Data Scientists Don’t Scale
“Big data is about to get a big reality check. Our ongoing obsession with data and analytics technology, and our reverence for the rare data scientist who reigns supreme over this world, has disillusioned many of us. Executives are taking a hard look at their depleted budgets — drained by a mess of disparate tools they’ve acquired and elusive ‘big insights’ they’ve been promised — and are wondering: ‘Where is the return on this enormous investment?’
It’s not that we haven’t made significant strides in aggregating and organizing data, but the big data pipedream isn’t quite delivering on its promise. Despite massive investments in technology to store, analyze, report, and visualize data, employees are still spending untold hours interpreting analyses and manually reporting the results. To solve this problem and increase utilization of existing solutions, organizations are now contemplating even further investment, often in the form of $250,000 data scientists (if all of these tools we’ve purchased haven’t completely done the trick, surely this guy will!). However valuable these PhDs are, the organizations that have been lucky enough to secure these resources are realizing the limitations in human-powered data science: it’s simply not a scalable solution. The great irony is of course that we have more data and more ways to access that data than we’ve ever had; yet we know we’re only scratching the surface with these tools.
A few innovative executives understand this and have sought scalable, automated solutions that interpret data, unlock hidden insights, and then provide answers to ongoing business problems. Artificial intelligence (AI) is beginning to transform data and analysis into relevant plain English communication. AI is shortening employees’ data comprehension-to-action time through comprehensive, intuitive narratives.”