Monday, September 28, 2009

Project Infusion, Miami

I'm back from the IDSA National Conference, Project Infusion, in Miami, and will post my impressions of some of the more interesting sessions in the next few days. Until then, we'll make do with some of the extracurricular events at the conference: Damien Vizcarra, Kevin Young, and Jung Tak of Continuum with their double-winning entry in the IBM Ultimate Derby, "Swine Flu." The design won both the race in their category and the People's Favorite award.

Money added to the car's piggy bank increased the weight and so made the car go faster. I and a number of others packed our change into it until it was full. The designers are multiplying the amount collected by ten and will donate $ 1000 to design education. Nice going, guys!

Below, Lorraine Justice, Head of the Design School at Hong Kong Polytechnic, in a round of PowerPoint Karaoke, in which she presents slides she has never seen before. This was a diversion cooked up by Tamara Christensen of Arizona State, and was great fun between sessions.

Monday, September 14, 2009

F L Wright's Meyer May House Anniversary

September 10th was the 100th anniversary of Frank Lloyd Wright's Meyer May House, built for retail magnate Meyer May in 1909 in Grand Rapids, Michigan. This is perhaps the most meticulously-preserved example of Wright's work, renovated by Steelcase in 1987.

As I get ready to take another group of students through the history of industrial design, it's a joy to find Steelcase's detailed site about this house. I especially like the video on the house and the process of restoring it, as well as the section that illuminates Wright's design principles. Any student of ID or architectural history will be rewarded for spending some time here.

Saturday, September 12, 2009

Analytical Toolsets

Here is the set of tools for analysis of research data that John Payne presented at EPIC 2009. He ran a workshop in which we discussed and refined this process. I was especially interested, as I had come to the same conclusion as John—that there are few who have assembled an organized and comprehensive way to analyze research results. I had begun to assemble a kit of tools of my own:

In my previous post I showed the "Tool Picker" for helping design students decide which research methods to use. The right-hand edge of that diagram containing the list of methods is shown, above. The question: after you use the proscribed set of methods in the field, how do you make sense of what you've found?

I have been putting together a set of tools gathered from my own experience and the experience of others (such as the good folks at the Institute of Design at IIT, Dori Tunstall, Lloyd Walker, Andy Ogden, among others). This is the "Insights : Opportunities" deck we've been using in my Design Investigations course. The intent is that, with the use of a variety of "lenses" through which to look at the data, the conclusions will be more robust. I've been very pleased with the results. Where before, students finished their research presentations with a single slide containing three or four bullet-point conclusions, they are now concluding with ten or twelve slides, each pointing out a viable design opportunity that derives from an insight from the research.

When I saw John's Analysis / Synthesis Palette at EPIC, I was fascinated. He is coming at the same problem from a completely different direction. I am using the metaphor of a group of individuals looking at the research data, each with a different point of view. John is looking at the process itself, and creating, in a wonderfully methodical way, different ways to arrange, sift, compile, deconstruct, and recombine the data, winding up with prescribed directions.

I will be looking over my notes for some time, to decide how I will change what I'm doing based on his approach.

Saturday, September 5, 2009

The Insight : Opportunity Deck

Research is worthless unless it fuels the design process. Once the fieldwork is done, we need additional tools to help us make sense of what we've got. I have been using a variant of the KJ Method (developed by Jiro Kawakita in the 60s, similar to Affinity Diagrams) for years in my course, but recently I've begun to beef up the process by which we analyze what results. I've begun to assemble a deck of analytical aids to help guide students' thinking into areas they might not automatically consider. I've found many methods in use for fieldwork and am developing an aid to reduce the complexity of navigating that decision (discussed in the previous post), but to date I haven't found many aids for making sense of the the analysis process.

