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Democratizing Analytics With BI Architecture

If your organization is thinking about democratizing its analytics and BI protocols—that’s great! There are many reasons why this is a forward-thinking approach that can yield long-term success for an enterprise (more on that later). But what does it mean to democratize analytics and data?

Many who have been in the corporate world recently know data is one of the big talking points for how to improve operational efficiency. Data democratization in particular has started taking on a life of its own, as more and more organizations and thought leaders see the benefits of this approach. Despite its growing relevancy, many people are still unfamiliar with what data democratization really means.

At its core, democratizing data is all about getting analytics and BI tools into the hands of more people. In the past, this was discouraged for a few reasons:

  • There were fears that granting access to data would increase the likelihood of a breach, or sensitive corporate data being used in a damaging way.
  • Business intelligence tools used to be pretty complex for laymen users. There was a justifiable worry that allowing more people to play around with BI tools would lead to poor analysis, as well as potentially mess up backend functionality or ongoing projects.
  • Hierarchy is a very real thing within the corporate world. Eroding those division lines can be concerning to individuals and organizations that take precedence seriously.
  • People unknowingly working on the same thing independently could potentially lead to a reduction in productivity.

While there are worthwhile elements to all of these considerations, they don’t negate the underlying value of data democratization. Let’s look at why this concept is so important, as well as some of the ways enterprises are benefiting from it.

Why Is Democratizing Analytics Important?

Before diving into how BI architecture specifically can help organizations with data democratization, it’s a good idea to fully understand why this concept is so important. Here are a couple things to take in mind when evaluating the significance of democratizing analytics in general:

  • Expertise not required for deep insights – While there are benefits to specialization, when people become too hyper-focused, they lose perspective of the bigger picture. Maybe not everyone needs to have this point of view within an organization, but when too many are wearing blinders, it can lead to a lack of vision and care. BI tools that have an architecture to accommodate data democracy allow more people to use analytics to improve business processes. This not only improves the way an organization operates, but also gets more people on board with bigger-picture ideas.
  • Save everyone’s time – Being able to get insights faster isn’t just good for marketers running an ad campaign, or sales managers looking for cyclical opportunities. It’s also a huge plus for individuals on the data team who won’t have to field every query, and can instead dedicate time to more advanced projects.

 How Can BI Architecture Facilitate Data Democratization?

Now that you have a feel for what data democratization is and why it’s important, it’s time to consider how BI architecture can be used to further these practices. There’s undoubtedly a connection between platforms powered by artificial intelligence and the ability to provide democratizing analytics.

Simply put, the capabilities of AI-powered analytics architectures are far superior to their predecessors. It’s possible to search through massive sets of data in almost no time. But even more important than that, insights have largely been automated for certain functions. Users can take advantage of features like relational search, which allows for queries to be done by simply asking questions and receiving instant results. Modern advanced analytics can sometimes even suggest follow-up queries based on previous searches. This can lead to insights that could have been dormant forever.

BI architecture is important to the functionality of tools. Those who want to promote data democracy within their organization should look at this carefully before opting for any products.