The name augmented analytics doesn’t really explain the full meaning behind the concept. While it’s obvious the analytics process is being augmented or altered by something, it’s not entirely clear what that is or how that affects the greater picture.
According to research firm Gartner, augmented analytics is “the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms.” So, considering the augmented element is the synthesis of analytics with AI and machine learning, it’s reasonable to expect these platforms and tools to have some powerful capabilities.
This definition, however, only scratches the surface of what augmented analytics is to enterprises today. Here are a few things to consider when evaluating the merits of these technologies.
Why Is Augmented Analytics Important for Enterprises?
If you’ve been living on planet Earth for the past decade, chances are you’ve heard someone talk about how artificial intelligence is set to change the world. Even if they were wearing a tin foil hat and talking about the end of days, you could probably pick up on the fact that AI is an essential piece of modern technology.
This is absolutely the case when it comes to emerging business intelligence platforms, which are increasingly leveraging the power of AI to analyze data more effectively. To be sure, there are already many ways AI is being used to improve analytics platforms. These advancements are leading to greater insights, and allowing them to come faster and from more places within an organization.
While this might sound like something out of science fiction, it’s not. It’s happening right now. And that’s all the more reason for enterprises to take it augmented analytics seriously—not just to improve internal operations, but to keep up with the competition.
How Is Augmented Analytics Used?
Now that you have a general understanding of augmented analytics and some of the broader ideas covered by its conceptual umbrella, it’s time to look more concretely at utilization. Analytics platforms such as ThoughtSpot have successfully integrated these technologies in order to provide more comprehensive data capabilities. Here are some of the top benefits offered by augmented analytics:
- Self-service analytics – There’s no denying the idea of self-service analytics is a major theme in the corporate world today. This is because self-service analytics totally turns the old way of analyzing data on its head. Instead of every query having to be sent to the analytics team, individuals can find insights on their own, thanks to AI-powered data tools. Relational search is one form of self-service analytics that utilizes natural language processing. Essentially, users can communicate with the platform, and gain actionable insights, through simple text or verbal queues. Not only does this vastly reduce the likelihood of bottlenecks from too many people requesting analysis at once, it cuts decision-making down from days or weeks to hours or minutes. That’s a remarkable power that’s only possible because of augmented analytics.
- Build a data-driven culture – Clearly, there are immediate operational benefits to adopting augmented analytics. These tools simply have more capabilities than those that have come before them. But there are also some longer-term reasons to opt for augmented analytics. Building a data-driven culture is one of the most compelling reasons. When your organization runs on a data-first mentality, it then benefits from a virtuous cycle of ever-greater reliance on hard facts over soft feelings. Over the long term, this can create massive advantages for firms that do this well.
There’s no doubt AI and machine learning are essential pieces of today’s technological landscape. This is absolutely apparent when it comes to augmented analytics, where they’re changing the definition of what’s possible.