In practice, designers always work in a multidisciplinary team and research findings are interpreted by a number of different specialists: designers, human factors engineers, anthropologists—the list varies according to the needs of the project. In student work and also in small design firms, those multiple viewpoints may not exist. The deck consists of lists of questions that we can "ask" the data—questions that an anthropologist might ask, or a cognitive scientist, or an engineer, or a management consultant.

Students stand in front of the wall of data and work their way through the deck, each card acting as a lens through which they view the data. The deck is in two parts: an insight deck and an opportunity deck. The first part helps reveal important insights that might fuel design opportunities. We work slowly and methodically through the deck, making an effort to find—even force—connections between the questions and the data.

The insights are listed, mapped, or arranged in diagrams, as needed. The second deck is used to create and validate the design opportunities represented by each insight.

This process takes two or three weeks, at least. At the end, we link the insights to opportunities for design intervention, seeking quantity, quality, depth, and range: products, experiences, and business models from near term to blue sky, mild to wild. Our aim is to present our clients with a robust set of insight : opportunity pairs, hooking each opportunity to the insight that inspired it.

This is a work in process. Last week at EPIC2009 I took part in an amazing workshop with John Payne from MomentDesign, who showed us an analysis framework he's been developing, and based on that excellent session (which I hope to cover in an upcoming post) I know I will be developing this further.

I'll be presenting this work at the IDSA National Conference in Miami in a few weeks. If any of you are attending, I'd love to have the opportunity to show you more and get your feedback. See you there!

Friday, September 4, 2009

Designing Design Research

By way of an explanation for why I haven't posted lately, this last term was consumed by two projects: finishing the plan for what I've come to call the "tool picker" (above) to help designers new to qualitative research expand their palette of methods, plus a set of analytical tools to use on the research data.

This, on top of a term of research for a multi-term project for the American Red Cross, kept me busier than a dot painter in a paisley tie factory. I'll post more on all of this in the upcoming weeks.

The so-called "tool picker," above, is an attempt to help designers explore beyond a set research methodology. As currently taught (and sometimes practiced), design research is often treated as a constant set of tools and, as a result, students tend to think that it's a standard process. The field of design research has evolved into a complex landscape of approaches, however, and good design practice stays abreast of these developments.

In order to help my students break out of a narrow approach and yet negotiate the complexity of the myriad methods in practice today, I am attempting to acquaint them with a comprehensive and yet manageable set of methods. Also, I need to equip them with an understanding of why, and in which situations, a particular approach would be effective.

Currently, the research approach is chosen by those with expertise. There is a "guru" who brings years of experience to bear on the decision. Is there a way to enable beginners to more quickly gain the experience necessary to know which approach might be best for a given problem?

I distilled the complex set of approaches in use today into a set of eighteen (you see them down the right-hand side of the diagram, above). I will be creating a decision-making tool to guide the students through the decision process by asking a series of questions about what type of knowledge they seek for a given topic.

Starting at the left-hand side with a careful choice of topic, students are asked to generate a research objective statement. We discuss issues of ethics, scope, appropriateness, and so forth, and gain explicit knowledge of the researcher's bias.

Moving on to the decision process (while at the same time generating specifications of which sorts of participants will be recruited and engaging in the recruitment process), students begin to consider the type of knowledge they seek. We consider three general areas of knowledge about the user: what they do, what they feel, and who they are. Moving right-ward through the diagram, you can see how we move into finer levels of discrimination, arriving at a recommended set of methods.

This is a first rough design for the tool. When I first completed this version, I was disheartened at first by seeing that, if one worked backwards through the chart one could see that a skilled researcher could use any of the tools to uncover any of the types of knowledge desired. But I reminded myself that this is a decision tree that helps beginners and widens their view beyond a limited single-thread process. The tool is designed to lead them to the most appropriate choice, by no means the only choice possible. Once they've used the tool for a few projects, they will begin to gain knowledge of the wider set of approaches and begin to see how the different methods work in different cases. Once they begin to see that the tools actually can be tailored to many purposes, they are right where I want them: imbued with a robust working knowledge of the multivariate research process.