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Jill Dyché

There you are! What took you so long? This is my blog and it's about YOU.

Yes, you. Or at least it's about your company. Or people you work with in your company. Or people at other companies that are a lot like you. Or people at other companies that you'd rather not resemble at all. Or it's about your competitors and what they're doing, and whether you're doing it better. You get the idea. There's a swarm of swamis, shrinks, and gurus out there already, but I'm just a consultant who works with lots of clients, and the dirty little secret - shhh! - is my clients share a lot of the same challenges around data management, data governance, and data integration. Many of their stories are universal, and that's where you come in.

I'm hoping you'll pour a cup of tea (if this were another Web site, it would be a tumbler of single-malt, but never mind), open the blog, read a little bit and go, "Jeez, that sounds just like me." Or not. Either way, welcome on in. It really is all about you.

About the author >

Jill is a partner co-founder of Baseline Consulting, a technology and management consulting firm specializing in data integration and business analytics. Jill is the author of three acclaimed business books, the latest of which is Customer Data Integration: Reaching a Single Version of the Truth, co-authored with Evan Levy. Her blog, Inside the Biz, focuses on the business value of IT.

Editor's Note: More articles and resources are available in Jill's BeyeNETWORK Expert Channel. Be sure to visit today!

August 2010 Archives

DQeBook-400

You may have noticed we've slowed down our "In The Field" blog entries, but it's for a good reason. Last week, Baseline launched its latest e-book, co-authored by Baseline consultants and frequent bloggers, Carol Newcomb and Caryn Maresic.

The Data Quality eBook is both a cautionary tale and a nuts-and-bolts toolkit for bringing a set of formalized data quality processes to your company. When the Central Health Alliance discovers just how costly bad data can be, the health care provider launches a data quality program that not only improves services—it can actually save lives. This e-book looks at data issues faced by companies across industries, and shows you how to apply a step-by-step process to prevent over-investment in untrustworthy data and drive business value in the bargain.

The book is currently available for download at Information-management.com.

And now, a brief excerpt from the book:


DQImprovementProcess-400

At Central Health Alliance—as with many companies—protracted explanations and guesswork cede to manual effort.   If there is a problem hidden in the data, an analyst will surely find it. The question is: how long will that take?

The problem with manual data exploration is that you’ve got a lot of data—probably a lot more than you know.   Data is captured, copied and transformed—it is everywhere in all shapes and forms.   When digging through the data, where do you start?   More importantly, where do you stop?   Unfocused and manual data   profiling might lead to interesting discoveries, but won’t get you a cohesive roadmap to better data quality. Moreover, it’s hardly scalable.

The right way to improve data quality is by focusing on four incremental steps:

Identify the Business Issue – Defining the business issue and its impact on business operations, strategic goals, or decision making maintains focus for the remainder of this process. The scope of the business issue should be well understood. You might identify several related business issues that have bad data as their core. Or you might have a number of overarching issues, as Central Health Alliance does.

Assess Conformance to Requirements – After your business issue is well understood, it is time to do a data quality assessment.   The assessment is a focused effort to determine where in the data lifecycle things go wrong.   Central Health Alliance knows its business issues and they are poised to kick off the data assessment.

Discover the Root Causes – After you’ve assessed your data quality issues, it is time to discover why these problems are occurring. What are the root causes? Is there a lack of consistent training for the people who key in data?   Is there some buggy code that is moving data around behind the scenes?   Maybe there is some confusion about what the data actually means?

Formalize Improvements – Once you know the ”what” and the ”why,” it is time for action.   Improving data quality is often a two-pronged effort—you’ve got to fix what’s wrong and you’ve got to put a monitoring system in place so that you will know when something goes awry in the future. By fixing the data problem at its source, you can not only prevent it from recurring, you can improve the quality of the data in upstream systems as well.

What are you waiting for? Go download the entire e-book today!


Posted August 26, 2010 6:00 AM
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