We need a new, forward-looking approach based on the data we will soon have a lot more of.
We live in a world rich in fuzzy diversity, evermore fragmented, micro-segmented, and brimming with organizational and cultural silos, making it hard to make out the forest from the trees accurately. The same holds true for the world of analytics, which is supposed to be precise, math- and data-driven, yet is full of confusion and fuzziness. There are too few instances where organizations are vibrantly happy with data they are capturing from their web, social, or in-house analytics or are able to take clear actions on it.
What Is Convergence Analytics?
Convergence analytics is a term being used by some to describe an intersection of digital marketing with big data, and it's fueled by the (somewhat overly optimistic) belief that real-time processing of big data results in improved business insights and real-time action capabilities. The caveat is that convergence analytics requires an even higher level of professional, customized services to implement than the current set of analytics platforms. And, as business roles blur, it is getting harder to identify users/buyers who will understand how to leverage these new platforms; changes are driven by technological, business transformations resulting from a flattening organizational chart, and the target stakeholders are more evenly spread out. An interesting development are new platforms that purport to make big data more accessible through simple interfaces that don't require a rocket scientist or customized professional services to set up or use. I'll cover some of these "simpler" platforms in future columns.
Convergence Analytics Needs an Expanded Query Language
I believe the real problem, in general, with analytics today is the lack of a common event-driven descriptive language. There is no standard way to describe phenomena, entities, or actions within the wealth of mobile, social, customer databases, call center, application data, VOC, membership data via login, email, advertising, and competitive benchmarking in a unified way. Others have pointed out the lack of a standard model to apply to the data; and when modeling is done at all, it's usually customized for a particular business and its use case(s), while the results are rarely shared in any detail outside the organization from where it was performed. In either case, lack of a common language or model, most convergence platforms have a pretty tough time materializing that 360-degree view for their clients, regardless of their claim.
As a consequence convergence analytics vendors typically will normalize the data they can collect by various means to create a new 360 data view, but more often end up with bad or fuzzy data because they're trying to solve the wrong problem!
For example, one of the biggest wastes of time is trying to extract intelligence from unstructured textual data generated by using "Boolean queries" and normalizing it within a custom database in a meaningful way. Boolean queries are about as useful for culling social intelligence as an axe for cutting butter. But that's exactly what most social analytics vendors have spent several years trying to solve, often failing spectacularly!
We need a new, forward-looking approach based on the data we will soon have (a lot more of).
Figure 2: New query language more suitable for convergence analytics (Marshall Sponder, WebMetricsGuru INC)
A Picture Is Worth 1,000 Words, a Video Is Worth 10,000
I believe Google Project Glass and similar devices will lead to profound data changes in the next three years and new query languages will be developed to meet the need. What has been expressed in text (when we decide to write, blog, or tweet, or even the metadata our devices and activities generate) usually fails to describe 99 percent of the data or intent behind what we do (and that is the very data marketers wanted all along).
Privacy issues aside (and there are ways to depersonalize the data), a new set of query languages will be developed to intelligently search massive amounts of behavioral data. All of this assumes wearable computing will become rapidly popular; in fact, the new behavioral query languages won't work without the data.
In the following months I will explore newer developments in convergence analytics that help us find the data we really need today.
Image on home page via Shutterstock.
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For over a decade Marshall Sponder has influenced the development of the digital analytics industry with his WebMetricsGuru writings that focus on social media metrics, analytics and media convergence. He also possesses considerable in-house corporate experience with roles at IBM, Monster.com, Porter Novelli, and WCG while continuing to work with start-ups. Marshall is a Board Member Emeritus at the Web Analytics Association (DAA) and teaches Web Intelligence at Rutgers University and Baruch Business College. Marshall is the author of "Social Media Analytics: Effective Tools for Building, Interpreting, and Using Metrics," published by McGraw-Hill in 2011.
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