Data democratization is hot right now. As author Bernard Marr explains, “Data democratization means that everybody has access to data and there are no gatekeepers that create a bottleneck at the gateway to the data. The goal is to have anybody use data at any time to make decisions with no barriers to access or understanding.”

We reference the concept, not merely as a goal unto itself, but as a factor enabling companies to drive toward what Forrester refers to as the “insights-driven business.”

However, if data democratization is one of the latest concepts to attract the attention of chief data officers and other key officials inside companies, it’s rather obvious that the existing way of doing things at many firms may not support any initiative in that direction.

While Gartner may write that "advances in distributed, semantic processing for data management and integration will soon neutralize location as a dominant planning constraint, making it possible to conceive entirely new approaches to insight generation, location-agnostic processing and data democratization,” existing processes and technology will not support those new approaches.

Companies seeking to take advantage of data democratization and its benefits must consider a significant change in the way they track data and gain a better understanding of all of the information under their control; not only the data that is most obvious, but “dark data” as well, which may be located in multiple locations across the enterprise. Tracking, correlating and putting all of the data to good use requires a firm commitment to technologies such as artificial intelligence and machine learning for both smart data discovery and cataloging purposes.

An article in Analytics magazine goes one step further, saying companies that want to engage in data democratization must deploy a three-pronged strategy: first, the use of enterprise cross-functional architects; second, the effective governance of seamlessly integrated data; and third, striking a balance between data quality, scalability and agility. Utilizing that strategy when democratizing business data, the authors write, “will prove to be the next frontier of competitive advantage facilitating democratization of data and analytics.”

There are caveats, of course: there’s always a possibility that some workers may misinterpret the data and make bad decisions based on those misinterpretations. And of course, there’s always the risk of an internal data breach if more people have access to it. But I think the promise of data democratization far outweighs the perils; the more people who can gain insights from the data your company has collected, the more productive and profitable they can be.

So, the value, even the imperative, is clear: Gartner says “about three percent of leading organizations have begun trying to better understand the value of their information and the information of others, as doing so is increasingly a prerequisite to managing and monetizing information as an actual asset."

Research from Forrester finds that “While firms have made great strides in becoming data-driven businesses, the march to insights-driven maturity is slow. The majority of enterprises still are unable to extract value from their data at scale and drive impactful business results.”

If firms can extract that value, they can better empower workers throughout the company to take advantage of it, gaining an advantage over their slower-moving competitors in the process.

I agree and firmly believe that data democratization makes sense; when properly deployed, it has the potential to empower individuals at all levels, giving them greater levels of ownership and responsibility, along with the opportunity to use the data in their decision making. But data democratization must not be deployed with a “ready, fire, aim” approach, with little more than lip service; an enterprise-wide implementation and commitment to all that it entails must be present as well.