Self-Service Analytics are Not Sustainable

Roman Stanek is CEO at GoodData.

In the last five years, Business Intelligence (BI), data and analytics vendors have all focused on delivering tools that are "self-service," granting a typical business user the ability to approach data and analytics without a background in statistical analytics, BI, or data mining. These self-service solutions turned legacy BI on its head by offering a powerful promise: Users of all skill levels would be able to operate complex software with a few easy clicks to make radically better business decisions, freeing analysts and data scientists to focus on strategic work rather than reporting. This sounds Utopian, but there’s a problem: It’s not sustainable.

On the surface, data democratization sounds ideal. The amount of raw data that business of all types generate as part of their daily operations has grown exponentially, so the idea of giving everyone in the organization (not just the “Data Elite”) the ability to derive insights and value from this information is extremely attractive. But as the flood of data continues to increase, the ability of these tools to provide clear insights is rapidly degrading. And simply trying to cram more information into these tools will only compound the problem; as Emperor Joseph told Mozart, “there are only so many notes an ear can hear over the course of an evening.”

"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." said 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.”

The fundamental problem is that "self-service" tools require business users to spend more and more of their time digging through data, but an average employee can’t be expected to reliably self-identify the intricate patterns in that data that could be either an important early warning sign, or nothing at all. More importantly, they should not have to. Business users don’t need access to more raw information; they need tools that take the leg work out of data analysis and automatically surface them the insights they need so they can focus on doing their jobs better.

The solution to this problem lies in harnessing advances in machine learning and artificial intelligence to automate analysis and surface actionable insights where they’re needed most: at the point of work. Embedding these “Smart Business Applications” into the tools that business users are already using every day will increase the penetration of data and impact of these recommendations, without every employee in the organization needing hours of training to master BI tools.  

Imagine how powerful it would be if, rather than having to use a separate BI platform to self-perform complex data analysis, your employees were presented with simple, reliable and machine-accurate recommendations for the next business actions they should take directly inside the core business applications they use every day. If implemented correctly, this combination of embedding and machine-learning capabilities can deliver in-context automation, recommendations and insights that can truly deliver tangible results. These Smart Business Applications can automate many of the low-level decisions that business users face, freeing them up to focus on more strategic and impactful problems. The applications are limitless, but could be game changing for industries like:

With the current generation of self-service BI tools, we’re asking everyone in the organization to become data scientists and business experts. And from everything I’ve seen, that’s not reasonable, and it’s not happening. Organizations should offload the work of analyzing reports to a trusted analytics partner and focus on giving their people the tools they need to get back to work. Machines are getting better at extracting insights from complex data than humans are, and companies need to invest in BI solutions that take advantage of advances in machine learning, predictive analytics and artificial intelligence to truly deliver data in-context with clear insights and machine-accurate recommendations.

Opinions expressed in the article above do not necessarily reflect the opinions of Data Center Knowledge and Penton.

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