The great data science hope: Machine learning can cure your terrible data hygiene

Will there ever be a technology that can fix decades of poor data hygiene? Probably not, but that isn't going to stop technology vendors from trying. The good news: Machine learning may come closest to saving your data management hide.

Data hygiene isn't easy. You can't hire enough interns to even come close to rectifying past mistakes. The reality is enterprises haven't been creating data dictionaries, meta data and clean information for years. Sure, this data hygiene effort may have improved a bit, but let's get real: Humans aren't up for the job and never have been. ZDNet's Andrew Brust put it succinctly: Humans aren't meticulous enough. And without clean data, a data scientist can't create algorithms or a model for analytics.

Luckily, technology vendors have a magic elixir to sell you...again. The latest concept is to create an abstraction layer that can manage your data, bring analytics to the masses and use machine learning to make predictions and create business value. And the grand setup for this analytics nirvana is to use machine learning to do all the work that enterprises have neglected.

I know you've heard this before. The last magic box was the data lake where you'd throw in all of your information--structured and unstructured--and then use a Hadoop cluster and a few other technologies to make sense of it all. Before big data, the data warehouse was going to give you insights and solve all your problems along with business intelligence and enterprise resource planning. But without data hygiene in the first place enterprises replicated a familiar, but failed strategy: Poop in. Poop out. And you wouldn't want to make your in-demand data scientists deal with poo.

TechRepublic: Cheat sheet: How to become a data scientist | Job description: Data scientist (Tech Pro Research)

IBM's Seth Dobrin, chief data officer for IBM, said "the idea that you could use a data lake and Hadoop (MapReduce) instance where you can dump all this crap in is a mistake.