Slideshow 7 Data Management Ah-Ha Moments

Published
  • January 06 2012, 9:33am EST

The Data Avalanche Is Coming – Are You Ready?

“This is not a problem that technology alone can resolve. For most companies it will require a seismic cultural shift in how data is perceived and managed by every person in the organization. Systems and IT infrastructure can evolve endlessly, but you can’t buy a culture of data quality. It takes time – sometimes many years – to create a vision, sense of urgency and an ability to adapt.” – Dylan Jones

Oughtn’t You Audit

“Ensuring that the data being used to make critical business decisions is as reliable and accurate as possible is why data quality is so vitally important to the success of your organization. That statement would probably make most people throughout your organization nod their head in ... agreement. So why do these very same people: never check that data is complete and accurate before sharing it with others; never seek to understand what the data means within a business context; never consider the costs and other risks associated with poor data quality; and never verify the data that they are using to make critical business decisions?” – Jim Harris

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A Master Data Dilemma

“Our presumption of a ‘master’ repository implies that for each unique real world entity, there can be one and only one representation in the repository. In turn, that representation contains at least those data attributes required to uniquely distinguish any entity from every other entity, and that must include all identifying attributes and their corresponding values. That means that for the customer domain, there is a record containing the identifying attributes for a customer; for an employee, there is a record containing the identifying attributes for an employee. So what happens when an employee is also a customer?” – David Loshin

Data Prioritizing

“With respect to data, no organization is completely governed nor completely undisciplined. These states represent different ends of the same continuum. To be sure, some organizations manage their data much better than others, but even the former have their challenges. Here's some examples of what separates the good from the bad and the ugly: at a high level, recognizing the importance of data – and good data; employing the right software, internal and external resources to get the job done; proactively addressing would-be issues; getting out in front of actual issues; resolving the biggest issues first; circling back to pesky minor issues before they become much larger ones.” – Phil Simon

Are You Really Ready for Data Governance?

“[I]t’s easier to avoid sticking your neck out. And in some cultures (particularly in high-tech companies, but that’s another blog post altogether) wholesale changes to policies and processes can actually begin from the bottom-up. It’s not only less disruptive, it demonstrates value to incent widespread adoption. Bottom-up data governance is a legitimate and proven approach. But at the end of the day, your strategy needs to include a plan to broaden governance beyond the initial domain. After all if you’re not sharing data across organizations and business processes you don’t need data governance.” – Jill Dyché

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Mergers and Acquisitions

“[U]pon announcement of [another] merger and acquisition, our teams would do a technology audit of the shop we were buying and we data geeks were always interested in grabbing all types of data from the shop we were buying. Once we had a handle on where their data was stored, we began working with their staff to pull the data out of those systems and start the integration projects on our side. Very rarely were there times when the shop being purchased had 'single sources of the truth' for their primary types of data; most times we’d end up having to pull extracts from 'this' system and 'that' system and figure out what to do with their data.” – Rich Murnane

PMI and the Master Data Project

“The similarities between how PMI thinks you should manage a project, and the way we would do our MDM project are very similar. In fact, PMI includes every task we would do in some part of their project management framework. Because MDM is so very data and integration intensive the tasks and processes can be very complex, and become 80 percent of the project.” – Joyce Norris-Montanari

More Discussion on Data Management

These data management thoughts and more are covered in the new book from DataFlux's Data Roundtable, “101 Lightbulb Moments in Data Management.” Click here to access a copy. All images were used with permission from ThinkStock.