In the past, big data discussions centered mostly on storage and organization followed by debates on the best way to do analysis in real-time or otherwise. This year the buzz centers on predictive analytics. Of, course we’ve been talking about this for a year or so now but really, it was just talk. Hardly anyone actually used predictive analytics but they pontificated often as if they did.
Presumably most folks are ready to get on with the deed now, so sayeth industry observers anyway. Still, for all we know, most are still just shooting the breeze around the water cooler and behind microphones on stage. But if you happen to be one of those first adopter types poised to sign the adoption papers, there is some good advice out there to consider.
Ben Kerschberg, a contributor at Forbes and founder of BKC3 Consulting, penned a helpful post recently. For the sake of efficiency, I’ll recap his suggested five steps to mastering big data and predictive analytics this year–but I highly recommend you read the full post for all the relevant details.
1. Infer, infer, infer
2. Empower a C-Level data and predictive analytics champion
3. Assess and modify your supply chain in a multidimensional global context
4. Give your data time-critical situational awareness
5. Rely on a core platform that creates derivative intelligence and knowledge in real time
To that I would add another step: layer data from multiple sources. You need variety in data and inputs to derive an accurate answer.
So how about it? Are you or someone you know working on using predictive analytics this year–or do you think everyone is pretty much still mulling it over? Leave a comment on your observations below please.
By: Pam Baker, editor, FierceBigData
Originally published at http://www.fiercebigdata.com
Excellent breakdown! At PoshanTracker real‑time nutrition tracking platform, we leverage real-time data dashboards to monitor maternal & child health outcomes—much like your steps for mastering data workflows to drive actionable results.