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Big Data Analytics: Avoid the Analytic Cul-De-Sac

Blog by Philip Russom
Research Director for Data Management, TDWI

Do you know what a cul-de-sac is? In French, it literally means “bottom of the bag.” But figuratively it means what most Americans would call a “dead-end street.” In residential real estate, a cul-de-sac is a desirable place to live. In analytics, a cul-de-sac is where the epiphanies of advanced analytics never get off a dead-end street to be fully leveraged elsewhere in the enterprise.

The current hype around big data analytics has most discussions of analytics focused on “discovery” analytics. That’s where a business analyst or similar user employs an advanced analytics tool (based on data mining, statistics, natural language processing, complex SQL, etc.) to discover facts never known before. For example, the analyst may discover the root cause for a new form of customer churn, a new partner behavior that’s potentially fraudulent, or the hidden costs that erode otherwise profitable customers.

While researching a new TDWI report on big data analytics, I’ve run across a number of business analysts who revel in the chase around the cul de sac, but can’t be bothered with operationalizing their epiphanies. “That’s someone else’s job,” one guy told me. Here’s what I mean.

Too often analysts drive through a figurative big data “bottom of the bag,” until just the right dataset yields an epiphany. Then they share their findings with managers and move on to the next analytic project.

This is an analytic cul-de-sac, when the analyst does not also take the findings off the dead-end street and “operationalize” them. In other words, once you discover the new form of churn, analytic models, metrics, reports, warehouse data, and so on need to be updated, so the appropriate managers can easily spot the churn and do something about quickly, if it returns. Likewise, hidden costs, once revealed, should be operationalized in analytics (and possibly reports and warehouses), so managers can better track and study costs over time, to keep them down.

I think that most analysts and similar users are avoiding analytic cul-de-sacs, by being sure that discovered epiphanies are operationalized by someone (whether by the actual analyst or another team member). I’m just saying that the product of analytics isn’t necessarily being leveraged to the hilt in every organization.

To avoid analytic cul-de-sacs and similar squanderings of insight, you might want to review some of the processes around your use of advanced analytics. In particular, be sure the process extends beyond discovery into operationalizing the epiphanies of analytics.

So, what do you think, folks? Let me know. Thanks!

Posted by Philip Russom, Ph.D. on July 21, 2011


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