One Great Reason (+ 8 Good Ones) Why CIOs Should Invest in Data Integration

Loraine Lawson
Slide Show

Drivers and Challenges of Enterprise Integration Revealed

Why would a CIO ever support a data integration team, develop a data integration architecture or even pay for a data integration platform? Haven’t developers just “handled” that stuff for years?

For many small and mid-size organizations, it’s a fair question. After all, data integration is often a to-do item on a developer’s project list. It gets done, and that’s it — why invest any more time or money on it?

Here’s one very simple reason: Because your data integration needs are growing, fast.

For master data alone, the number of potential interfaces between applications “is an exponential function of the number of applications,” according to April Reeve, a strategic data consultant at EMC Consulting and author of the recently released, “Managing Data in Motion: Data Integration Best Practice Techniques and Technologies.”

“Thus, an organization with one thousand applications could have as many as half a million interfaces, if all applications had to talk to all others,” Reeves writes in a recent post. “By using hubs of data, an organization brings the potential number of interfaces down to be just a linear function of the number of applications.”

That’s just one of the reasons why data integration is becoming more of an issue for organizations of all sizes. In all, Reeves lists eight drivers for investing in data integration solution:

1.     Supporting data conversion.


2.     Managing the complexity of data interfaces created by data hubs, such as MDM and Data Warehouses.

3.     Integrating vendor packages with an organization’s own application portfolio. I really like her point her that every vendor package comes with it’s own master data, which then creates more data integration challenges.

4.     Sharing data among applications and organizations.

5.     Archiving data.

6.     Leveraging external data.

7.     Integrating structured and unstructured data.

8.     Support operational intelligence and management decision support.

She explains each of these points clearly and concisely. In fact, more business-minded readers should have no problem following this post, which even defines “hub and spoke” and niche terms like “canonical model.”

Even those who are otherwise unconvinced by hand-coding horror stories will have a hard time ignoring Reeve’s case for investing in data integration as a discipline and technology.



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