Anatomy of an Iteration

“Where did the Office team at Microsoft get their inspiration and ideas for the last version of Microsoft Office?”

I’ve been asking this question to different teams of developers every month, for the past 15 years. Every team comes back with the same list of sources, even though, over that period, there have been no less than six new versions of Office and tremendous advances in technology.

The answer is always “from watching and listening to the customers and users of the previous versions of Office.” In other words, the newest version can only exist because there were previous versions. Microsoft’s team has been iterating their designs, one after the other, to get to where they are today. And they’re still iterating.

Iterations are a key piece of any engineering effort. You take an idea, develop it into something, try it out, see how it works, and then start all over again. Microsoft, and every other software company, does this with every new version.

Of course, the release of the new version of Office isn’t the only iteration since the last version. The team has iterated hundreds, if not thousands, of times, using the same exact pattern. It’s just that they don’t show those iterations to the public (and only some iterations to the armies of alpha and beta testers).

And this is one of the great paradoxes of design management: The vast majority of iterations are never seen by anyone outside the team. So, it looks to the outside world that, when a great product comes out, that the team just sat down, thought it through, and built it, without any trial or errors. But nothing could be farther from the truth.

Dissecting A Successful Iteration

Iterations are key to successful design. They help reduce risk by letting us get all the bad ideas out of our system early, keeping only the best of the best.

Unfortunately, we’ve found that many teams don’t know how to iterate effectively. Good iteration is a deliberate activity, with four important stages: planning, implementing, measuring, and learning. The best teams focus on each stage appropriately, making sure they get the most out of it. While iterations can be very short, (we’ve seen teams that can iterate a dozen times in a single day,) the best teams don’t short change any of the stages.

If you’re familiar with Agile development, these stages will sound very familiar. That’s because there are parallels between the types of iteration we do when designing great user experiences and what developers do when building applications. The big difference between UX iterations and Agile iterations is that most Agile iterations focus on coding something, whereas UX iterations can have alternative deliverables, such as wireframes, or persona descriptions.

Interestingly, iteration didn’t originate in either the UX or Agile worlds — its origin goes back to the beginning of engineering practice, hundreds of years. And most interestingly, the stages are still the same today:

Stage #1: Planning

In the planning stage, we decide what we want to learn from this iteration and how we’ll make it happen. We’re not planning what the final design will be, only this iteration. Planning doesn’t have to be a complex process, but it is a necessary part of the conversation.

We go into the iteration with a specific problem in mind. It’s in the planning stage that we decide how we’ll try to tackle that problem and how we’ll tell if we’ve succeeded.(Because it’s easier to understand what to plan once you understand the other stages, we’ll come back to it a little later.)

Stage #2: Implementing

The implementing stage is the most famous part of the iteration. It’s where we create the prototype or deliverable we want to get feedback on.

What’s important to remember about the implementation is that we just want to build out those portions that we’re looking for feedback on. If our final design will have certain functionality, say print capabilities, that we’re not quite ready to look at, then we don’t have to build that portion.

In fact, we can get quite lean. For example, if we are only interested in how the data will show up on the screen, we can hard-code the data into the prototype, eliminating any need to implement a back-end database for this iteration. Conversely, if we’re only interested in whether the server can handle the transactions at a reasonable speed, we only need to write a program that stresses the transaction engine, ignoring the UI.

Stage #3: Measuring

Measuring is a critical stage of the iteration. It’s where we collect the data that will help us decide if our iteration is moving us forward, or if we have to rethink the way we solve our problem.

How we measure will depend on the information we’re trying to collect. If we want to see whether the user interface works for the user, we conduct a series of usability tests. If we want to see if a particular interaction sequence feels right, we just try it out ourselves. Measuring doesn’t have to be a complex process — it just needs to reflect what we’re trying to assess.

Stage #4: Learning

Learning is the final stage and one we often don’t pay enough attention to. In this stage, we take the data we collected during the measuring stage and identify the lessons we’ve learned.

Some lessons will be affirmative — they’ll tell us that the ideas we’ve generated achieved the goals we set out. Some lessons will be more constructive — telling us that we haven’t reached our objectives, but giving us ideas on what to do differently. Using what we’ve learned, we’ll then go back into the planning stage to decide what we want to get from the next iteration.

Revisiting the Planning Stage

Now that you’ve had a chance to see all the stages, let’s take a closer look at the planning stage. As I mentioned earlier, the planning stage is only about this iteration. We want to plan out exactly how we’ll implement and measure. We want to ensure we have the resources necessary to collect the data and to provide the time for identifying what we’ve learned.

