streams; tracked geolocations; sensor outputs: This data was created for purposes other than analytics. The devices, servers, software, and processes involved – the technology – were designed for doing rather than studying. So you’ re not going to make seamless integration happen. What you can do: Stay open, flexible, agile. You’ ll then be well-positioned to tap latesttechnology data for analysis.
What
are
some
of
the
interesting
projects
that
you have
worked in
the
past that has broadened
your research perspective?
methods. your other option is to ditch
In the coming 5 years how low-grade analysis tools and simplistic metrics and methods
Seth Grimes: The interesting projects are the tough ones
is social
media going to
in favor of indicators chosen to
that force you to take risks. I’ ll
impact the way we conduct market research?
Seth Grimes: The availability of social data enables fastresponse, focused research. It facilitates early trendspotting and provides for broad access to unprompted consumer conversations. It lets you test easily and make quick judgments that optimize Campaign performance. And it supports a new sort of study, of interactions among consumers, of influence and advocacy and opinion spread by word of mouth. Social media will revolutionize market research.
Often clients complain that the research agencies are just giving information and not‘ actionable’ insights. How can the research community best tackle this issue?
Seth Grimes: Find the
support decisions and facilitate outcome measurement and the tools( and data sources) that can produce the needed insights. But I’ m repeating myself, really just rewording responses I’ ve given to other questions. And that’ s because the core point really bears repeating: Business needs come first. Work back from them to design studies and choose data and tools that will answer those needs.
We are still not seeing seamless integration of latest technology with data analysis. What will you suggest to make it happen?
Seth Grimes: We will never see seamless integration of the latest technology with data analysis needs. That’ s because our methods are either after- or outside-the-fact – surveys and focus groups being prime examples – or make secondary use of data created for other name two. I designed and led development of the Census 2000 analysis system. My team computed billions of statistical tables, some with complex hierarchical roll-ups and category definitions, under exactly timeliness, accuracy, and confidentiality strictures. We did a kick-ass job, but what I learned is how important it is to have enough verifications of your work. Second was a failed project, to build a system for an industry association. The aim was to acquire and integrate operational data from disparate sources for cross-functional research and analyses. The system worked, but the project failed because the client organization didn’ t have, and didn’ t learn, a culture of datadriven decision-making. Work that’ s not useful and usable isn’ t valuable. undemanding clients? If that purposes. Social profiles, solution doesn’ t work for you, activity, and content; click