Data Quality & Cloud-based services

April 23, 2011
By William Sharp

Software as a Service (SaaS) will help proliferate data quality solutions I agree with this assertion for a few reasons, not the least of which is the ease at which “front-end” data quality solutions will be included in the suite of services in a Service Oriented Architecture (SOA). In my opinion, data qualities true promise lies in a DQ service that can be integrated into any SOA. Thanks for...

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Data Quality ROI = Address Validation and Duplication Consolidation

April 22, 2011
By William Sharp
Data Quality ROI = Address Validation and Duplication Consolidation

  I have had conversations recently with fellow data quality gurus which centered around DQ ROI.  We all know how important it is to tie a DQ initiative to a return on the investment.  This is even more true of an initiative with long-term implementation objectives.  During the course of the conversation I pointed out that I believe DQ ROI is all about validating addresses and consolidating duplicates and there...

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Data Discovery. The first step toward data management.

April 20, 2011
By William Sharp

Introduction Recently on a data discovery project I observed something that I wanted to share.  Data discovery efforts, and the tools that support them, are well suited for those organizations who’ve had data explosive growth.  With this kind of growth the data landscape expands to the point where in-depth knowledge of data, and more importantly metadata, details becomes unobtainable.  This is where a product suite like Global IDs data transparency suite can enable...

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Data Quality Resource

April 20, 2011
By William Sharp
Data Quality Resource

  Recently a reader, Richard Ordowich, posted this resource in a comment so I thought I’d pass it along. The most comprehensive list I have seen is in the book; Managing Information Quality by Martin Eppler in which he lists 70 typical information quality criteria which was compiled from various other sources (and referenced). Thanks for taking the time to visit the weblog!William Sharpsharp@thedataqualitychronicle.org

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It’s a date!

April 18, 2011
By William Sharp
It’s a date!

I’ve started using the date related functions in the data quality developer tool. I’ve found some fun ways to implement them and wanted to share. Is_Date Before you use any date function you need to be sure you’re dealing with a date string. The Is_Date function, available in the Expression transform, is how you test a string for proper date format. The syntax is simple, Is_Date(Input Port). The result...

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Know Your Data – Data Profiling

April 17, 2011
By William Sharp

Having data issues is a lot like having any other issue; you just want to get it fixed and move on with life.  I talk with a lot of business users who feel this way.  They have stories for me when I arrive.  From 62 different states codes to 5 different versions of one of their most high profile customers, they tell me these stories and I patiently listen....

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Data Quality Polls: Troubled domains and what to fix

March 24, 2011
By William Sharp
Data Quality Polls: Troubled domains and what to fix

As expected, customer data quality remains at the top of list with regard to having the most issues. Ironically, this domain has been at the forefront of the data quality industry since its inception. One reason for the proliferation of concerns about customer data quality could be its direct link to revenue generation. Whatever the reason, this poll seems to indicate that services built around the improvement of customer...

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Data Quality Basic Training

February 2, 2011
By William Sharp

Recently a reader asked me if I had any posts on “data quality basics”.  Turns out, I didn’t.  So I’ve decided to put together a series of posts that covers what I feel are the basic essentials to a data quality program. The many sides of data quality It is generally accepted in the data quality world that there are  seven categories by which data quality can be analyzed.  These include the following: Conformity...

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A Data Quality Checklist

January 9, 2011
By William Sharp

As a follow up to my previous post, I have compiled a checklist that can used to manage the effort of preparing for a data quality engagement. Form a data quality mission statement Define the systems and data in-scope Define the data quality environment Extract the data and land it to the data quality environment Form a data quality mission statement This statement should include the nature of the...

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Get ready ’cause here I come: A Tale of Data Quality Preparedness

January 8, 2011
By William Sharp

Normally I use this blog to tell stories of a very technical nature.  However, a recent experience has led me in a very different direction.  I want to talk informally about what it takes to prepare for a data quality project.  First and foremost, there needs to be a catalyst for starting a data quality project.  Typically this usually takes the form of anecdotal tales of woe from data consumers...

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Putting your best foot forward: Data Quality Best Practices

December 22, 2010
By William Sharp

Data Quality Best Practices Enterprise data quality is a challenging endeavor that can seem overwhelming at times. It requires coordination and cooperation across the technology and business domains along with a clear understanding of the desired outcome. The following are some guiding principles that can bring order to the chaos over this challenging effort.

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