From data divination to data-aware design

humans and technology more generally? Do they contribute to social science understandings of human psychology and/or social behavior, and in turn, do they offer any insights that may contribute to generative design efforts? • modeling and designing adaptive systems. Can we infer from people’s actions what they are likely to want to do next so we can invoke some model-based, on-the-fly interface and/or interaction adaptations to improve their experience or move them through some pre-scripted sequence of experiences (e.g., move them up a gaming level)? • business relevance and prediction. Does the activity data within and across applications and services offer any insights that are businessrelevant to existing offerings or suggest areas for innovation? These of course represent a small fraction of the activities that are part of the current worldwide obsession with data, most publicly manifested in discussions of the global and societal implications of “big data. ” Indeed, in the world of Internet innovation, you can’t walk 10 feet without someone telling you how much data they have, and it seems calling oneself a “data scientist” is like saying you can lay golden eggs. As discussed by authors from Microsoft in the May + June issue of interactions, there are many challenges facing data analysts [1]. These challenges will only increase, as more and more data is collected from wireless sensor networks, mobile devices, software logs, cameras, and RFID readers. We have our work cut out for us in designing better tools and services for data analytics [2]. For the purposes of this column, my comments will sit conceptually somewhere between daily data analytics and societal concerns about big data. I want to offer a few personal observations and cautionary remarks about the frenzy over data and data analytics, as well as a couple of ruminations about where practitioners and researchers in HCI can—and, I believe, must—weigh in. First, while most of us in designoriented research areas are very aware of the value of qualitative methods and data, many discussions in which the word data is used tend to focus almost entirely on quantitative data, with little acknowledgement that behind every quantitative measure and every metric are a host of qualitative judgments and technological limitations: What should we measure? What can we measure? Of what is the metric constituted and what assumptions are embedded within that metric? Decisions about what does and what does not get measured are choices. Those choices have weight, and should not be taken lightly. In their 1999 book, Sorting Things Out: Classification and Its Consequences, Geof Bowker and Susan Leigh Star look at many In the past few years, I have sifted through more trace data than I care to remember. By trace data I mean logs of actions taken by users on Internet sites—mostly aggregated data from many users, but sometimes data from single users, such as search query logs. Why all this immersion in activity logs and trace data? When designing and developing any kind of technology, data is useful for many reasons, some of which are: • evaluation and understanding, using summative methods. Did feature X or service Y get used? And if so, what happened just before or just after it was used? From this we can get a sense of someone’s experience as they interact with a page or an application or between pages and applications. If it did not get used, can we determine why not? • iterative design, and data collection for formative and generative design. Did people do what we expected or desired them to do? Did their actions match our assumed task models? Did users seem to be thwarted, repeating actions and abandoning without completing a set of task-related actions? And if so, do we have a way to understand what would have worked for them? • theorizing about fundamentals of human behavior. What are the gross patterns we can discern from individuals, from variously grouped sets of people, or across the whole user population? What, if anything, do these data patterns tell us about p ot og ra p h by r aq ue l V an N ic e