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Data democratization, the concept of empowering any employee to make data-driven decisions for their company regardless of skill set, was supposed to rival the Elysian Fields in its paradise-worthy promise of analytics.
It’s easy to see why this concept made such a big splash in the enterprise. Since the early 2000’s, companies have been amassing raw data, which has morphed into the $203 billion big data analytics market. But this data always lacked transparency. Housed in messy data architectures that led to siloed information, companies struggled to gain a singular big picture into what their data was telling them. IT departments were left to sort through data warehouse integrations and complex extract, transform, load processes to try and create structure so any data analytics tool would single view into solving business problems.
But then came a new problem: A 1,000-plus-person company couldn’t have every employee ask IT every time they had an analytics-worthy question to solve. This brought the emergence of self-service BI tools — dashboards that were supposed to present a clear picture of what was happening in the organization so each employee could be transformed into a citizen data scientist, capable of answering their own analytics-based questions and speeding time to insight without needing a tech-savvy middleman. However, a single source of truth became the mantra and static dashboards were all a snapshot in time.
The concept of giving everyone in the enterprise access to mass amounts of data to drive decisions is flawed as long as advanced analytics requires a lot of handholding. It’s difficult for business users to keep up with the changes in self-service analytics tools and predictive monitoring and also keep up with their traditional jobs. Employees don’t know how to interpret data from cumbersome dashboards and want automated, real-time decisions. Giving everyone in the enterprise access to as much data as possible became the equivalent of handing over a stockpile of ingredients with no recipe and expecting everyone to bake a delicious cake. Yes, they have all the pieces of information in their possession to accomplish their goals, but they didn’t have the training or knowledge to drive the best results. And it shows.
It turns out less than 20 percent of these citizen data scientists use advanced and predictive analytics tools consistently. Only three-quarters of companies committed to big data analytics initiatives report that their revenue or cost improvements from the effort have been less than 1 percent. With self-service analytics and visualization tools turning into shelfware, what can save the state of big data analytics in the enterprise?
Industry momentum points to the fact that 2018 will be the year that embedded analytics coupled with machine learning will begin to replace self-service analytics.
How AI Can Help
Embedded analytics augmented by AI are going to drive down the manpower it takes in the enterprise to manage and analyze data. And over the past year, narrow AI — or AI applications that are designed to perform one task — has proven it’s more than capable of handling offloading some of data scientists’ workloads.
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Source: ITProPortal