Here’s an example plan: We’ll start by deciding what we want to learn in this iteration. Can we can come up with a checkout flow that makes sense with users using the point redemption process? We decide that we can do it with a paper prototype, starting with modifying the existing checkout process with the new functions. We’ll test it with a handful of shoppers, who we’ll recruit from other parts of the company, like the accounting department. We’ll give ourselves a half day to do the paper prototype and a half day to run our usability tests, wrapping it up with one hour debriefing meeting.

Best Practices for Successful Iterations

Over the years, we’ve learned some tricks to ensure we get the most out of our iterations:

First, you need to allocate as much time and effort to the Measuring and Learning stages as you do to the Planning and Implementing stages. If you find yourself taking two weeks to implement, but only give yourself a day for measuring, then something’s out of balance. It’s a common mistake for teams to short themselves on measuring, thereby not getting a chance to learn everything they should.

Second, look for techniques to shorten implementation. Make sure you’re only implementing functionality that is necessary for this iteration — leave the other functionality for future iterations, when it will matter. Also, look to prototyping techniques and deliverables that get you the answers you want quickly. We like paper prototyping because it’s very fast to get something a user can play with.

Explicitly looking at your process for iterating will give you a chance to refine your techniques, while helping you get the most out of your design process.

Iterations and the Prototyping Process

Iterations are a key part of the prototyping process. If you’re looking to improve your prototypes, you’ll want to attend Richard Rutter and James Box’s full-day seminar at the UIE Web App Summit, Wireframing and Prototyping for Highly Interactive Web Apps.

Share Your Thoughts with Us

Have you started to put together frameworks? Is this something you’re exploring? Share your thoughts and comments at the UIE’s Brain Sparks blog.

Using assumption questions to tame complexity – PQ & PA Skill Sharpener

February 2009

Precision Questioning and Precision Answering are tools for working with complex situations in smart ways. The current global economic situation certainly qualifies as complex! Almost every day we have a chance to see, through the window created by the media, national leaders, economists, business executives, and journalists asking questions about the changing economic climate. Even these experts, however, can be stymied by the complexity of the situation.

For many of us who aren’t experts, when complexity threatens to become overwhelming we move to the sidelines of the conversation. With PQ+A as part of your repertoire, however, you don’t need to be an expert to ask useful questions that keep you in the discussion. And in situations where you need to tame complexity, one of the most helpful intellectual skills you can develop is your ability to ask questions about assumptions.

So this month, during times of great complexity, we want you to use your environment to build your assumption questioning skill. We are going to ask you to try this: every time you turn to a source of news, analysis, or opinion, use the PQ+A Toolkit and its detailed examples of assumption questions to build your ability. In fact, get out your PQ+A Toolkit right now, and follow along as we take a look at assumptions in some of the news and analysis we’ve been reading.

Existence Assumptions
We hear a lot of talk these days about “bubbles.” For instance, you might be familiar with the mortgage bubble that started downward pressure on home prices, or the consumer debt bubble that might be next to pop. Have you been reading about the bubble created by zombie banks? To really understand the economic climate, we need to ask about the existence of all of these so-called bubbles.

Existence assumptions help you sort out whether or not all of these bubbles are equally real. Some people claim, for example, that there’s an enormous bubble created by credit default swaps. But apparently, if that bubble were to pop, each player’s losses would be more or less matched by their gains. So does a bubble exist in that market?

Whenever you encounter provocative headlines such as depression, epidemic, crisis, or panic, ask yourself and others: does this condition actually exist? We guarantee an interesting conversation will result.

Uniqueness Assumptions
Since we are talking bubbles, let’s ask this: how many bubbles are there in the contemporary economic situation? Are they distinct from one another? In what ways are they unique, and in what ways do they overlap?

These questions about uniqueness assumptions—applied to bubbles or meltdowns or any other financial jargon—will help you sort out what is happening by getting a better picture of where concepts border one another. Knowing the boundaries of the intellectual terrain helps you tame the complexity.

Measurement Assumptions
What assumptions are people making about the validity of key metrics in the current economic climate? We’ve been hearing a lot about unemployment rates, for example. You probably know that the numbers of unemployed people reported in the press typically exclude people who have been unemployed over a longer term or who have given up looking for a job. What do you see differently if you adopt a different measure of the unemployment rate? Understanding more about the assumptions embedded in measurement helps you form a more accurate picture of a situation and also spot areas of unmeasured or untapped complexity.

Measurement assumptions also matter when you want to compare across groups. For example, if we want to understand the economic situation from a global perspective, perhaps we need to understand measurement assumptions for phrases like “economically active population,” and “currently available for work,” vs. “seeking work” as they apply across different populations and national boundaries.

Possibility Assumptions
Possibility assumptions are particularly important when we start to talk about solutions to complex problems. One way of understanding the debate between economists and politicians about a “stimulus package” is to unpack people’s assumptions about what it is possible to change in this situation. For example, we might ask in the current economic climate: are we assuming that it’s possible to safely let the air out of all the bubbles?

