Stalking the Data-Curious Culture – SDC Reflections

This is the first monthly post in the SDC Reflections series where I gather examples from recently contributed articles to address a timely topic. Regardless of advances in analytics tools, a company’s culture can be the show-stopper in deriving value from available data.

This is the first monthly post in the SDC Reflections series where I gather examples from recently contributed articles to address a timely topic. Regardless of advances in analytics tools, a company’s culture can be the show-stopper in deriving value from available data.

I recently read an article by Rebecca Denison in the blog PRbreakfastClub titled Measurement and Data Analysis Should be Built into Culture. The gist of it was this:

According to a recent study, only 40% of companies are measuring social media’s performance on a regular basis. While there are likely many reasons for this, one overarching reason for a lack of measurement is a lack of measurement culture.

It reminded me a the keynote Tom Davenport delivered at the eMetrics Marketing Summit last March in San Francisco where he barely mentioned technology while preaching the critical need for business culture to become more analytic so as to make use of the growing bonanza of data available.

Here in SmartData Collective we’ve seen a series of articles lately describing this cultural conundrum. At the same time, on our sister site (also moderated by yours truly) Social Media Today, there is more and more reference to social business. (See Michael Fauscette’s Three Cs of Social Business and our SMT interview with IBM’s Sandy Carter.)

Clearly there’s plenty of overlap there – social practice, social data, social analytics.

SDC contributor Meta S. Brown wrote Executives Don’t Like Analytics – Why Business Isn’t Data–Driven. She describes how there are case studies of companies using analytics productively and there are shelves full of books about how to implement analytics in your business, but executives don’t like being proven wrong in their decision making, which is more likely when data analysis digs deeper into the facts.

It’s true: executives don’t like analytics. But you can guide them to an appreciation of data-driven decision making by introducing the ideas step-by-step and building understanding and comfort along the way. Remember – start small, focus on low-risk decisions at first, and learn to speak their language!

As Michael Ensley explains in his post The Analyst Function is Dead,

The culture has to reward critical thinking.  This is not true in most corporate cultures.  All too often, the analyst is criticized for not “going along” with the current belief.  If the culture does not reward new thinking, then the analysis will quickly fall in line with visualizations that support the status quo.

Brett Stupakevich recognizes the cultural realities in Making Collaborative BI Happen, observing:

Everyone loves to talk about collaboration. It evokes positive associations – teamwork, sharing, and a host of others. And it fits our experience that great ideas and successful efforts usually happen when people come together. So it is no surprise that collaboration has become a hot topic in the BI community.

But making collaboration happen, in BI or any other domain, is dicier. Good tools for collaboration are not enough by themselves. If people aren’t open to the give-and-take of collaboration, or the institutional culture does not foster it, the best tools in the world won’t create it.

R evangelist David Smith, in his article Big Analytics – Closing the Clue Gap, refers to the cluelessness that prevents businesses from keeping pace with the massive amounts of data relevant to them. He quotes Dion Hinchcliffe of ZDnet, who blames the “parochial ways” of companies, and Tim O’Reilly who said,

Companies that have massive amounts of data without massive amounts of clue are going to be displaced by startups that have less data but more clue.

These are challenges that some businesses will certainly overcome, but that many will find too daunting. It’s not enough to have good models to simply emulate; culture is far too complex for that.