Few techies will disagree that when it comes to getting the most out of Big Data, organizations need to develop a data-driven culture within the workplace. This is one of the most oft-repeated claims we see from countless experts on the subject, but just how does one go about doing so?

It’s easier said than done, that’s for sure. As an example, it can be tough to convince an over-bearing vice president that relying on analytics is a safer bet than his or her opinion. And persuading an Excel power user to give up on their tried and trusted spreadsheets? Forget it…

But it’s been done many times before, and there are few better examples than IBM, which rakes in billions not only from selling and servicing Big Data software, but by basing its own business decisions on analytics.

Now, the co-authors of a newly published book: Analytics Across the Enterprise: How IBM Realizes Business Value from Big Data and Analytics, have taken to InformIT to explain how IBM built its own ‘data-driven culture’.

Dietrich, Plachy and Norton are all former IBM employees who’re very familiar with that company’s analytics work. Their “Ten Tips to Realize Value from Big Data and Analytics” draws upon their experience and interviews with more than 70 managers, executives and analytics experts, together with 31 case studies.

The all important first tip emphasizes what was iterated at the top of this article – that companies should create a “strong culture for the availability and use of data“ – before they delve into some of the ways leaders can make it happen.

Tips for building a data-driven culture

The authors advise leaders they can start by always asking for data to support ideas and suggestions on how the company can move forward. Leaders need to know where the ideas come from, and asking for facts to back up a suggestion is a sure way to encourage employees to start harvesting and analyzing company data.

A second tip is to require employees to follow an analytics mode. For example, Dietrich, Plachy and Norton write that IBM insisted its sales managers all begin using a ‘Coverage Optimization for Profitability’ analytics model.

Finally, they also recommend using a prototype and delivering results iteratively. Doing so will help companies to achieve value faster, and also serves the purpose of engaging users and stakeholders far sooner in the process. This helps users become familiar with using Big Data analytics tools and embracing the general concept of the idea.

There’s lots of other advice out there too. For example, Jessica Sprinkel of Logi Analytics writes in the Huffington Post that companies can require employees to report their results to a dashboard or internal wiki each day. Employees can then be rewarded as they use data to make better decisions and deliver value to the company. Meanwhile, McKinsey & Company offers up its own advice, which including embedding analytics in simple tools to help frontline managers make decisions and perform better in their jobs.

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