Enabling real-time data democratization is no easy task philosophically, organizationally, or technologically.
At the beginning of data democratization, the biggest barrier was philosophical and bureaucratic. Organizations of all sizes operated under the now-obsolete concept that certain people in specific departments had the access required to integrate data sources, manage the data infrastructure, and run analytics software.
Once organizations broke themselves free from that perception and started letting other business users run analytics on their data regardless of their technical know-how, they began to unlock performance and efficiency benefits they would have never thought possible. And plenty of tools, like low-/no-code analytics platforms, emerged to meet a growing demand to visualize data in ways more people can understand.
But this change, however positive, has also been close-sighted. For the most part, data democratization has focused on historical batch analysis on stable, stored data. Think of marketing folks trying to understand which version of their promotional materials transformed into the highest average lifetime value for the company. Or for customer service teams to understand, holistically, how a new effort to document their APIs reduced the volume of help desk calls, made customers more proactive and profitable, and ultimately improved the company’s bottom line.
The next frontier is the democratization of real-time data—the idea that everyone within an organization should have the access and tooling required to analyze and make sense of what’s happening right now to make faster, more proactive decisions around their KPIs and overall objectives.
Here are some technological trends supporting this drive toward real-time data democratization.
Automated integration tools: These tools unburden technical staff from being the gatekeepers—or enablers—of the business users masses who want to connect platforms X, Y, and Z together. Instead of manually connecting APIs or mapping fields through new code, automatedintegration tools use tools like AI to develop templates that teams can then leverage to quickly un-silo their data.
Active metadata: metadata is context for information—things like its creation date, source, organizational labels/tags, and more. In the past, metadata was a static resource and generally not considered nearly as valuable as the data itself.
But with active metadata, there’s a massive opportunity to apply machine learning (ML) or other automated processing techniques against large real-time datasets to ensure data is interpreted properly. New metadata techniques also help collect and clean data, helping business users focus on what’s truly important.
Synthetic data: For organizations that want to fine-tune their ML training or analytics algorithms but don’t have enough (or the correct) data to work from, Synthetic data could be a massive opportunity.
