The basic concept of data democratization sounds idyllic. The volume of raw data that businesses of all types are generating as part of their daily operations has grown exponentially, so the idea of giving employees throughout the organization the ability to derive insights and value from this information is extremely attractive. However, I believe the principle that giving everyone in the enterprise access to more raw data creates a competitive advantage is flawed. As the flood of data continues to increase, the ability of legacy-analytical tools to provide clear insights is rapidly degrading. And simply trying to cram more information into a dashboard only compounds the problem by presenting too many signals for most business users to fully understand and derive meaningful insights from.
The basic premise of data democratization is that putting more data in the hands of more people leads to those people making better business decisions. This sounds good, but the emphasis over the past few years on delivering so-called “self-service analytics” has made it difficult for business users to keep up with the changes to analytics and predictive monitoring in addition to their core job duties. Giving these users access to as much data as possible has become harmful rather than helpful, as humans are naturally subject to biases and making errors, especially when they are overloaded with an abundance of unfiltered data.
Not every business user needs access to self-service analytics tools; they don’t have the time nor expertise to parse through and make sense of all the data available to them. And more importantly, I would argue that they should not have to. We don’t need more data in the hands of more people; we need better insights into data in the hands of more people.
“By 2018, most business users will have access to self-service tools, but the fact remains that there’s too much data for the average business user to know where to start,” notes Anne Moxie of Nucleus Research. “What we are starting to see is a new generation of BI where mundane daily tasks are automated while flagging more significant anomalies for employees to focus on.”
Achieving this type of automation requires a new generation of analytical tools that take advantage of advances in machine learning and predictive analytics to draw real, actionable insights from complex data sets, and surface them where and when they are needed most. It’s inefficient to have employees spending time analyzing data and making mundane decisions when massive increases in computing power have evolved machine learning to the point where these types of insights can be extracted from complex data sets in seconds.
I’m not arguing that roles throughout organizations shouldn’t be data driven, but the expectation that every employee should also be their own data scientist isn’t reasonable, and from everything I’ve seen, it isn’t happening. Most workers are part of what we might call the production environment within a business. They have core job tasks to complete each day, and the notion that hundreds of employees have the time, inclination and expertise to slice and dice data on a regular basis isn’t sustainable.
The idea behind data democratization isn’t a bad one. Fulfilling the promise of Big Data requires getting the right information to the right people at the right time, in a way that they can use it. Truly achieving this, and making full use of the mountain of structured and unstructured data that your organization generates, requires re-thinking our business tools. What is needed is a new generation of analytical systems that automatically take the heavy lifting out of data analysis to give employees the right insights they need to focus on to complete their core responsibilities, make better decisions and drive more impactful business actions.
Roman Stanek is CEO and founder of GoodData. Stanek is a passionate entrepreneur and industry thought leader with over 20 years of high-tech experience. His latest venture, GoodData, was founded in 2007 with the mission to disrupt the business intelligence space and monetize big data. Prior to GoodData, Roman was Founder and CEO of NetBeans, the leading Java development environment (acquired by Sun Microsystems in 1999) and Systinet, a leading SOA governance platform (acquired by Mercury Interactive, later Hewlett Packard, in 2006).
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