Recently Read: March 22, 2010

Recently Read is an OCDQ regular segment. Each entry provides links to blog posts, articles, books, and other material I found interesting enough to share. Please note “recently read” is literal – therefore what I share wasn't necessarily recently published.

Data Quality

For simplicity, “Data Quality” also includes Data Governance, Master Data Management, and Business Intelligence.

The Data Quality Herald Magazine – Dylan Jones, the founder and community manager of Data Quality Pro, recently released the first edition of a unique new magazine focused squarely on the needs of the data quality community.

Defining Master Data for Your Organization – Loraine Lawson recaps a recent David Loshin MDM vendor panel discussion, and looks at both the simple answer and the complex, but more useful, answer to the question “what is master data?”

What is Data Quality anyway? – Henrik Liliendahl Sørensen asks two excellent questions in this blog post (which also received great comments): “is data quality an independent discipline?” and “is data quality an independent technology?”

Business logic – Peter Thomas provides a hilarious adapted comic strip.

Police Untelligence – from IQTrainwrecks.com, which is provided by the IAIDQ, read the story about the home of an elderly Brooklyn couple that has been raided by the New York City Police Department 50 times over the last 4 years.

Julian Schwarzenbach on Implementation Challenges – in his Technology Today podcast, Phil Simon discusses implementation challenges with Julian Schwarzenbach, including the relationship between data quality and technology.

Metadata and 3-D Glasses – David Loshin explains the data governance, data stewardship, and metadata/harmonization albatross hanging around the neck of the common question “what is the definition of ‘customer’?”

No Enterprise wide Data Model – Ken O’Connor continues his excellent series about common enterprise wide data governance issues with this entry about the impact of not having an enterprise wide data model.

Putting data on the web – Rich Murnane shares an excellent recent TED video by Tim Berners-Lee showing some of the benefits of shared data on the web.

How Are You Creating a Pull for Data Quality in Your Organization? – Dylan Jones provides two brief case studies contrasting the “push” and “pull” approaches to getting the organization engaged in a data quality improvement initiative.

Building your Data Governance Board – Marty Moseley continues his overview of agile data governance by discussing how you select a data governance board, and how you establish data governance priorities.

Staring at the Lights: Your Data Warehouse Isn’t a Commodity – Evan Levy explains how far too many data warehouse development teams have completely lost sight of their success metrics—and have become entirely focused on loading data.

The Change Paradox – Carol Newcomb examines the “change is good, but change is bad” paradox often encountered in consulting when recommended new technology or new methodology conflicts with your client's corporate culture.

Data Quality Non-Believers – Phil Simon takes on the data quality non-believers making “dataless decisions” by relying on gut instincts to explain such things as customer churn, employee turnover, and intelligent spending of corporate funds.

Data Cleansing to Achieve Information Quality – Jackie Roberts raises some interesting questions regarding the efforts needed to cleanse data though multiple stages of analytics and processes to achieve appropriate information quality.

Data Quality Principles within the PMO – Phil Wright provides a list of six excellent principles that must be met in order to help embed a culture of data quality, data assurance, and data governance within each new project.

Reduce Business Intelligence cost through better data migration – James Standen examines the challenges of cost control on business intelligence initiatives—and as usual, his blog post includes some great satirical cartoons.

Is computer analysis accurate? – Julian Schwarzenbach considers the accuracy of computer analysis in decision making, especially automated decision making that attempts to mimic human logic, intuition, and insight.

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