Written by Sam Osborn |
A major focus for Business Intelligence (BI), data and analytics vendors has been delivering tools that are ‘self-service.’ These solutions are based on the premise that with just a few easy clicks, users of all skill levels (regardless of their ability with data and analytics) can quickly sift through volumes of data to make radically better business decisions. But there’s a big problem; as the flood of data that businesses generate as part of their normal operations continues to increase exponentially, the ability of current tools to provide clear insights is rapidly degrading. And expecting everyone in the organization to become data scientists and business experts, in addition to performing their core business responsibilities, is simply unsustainable.
"By 2018, most business users will have access to self-service analytical tools,” said Anne Moxie, a senior analyst at Nucleus Research. “But the fact remains that there’s too much data for the average business user to know where to start.”
The fundamental issue is that even with these ‘self-service’ tools, most business users lack the analytical skill to reliably identify the intricate patterns in data that could be important information, or nothing at all. And more importantly, they shouldn’t have to.
In a recent article in Data Center Knowledge, GoodData CEO Roman Stanek argues that radical advances in computing power, predictive analytics, machine learning and artificial intelligence have opened the door to a new generation of analytic tools. If implemented properly, these systems can finally make good on the promise of Big Data by instantaneously pulling actionable insights from complex data sets and automatically surfacing them as recommended business actions, where and when they are needed most.
This isn’t some far off science fiction future. Machines are already getting better at extracting insights from complex data than humans are, and these cognitive abilities will only improve. Rather than continuing to pour money into tools that require employees to spend their time manually analyzing data and making mundane decisions, business organizations should be investing in next-generation systems that automate the bulk of these processes and allow their talent to focus on the strategic problems that really move the needle.
To learn more about the intersection of artificial intelligence and business intelligence, check out Roman Stanek’s article in Data Center Knowledge.
Download the Nucleus Research GoodData Guidebook to learn more about the next generation of predictive analytics and Smart Business Applications.
Written by Sam Osborn |