The topic has shifted to Big Data (BD from here on out) and moving from general talk to some tangible applications.
The first speaker was Jeff Hunter who showed us some specific uses of BD at General Mills. Well, maybe not so much BD as creative use of a whole range of different data sources, each fit to a specific problem related to company growth. One of his key points is reminding us that we live in a very rich data environment, much of which has not been mined and, sometimes, is free or very low cost. One of his examples was use of social media and sentiment coding to evaluate a new product trial. It worked well but there are caveats: the sentiment coding method makes a difference and the approach works best with high involvement products that generate lots of buzz. In another example he described what we might think of as "desk research" to gather a number of data series that were essentially free in order to evaluate a possible acquisition in an Eastern European country. Good, practical stuff from major buyer.
David Krajicek was up next and talked about whether MR has been doing enough to take advantage of Big Data. He used terms like "datification" and "digital exhaust." (I hope these don't stick.) He showed us a nice chart depecting the difference between BD and MR: Census/Sample, Flow/Fixed Point, Atheoretical/Hypothesis-Based, Unstructured/Structured, etc. The opportunity, as he sees it, is putting the two together and translating BD into "smart data." His argument is the current MR argument in this realm, specifically, that the skills of the market researcher and our classic concerns –representativeness, correlations vs. causality, complexity, etc. are essential here. The issue here is that value of BD is too great to leave it to the mathematicians. He showed some specific examples that merge survey data with BD-like data to enrich data and provide insights not possible from each source along. The thing is, we have been doing this for years. The presentation was a really good summary of the current storyline about BD in MR. Whether it is right, remains to be seen.
Finally, we had Greg Mishkin and his client, Don Hodson from AT&T who talked about a research program that goes through a cycle of interviewing, big data integration, and qualitative work as part of continuous cycle of improvement. One neat thing about this is that it includes a survey to assess whether the original model built by merging survey data with behavioral data is right as well as assess the impact of any actions the client may take to affect attitudes and even behavior. One key advantage of the BD approach that they highlighted is solving the recall problem inherent in surveys. Most importantly, the presentation has showed how surveys and big data can be used to great effect with a company that has a huge data base of interactions with their customers.