Busting Data Silos

One of the things I’ve noted across…let’s just say years of analyzing different types of data is the degree to which data silos can obscure the ability to see what’s really going on in the world.   By data silos, I mean the set categories of information that people who analyze and report information tend to organize around…and the categories that those who make use of the information expect to see.

There’s an example in my post: Is Your Video Traffic Upside Down?   In general, site dashboards are set up to show traffic trends (what’s happening with page views) and video trends (here are the video starts, completes, etc.).   But unless you put one in context of the other (as per the post) you won’t see the interesting patterns that reflect how people consume video relative to text…by day of week, time of day, etc.

Data silos run rampant when the results of survey research are reported.  They’re often reported by question, the answers to question 1 followed by the answers to question 2.   When the real insight is sometimes only apparent when you put the answers to question 1 next to the answers to question 17, or 21…or some other context that tells the story.   In general, I think the best practice is to isolate the themes, the learning, what do the results of the survey say?   And then pull in data to support those themes in whatever order makes conceptual sense rather than the order of the questionnaire.

Different sources of information can become data silos.   In the digital world, editorial people look at traffic data and ad sales people look at revenue, impressions and CPM data.  But the reality of the site and its business can only be truly understood by looking at the two sources in tandem.  In the world of TV networks, ratings, revenue data and brand strength data from survey research may seem like three disparate pieces of a puzzle.  But when you put the three together there are stories to be told…

I’m not recommending that we throw everything into one pile and make a great big data soup.  Hypotheses about consumer behavior and market dynamics have to be used as a lens to focus how and where we slice the information.

But I think, to large degree, the process of analysis is a process of breaking the silos of how the data was obtained and reclassifying it along the lines of what it says about the consumer and the marketplace.

Essentially, the sense we make of the world reflects the categorical structure we use to see it…

And the essential job of the data analyst is to break the artificial, ingoing structures and build the one that best approximates reality…out there in the world.


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