Time to Think Broader on Integrating, Managing Data

Loraine Lawson
Slide Show

Analytics and the Path to Value

When it comes to business, information is clearly power.

I flipped through an IT Business Edge slideshow this week called "Analytics and the Path to Value," which covers the results of a global survey of 3,000 executives and analysts conducted by IBM and the MIT Sloan Management Review. The survey determined that the top performing companies were five times more likely to be making use of analytics. That's a pretty significant sign that information management, and business intelligence in particular, matter.


Earlier this week, I shared how companies want better business intelligence, but they're falling short when it comes to the foundational work that will get them there-including data integration. The survey had some revelations along those same lines, too. It turns out, data integration was the top answer given at 44 percent when companies were asked about their top data priority. But I have to wonder if that's all about technology, or if it's cultural. After all, when asked about the top barrier to analytics, 24 percent said their culture does not encourage the sharing of information, compared to 22 percent who cited concerns about data. These two things aren't necessarily mutually exclusive, but I also found it telling that the most mentioned barrier at 38 percent was "a lack of understanding on how to use analytics to improve business."


Maybe it's time to stop focusing on analytics, or BI, or even just data integration or data quality-or any one foundational problem-and take a broader look at an overall information management program. I know-just what you need: another enterprise-wide project. But if you think about it, it's where you're headed anyway, and you can get there by stops, starts and lessons learned the hard way, or you can be a bit more systematic about things.


These certainly shouldn't be solely IT's responsibility, but, again, the statistics aren't in your favor. Kalido recently surveyed organizations on data governance (there's another thing you'll need to consider) and found that, "despite the commonly expressed belief that data should be owned by the business, traditional IT organizations are accountable for data in nearly two-thirds (63 percent) of organizations."


It's unfair, but perhaps, as I shared earlier this week, it's changing. After all, as Ann All pointed out, even upper-level executives are starting to experience a pain point with poorly managed data.


But given that IT is likely to have to lead (or at least aggressively point the way to) this effort, you might want to check out a recent article, "How to Develop an Information Lifecycle Management Methodology." The article is written by Edwin D'Cruz, a principal at Princeton Data Solutions. Like all methodology approaches, it's heavy on questions and details that can seem a bit tedious, but, again, you're probably going to have to deal with these issues eventually anyway.


Cruz outlines some of the core components of any lifecycle management program:

  • Canonical models and design strategy
  • Metadata, master/reference data strategy
  • Data quality
  • Integration strategy
  • Analytics (BI)
  • Physical data management

The piece goes on to explain how you apply a new concept-analyze, design, acquire and manage or ADAM-to these areas.


If that approach sounds too formal for where you are now, you might want to check out the very clever, and more immediately useful, "A Framework to Map and Grow Data Strategy," which appeared in this month's Information Management electronic issue. It cleverly uses Maslow's Hierarchy of Needs as the basis for prioritizing enterprise data problems.

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