Political leaders might be wondering about the possibility of global financial collaboration. Is it possible to bring world leaders together to address a global financial crisis in a coordinated manner? (Political crises don’t necessarily engender the possibility of cooperation!)

Value Assumptions
If we determine it is possible to let the air out of the economic bubbles, we could then ask about value assumptions associated with that possibility. For example, even if it is possible to break the consumer credit bubble, is it a good idea to do so?

Value assumptions show you where you are taking for granted that something is bad vs. good, positive vs. negative. The press tends to construe current economies around the globe as bad for everyone. In the long run, however, who stands to gain the most from the current situation? (One answer: in the current climate, the business for fortunetellers is expanding, not contracting!)

Audience Assumptions
When economists talk about recessions, trade deficits, and national debt, they tend to assume that everyone in their audience understands the technical meaning of these words. If you don’t fit the characteristics of the assumed audience, you will have a chance to form and practice many types of questions of clarification. Of course, we urge you to do that whenever you encounter technical terms that you don’t understand.

But here is another audience assumption we’ve been seeing a lot these days: a newspaper columnist writes something like: “We need to think about how the downturn is affecting the people at the bottom of the ladder. They keep our economy running.” Hey, we say to ourselves, is it really the case that “we” the readers are thinkers and decision makers, and “they” aren’t reading this column? Audience assumptions can help you re-think your implicit ideas about we vs. they or us vs. them and then reconfigure your solutions to complex challenges in ways that become more inclusive.

Category Assumptions
We’re swamped in category assumptions in the current financial situation. Consider how many times you’ve read about a “mortgage” crisis, a “regulatory” crisis, a “credit” crisis, and “a crisis of confidence” in the banking system. As we begin to divide the market into categories like these, we come to take for granted that these categories correctly or meaningfully represent what is happening. What if, beneath the surface, there is now a completely different kind of crisis? What if separating these topics into categories masks the reality of a holistic financial crisis that has evolved in such a way that it is essentially unrelated to any one of these categories?

When we examine category assumptions, our thinking about the complexity of the problem and the necessary solution opens up to consider different directions or ideas that are not immediately obvious. Category assumptions often blind us to new ideas or to seeing complex situations in holistic ways. Every once in a while, challenge the expert assumptions about categories if you want to see new approaches to the situation.

Similarity Assumptions
In the United States, for people of a certain generation, the word “depression” automatically invokes a picture of a 1930′s era dominated by soup lines and public works projects. Asking about similarity assumptions will help break down these types of automatic pictures, because they help us sort out how this situation is similar to or different from the economic and cultural situation in America at that time. Your questions might sound like this: comparing 2009 with 1931 or 1935 or 1939, what are the similarities in population, culture, consumerism, literacy, and other relevant concepts? What are the differences in economic instruments and economic regulation, as well as culture, consumerism, population density, and population distribution?

Comparison across time and across rapidly changing circumstances is tricky stuff-whenever you find yourself thinking that now is similar to then, stop and ask more questions about similarity assumptions.

Time/Constancy Assumptions
Last month in Vervago’s skill sharpener, we suggested that questions about time and constancy assumptions should become your best friend in the current economic situation. The changing circumstances and the evolving responses to those circumstances provide us with challenges on multiple fronts. Most of what we know about the situation is dynamic, because very little in our economic climate is constant just now. Learn to ask more questions that sound like: how has that topic or situation changed in the last days, weeks, or months? How do those changes reshape the picture I have of the situation now as opposed to the picture as it was a few days, weeks, or months ago?

Practice Taming Complexity
One of the deepest lessons that PQ+A offers is that you do not need to be an expert in a topic area to ask precise and valuable questions. To answer the questions – yes, that sometimes requires expertise. But as Precision Questioners, we don’t need to become overwhelmed by complexity or give up and sit passively on the sidelines of conversations about the changing world.

Every day this month, as you listen to the news or read about your marketplace, use one of the nine types of assumption questions we reviewed above to tame its complexity and gain a better view of what is happening. Keep this list in the same place and add to it each day. By doing this, you will find that as your list grows, you can identify those questions about assumptions that are most pivotal to your work and to your own changing economic circumstances.


Are you trying to find PQ+A colleagues and PQ+A companies? Join our LinkedIn alumni group.

If you’re looking for other forms of support as you learn to use PQ+A in a non-PQ+A world, contact QuestionMaster@vervago.com.

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Hollander 2 Residence in California by Dutton Architects

Comment
There is hope for those hunting in the Bay Area…

If they have a couple of million.
enjoy

Hollander 2 Residence in California by Dutton Architects

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Dutton Architects took a 1950s ranch house and added more living space in addition to a 2,500-sq-ft art gallery. Oh, the modern mid-century-style ceiling is to die for.

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http://www.duttonarchitects.com