond to significant challenges with early customers, a redesign or revisiting decisions seems risky. Maybe the data is poor. Maybe the fear of discovering the need for big changes concerns you. Perhaps you just want to keep moving forward. All too often when problems arise in a project the need to iterate quickly trumps taking a step back. In today’s environment it is often viewed as a positive to iterate and try something different. Activity is not always progress. Change is not always improvement.

Of course the data you use to determine what to try and how to value the feedback is important. It is just as easy to get led astray by the wrong measures of success/failure. Regardless of the quality of data, you’re going to reach a point where you are faced with the need to change something. The question is whether the changes are the right ones.

The point of a change will be to optimize your product. You’re going to have to pick the dimension for which you want to optimize—is it for immediate mitigation or longer term success. It is easy to see mitigating the immediate challenges with some changes. Longer term might feel like another guess. On other hand immediate changes have an obvious fragility relative to broader goals and there’s clearly an appeal to being thoughtful about what to change.

Having a sense of a plan helps you to weigh the alternatives. Are you dealing with a bug or minor design nit or is there a fundamental flaw in the value proposition? There’s no mistaking the reality that you might hit a major reset, especially on a brand new product. There’s also a reality that you will have to revisit a pretty broad set of small design choices—that is steps in a flow or portions of a design, rather than the entire flow or design language.

Defining a time up front when the product is in a state to evaluate all the feedback and make choices about how to optimize is an important part of the process. This checkpoint stage can be a first self-host, private beta, partner beta, public beta or anything in between. Any product today is going to have telemetry and an understanding of how it is used and what you are measuring. This helps you to inform both what is going on and even what you failed to measure correctly.

Armed with a set of potential problems to address—optimizations yet to be done—there’s a simple question to ask of the team, which is “do we need to change/fix this or not, and if we need to take a step back and re-evaluate?”

A way to look at what to change/fix or not is to think of changes relative to the longer term goal, to go beyond the immediate. There’s no doubt the feedback about something is real. The question is really whether the cost to change (hours, risk, churn) gets you closer to the broader goals of the plan or is more reflective of iterative activity.

A checkpoint discussion where members of the team are aware of all the changes going on and what is being prioritized is a way to level-set. Some teams might have bigger challenges or more changes and other teams might be making more local changes. Calibrating these across the team is akin to making sure the whole project is thinking and acting globally, not locally. The plan that was put in place serves as a reminder of what the team was hoping to accomplish. Accountability, aka decision making, can be clear because the roles and responsibilities are clear and communication has been clear. A discussion to inform doesn’t have to be an invitation for everyone else to join in revisiting the choices made by the team.

Is the new data driving you to revisit the plan completely (whether immensely detailed or not)? That could be. For a brand new business and/or a brand new product where the effort is to grow an entirely new customer base, you could be going in a wrong direction. For a product update, there’s always going to be pressure from existing customers to innovate in a more incremental manner versus taking the product in a new direction. The presence of a plan allows you to have an informed dialog as to what went wrong. When you make choices to change things you have a shared foundation upon which to agree about what went wrong.

Is the data driving you to tweak something? That certainly is the case with some changes. The presence of the plan allows you to decide how critical it is to make changes. Too often changes to the code are made because of the presence of feedback even if the change doesn’t really alter the overall outcome. When you choose to keep things a bit more stable it doesn’t have to be viewed as blindly sticking to a plan when it can just be prudent engineering of cost-benefit. The capability to change is not the same as the value of change. Something that might be 10% better might introduce a high risk of change management or might just be 100% different–ask if something really is twice as good (or 10x better) after the change.

There’s a simple view of optimization that one can use in having discussions about changes—whether at the feature level of the whole plan. The idea is to discuss whether the change is a global optimization or a local optimization. When resources are right and time to market critical, optimizing locally is wasteful. When the plan is generally right but has some holes you discover, then making sure you optimize globally leads to an agile view of planning. The following just sketches this conceptual view–believe it or not this can often be a useful visual aid in the discussions around whether a change is needed.

Image that represents the difference between local and global optimization.  There is an x and y axis showing conceptual peaks and valleys in product development.  The current "you are here" position is a low point.  Local optimization is shown as a positive peak.  Global optimization is an even higher positive peak.

Whether you label the axes performance, suitability to task, conversation rate, success rate, or transactions per second is not really as important as taking a step back and asking the question about whether the change gets you close to where you need to be over time. It is far too easy to get caught optimizing your plan relative to the nearest peak, not the best peak.

Whenever you have more than a few folks working together, having a set of tools to help you make a consistent set of choices across the team is critical. The more there is a shared view of the goals and the way to make choices the easier this becomes—a plan is a way of encapsulating the broader goals and giving you a place to both measure relative success and to decide the target was wrong. The presence of a plan does not enforce rigidity any more than the use of agility guarantees you will iterate to success.

Product development is a lot of guesswork. Planning, checkpointing, and deliberate decision making are tools to help you make the most informed guesses you can make.

This Week’s Poll

This week kicks off a new feature of Learning by Shipping — Three Quick Questions. This is a snap poll to share aggregate (non-scientific) reactions to the topic of this post, which will be reported in the next post. Take the poll – Three Quick Questions. Cameron Turner, an expert in big data and measuring how products are used in the real world is helping with these polls. No identifying information is collected or maintained.

–Steven

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Written by Steven Sinofsky

March 11, 2013 at 1:00 pm

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