Can Your Data Systems Keep Pace with the Emerging Business Demands?

Three Big Problems Big Data Will Create in 2014

Discussions about IT and business alignment are almost taboo these days. I suppose people have heard too much about it in the past decade.

Yet, that’s exactly the kind of discussion data experts seem to be calling for when it comes to how IT manages data.

“Over the past year it is becoming increasingly clear that we have to stop thinking as data managers and start thinking as data designers,” writes Forrester analyst and data management expert Michele Goetz in a recent Information Management article. “What matters is what data drives for the business first and then design a data system around that. We need to educate ourselves on what the business does with the data.”

She’s not the only one who’s noticed that the old ways of data management need a business-savvy overhaul. Leo Eweani, co-founder of Ontology Systems, makes a similar argument about data integration in a recent TechRadar column. He points out that a “data tsunami” is coming, and with it, a huge demand for data analytics. The problem is that most organizations haven’t revamped integration technology to accommodate the data surge.

“The root causes of data integration’s troubles are two-fold: relational data management technology that is nearly half a century old and a dizzyingly rapid rise in the variety of data sources available to the enterprise, the volume of data they produce and the velocity at which they produce it,” Eweani writes. “This combination is like navigating a tidal wave in a kayak.”

Obviously, he’s trying to sell you on his company’s semantics-based solution. But the question still stands: Are your data systems, including how you handle data integration, up to the demands of modern analytics?

Goetz’s column lists some dos and don’ts that will get you started, but she actually wrote a second piece on Forrester’s site that I found more helpful. 

Goetz says organizations need a more flexible approach to data management, which focuses on:

• Context first
• Data governance “zones” rather than a top-down, one-size fits all approach that stifles innovation
• Speed and democracy
• Data performance management that allows you to provide a “tangible connection between data investment and business outcomes”