Dec 21, 2011 by Jim Harris in Data Governance, Data Integration, Data Management, Data Migration, Data Quality, Master Data Management
Welcome to the unofficial 2011 quarterly review that I have decided to perform on the Data Roundtable. In this four-part series, I will summarize my personal favorite blog posts published on this blog during each calendar quarter of this year.
Part 1 of this series covered blog posts from January, February, and March.
Part 2 of this series covered blog posts from April, May, and June.
Part 3 covers blog posts from July, August, and September.
Things That Don’t Work So Well – Not Addressing Data Quality Upfront — Joyce Norris-Montanari explains that data quality issues will eventually come and bite you in the ASCII.
South Sudan… — Rich Murnane explores the data management implications of adding a new entry to the list of existing countries in the world.
Entity Identification vs. Identity Resolution — David Loshin examines the challenges of differentiating entities sharing an identity.
Being Horizontally Vertical — Jim Harris teaches basic enterprise mathematics, explaining how, in an organization with five functional silos, 1 + 1 + 1 + 1 + 1 = 1 (not 5).
Signs of Hope — Thomas Redman shares signs of hope that the pace at which organizations are understanding the importance of data is growing.
The World Spins on Data — Phil Simon explains although good management, powerful technology, and a customer-friendly culture may make the world go round, that same world spins on data.
Leadership Lessons from Colby the Dog — Jill Dyché admits that everything she knows in business she learned from her dog.
Coupling Data Quality and Business Intelligence — Dylan Jones ponders how we can more closely couple data quality metrics directly with the information consumed.
Episode 12: Knights of the Data Roundtable — Julian Schwarzenbach explains his observations of generic data behaviors people exhibit when they interact with data, aka The Data Zoo.
Things That Don’t Work So Well – No Organizational Acceptance or Buy-In — Joyce Norris-Montanari explains the Rule of Three Gimmes.
Trouble with Data in Paradise… — Rich Murnane explains that any organization, even a wonderful neighborhood, can have serious data management issues.
The Persistence of Error — David Loshin ponders if we are really being proactive with data quality or just arranging to be reactive earlier in our data management processes.
Song of My Data — Jim Harris concludes Data as Literature Appreciation Month by channeling the poet Walt Whitman in an attempt to prove that poetry is data of the highest quality.
It’s All Unstructured — Thomas Redman shares the prediction of Professor John Talburt of the University of Arkansas at Little Rock, that, in time, all data will be unstructured.
On Babies, the Exposure Argument, and Frameworks — Phil Simon rants about how paying for expertise yields better results and, arguably more important, accountability.
Smart Stuff from the BI Summit — Jill Dyché summarizes what was discussed — and what was imbibed — at the 2011 Pacific Northwest BI Summit.
The Case for Continuous Data Quality Inspection — Dylan Jones explains why he dislikes the notion data quality should be built into systems, negating the need for regular data quality assessments.
Things That Don’t Work so Well – Cloudy with a Chance of DataBalls! — Joyce Norris-Montanari advises taking it slow and thinking about enterprise data integration as it pertains to the cloud.
Color Commentary… — Rich Murnane provides his color commentary on a job openings and labor turnover report from the U.S. Bureau of Labor Statistics.
Location, Location, Location – The Location Domain — David Loshin examines an often overlooked data domain. (I am not absolutely certain, but I think it might be . . . Location.)
“What is is the was of what shall be” — Jim Harris discusses why most predictions about the future often do not come true — and how predicting the future is about changing the present.
The Simple Elegance of the Customer-Supplier Model — Thomas Redman extols the data quality virtues of the customer-supplier model.
Practices Smractices — Phil Simon explains that clean, comprehensive, and accurate data is not solely a function of systems and applications.
Data Governance Death Sentences — Jill Dyché argues that the new crop of data governance clichés are all dressed up with nowhere to go.
The Secret to Long-Term Data Quality Survival? — Dylan Jones battles the arch enemy of every data quality initiative: Sponsor Churn. (Don’t worry, no tights or capes are involved.)
On behalf of all the contributors to the Data Roundtable, thank you for reading and commenting on the posts published on this blog throughout the year. Your readership is deeply appreciated.
Next week, Part 4 concludes this series with blog posts from October, November, and December.