About a month ago, Dan Rockwell and I finished writing an article for interactions magazine about Casual Data, the term we’ve used to describe rich data propagated or mined via some form of social media. The piece defines Casual Data, talks briefly about why it’s becoming so prevalent, and then proceeds to identify current ways it’s being used and what that means to the fields of design and research. It will be out in a spring 2010 issue of interactions, however, here’s a sneak peek at some of the data nugget goodness:
The problem with too much data
While there are a number of firms analyzing the surface value of casual data, there is a need dig deeper to understand context and higher-level implications. The more connected we become, the more connected our data becomes, and the more we need a structured approach for making sense of it.
Companies having loads of customer data available is not news, however this casual data is not quantitative in nature (demographics, pattern-focused). The emotional meaning behind casual data should not be analyzed statistically, and the methods used to gain this data are as important to understand as the data itself. If customer voice is only harvested through an existing medium (e.g. submitting a query for iPhone-related tweets) the results you get will be brief and will tend to either be of intense glee: “new iPhone copy/paste function, thank GOD” or intense distaste: “Apple sucks!” – leaving little room for understanding context of use, while still providing good touch-points for product improvement. There is the potential of casual data being more dangerous than helpful if not properly understood.
The need to find long-term meaning via any quick casual data-farming medium creates a niche opportunity for research firms to use their proven techniques to analyze and understand this abundance of user input. Professional researchers will be able to understand how casual data is useful, where it is applicable and where there are still unanswered (and often unasked) questions. This will allow research companies to reinforce doing more in-depth research as a result of learnings from this data, rather than allowing clients to use this data (which is often incomplete) as conclusive.Even tools that have built-in analysis capabilities cannot play down the importance of involving a comprehensive research process. Design researchers look at data to understand not only design opportunities but also to come up with high-level emotional themes. If 10 people say that they want a certain feature from pampers.com, what does that mean in terms of their needs, and how will they benefit from that feature? Extrapolating concepts, ideas and feedback into themes can help the design team understand trends and potential meta-themes, and consequently how to design new products and services that weren’t necessarily articulated by their customers. Researchers also have the opportunity to help companies understand how to manage all of this data – does it need to lend itself to searching by future company stakeholders, or will it be regenerated? Having a plan for where the data goes can increase the value attained from it, and help to track trends over time.
