Blog: Jill Dyche: J'accuse! (Part Deux)

J'accuse! (Part Deux)

In which Jill continues her francophile diatribe, and Emile Zola spins in his you-know-what.

This blog continues the prior French-themed entry offering basic tips for launching a data management organization. (If you missed the first blog entry, I quoted both Emile Zola and Victor Hugo. There will be a test.*) You might recall that a few well-meaning blog readers had done some finger wagging at me for not addressing some of the basics of data management, particularly when it comes to formalizing the function organizationally, so I offered a few helpful tips to get people started. To review the tips so far:

1: Think local. Don’t boil the ocean.

2: Choose the right team. The right skill sets can make or break a nascent data management effort.

3: Keep current with business initiatives. Have one eye on upcoming business initiatives so that you can support them with data--and force managerment to argue why this isn't important.

Here are three additional tips to round out your data management

4: Know your company culture. It’s all well and good to form a data management team and start tackling dirty data and creating a platform for MDM, but if your company has a consensus-driven culture, you’re probably not doing yourself or your team any favors. Know how to make the case for data management and data integration. Often this means adhering to corporate standards for business case development or cost-benefit analysis. Even if you have a “let’s just build it” culture, you’d do well to make justification for data management more than a back-of-the-napkin exercise.

5: Plan for problems. Stuff happens, so risk management is always a good idea. Have contingency planning for unavailable platforms and people, know what to do if a tool doesn’t work right, have an escalation plan for wrong results and inexplicable data erros, and do “what if” scenario planning using your initial project plan as a baseline. Not only will this help you when the inevitable problems arise, it will demonstrate to management that your data management planning has been experienced-based and sober.

6: Think big. Often, we’re beholden to management that doesn’t understand what we do, to existing development methods that don’t cut the mustard, or to business users who question our value. We get saddled with data projects that might not be within our scope, but that developers or other teams couldn’t handle. One of my clients recently had to support a point-to-point data integration effort, when it was exactly these type of efforts the new organization was trying to eradicate altogether! In all these cases, data management teams should be continuing their “internal PR,” keeping upcoming projects on everyone’s radar and proselytizing the value of information.

The point is to define potential improvements, measure success, know the risks, and keep one eye on what’s next. It’s good advice for any IT project, but especially germane for data management.

* Yeah, but it could be worse. I could have quoted Flaubert. But then I’d get a new group of readers I hadn’t bargained for, and if you’ve read Flaubert, you know it’s simply not worth it.

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