Analytics for All: Why Data Democracy Makes Business Sense
I recently helped complete a survey of 270 members of the Oracle Applications Users Group that showed that while most organizations aspire to the ideals of “data democracy,” most are still data oligarchies. (Hopefully not data dictatorships, but I'm sure there are a few in there as well.)
In fact, barely a handful of organizations in the survey (4 percent) have even attained what we defined for the survey as robust “corporate performance management-driven cultures” at this time, meaning a majority of their employees have access to the tools, dashboards and solutions available to get an accurate, up-to-the-minute snapshot of where their business stands. About half of the respondents say “very few to none” of their overall workforce has access to corporate performance data. This was the third year in a row we asked this question in the OAUG survey, and there has been no change.
While democracy in any context is an important ideal to strive for, there's more than idealism at stake when we talk about data democracy. Putting the power of analytics in the hands of decision-makers up and down the line helps affect positive and profitable changes for the business as situations change.
Venkat Mullur, senior director for financial services industry solutions for TIBCO Spotfire, recently shared some of his observations about developing a pervasive analytical culture within insurance and financial services organizations. There is a strong case to be made that successful data management initiatives are about helping the users access the data themselves, identify the data quality issues, and have more control over what they’re seeing.
“The pressure to improve profitability, while avoiding risks, has become relentless,” Mullur points out. “Successful data management and MDM initiatives must incorporate analytics that are accessible to a much broader base of users so people on the front lines — who have the domain expertise — have the power to analyze, ask questions and manipulate large amounts of changing data coming from these systems in order to test their assumptions and independently fact-check the numbers, without involving data modelers or IT.”
The payoffs from such user-driven analytic capabilities can be tremendous. Looking at financial institutions, Mullur observes that on average, about 10 percent of the data generated from their internal capital engines is incorrect. If the end-users of these reports could go in and fix the data parameters, they could tighten up these operations.
“By making analytics technology accessible to every day business users, they can easily save between eight to 15 percent by finding the irreconcilable data and outliers,” he explains.
Project that savings over a multi-billion-dollar institution, and you're talking about some serious money.
This originally appeared on Insurance Networking News.