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Dec 4 2024
Through the Looking Glass: What Does Data Quality Mean for Unstructured Data?
I go to data conferences. Frequently. Almost always right here in NYC. We have lots of data conferences here. Over the years, I’ve seen a trend — more and more emphasis on AI.   I’ve taken to asking a question at these conferences: What does data quality mean for unstructured data? This is my version of […]


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Author: Randall Gordon

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Nov 20 2024
Data Speaks for Itself: Data Validation – Data Accuracy Imposter or Assistant?
In my last article, “The Shift from Syntactic to Semantic Data Curation and What It Means for Data Quality” published in the August 2024 issue of this newsletter, I argued how the adoption of generative AI will change the focus and scope of data quality management (DQM). Because data quality is measured in the degree […]


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Author: Dr. John Talburt

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Nov 20 2024
The Art of Lean Governance: A Systems Thinking Approach to Data Governance
A systems thinking approach to process control and optimization demands continual data quality feedback loops. Moving the quality checks upstream to the source system provides the most extensive control coverage. Data quality approaches not utilizing these loops will fail to achieve the desired results, often worsening the problem.  Data Governance is about gaining trust and […]


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Author: Steve Zagoudis

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May 1 2024
Data Speaks for Itself: Is Metadata Data?
Well, of course, metadata is data. Our standard definition explicitly says that metadata is data describing other data. So why would I even ask this question in the article title? The reason I ask it is because we seem to think about and manage metadata as somehow different than “normal data” such as business operations […]


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Author: Dr. John Talburt

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Feb 7 2024
Data Speaks for Itself: Is AI the Cure for Data Curation?
By now, it is clear to everyone that AI, especially generative AI, is the only topic you’re allowed to write about. It seems to have impacted every area of information technology, so, I will try my best to do my part. However, when it comes to data curation and data quality management, there seems to […]


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Author: Dr. John Talburt

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Nov 15 2023
Data Speaks for Itself: Data Love and Data Limerence
Now that “data” is finally having its day, data topics are blooming like jonquils in March. Data management, data governance, data literacy, data strategy, data analytics, data engineering, data mesh, data fabric, data literacy, and don’t forget data littering. In keeping with this theme, I’d like to propose a couple of new data topics not […]


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Author: Dr. John Talburt

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Nov 15 2023
Eyes on Data: The Right Foundation for Trusted Data and Analytics
Trust. Trust is defined as the assured reliance or belief on the character, ability, strength, or truth of someone or something (Webster’s Dictionary). It’s a term we use often to describe how we feel about the people, the institutions, and the things around us. But I would argue that the term “trust” was used differently […]


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Author: EDM Council

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Oct 4 2023
Data Professional Introspective: Accelerating Enterprise Data Quality
My recent columns have focused on actionable initiatives that can both deliver business value, providing a tangible achievement, and raise the profile of the data management organization data management organization (DMO).(For more on the DMO, a plug-and-play initial organization was proposed in an earlier TDAN column, “Coming in from the Cold.”) In that light, let’s […]


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Author: Melanie Mecca

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Aug 16 2023
Data Speaks for Itself: Placing Data Governance in Context
Yes, let’s talk about data governance, that thing we love to hate. I just attended the 17th Annual Chief Data Officer and Information Quality Symposium in July, and there, I heard many creative suggestions for renaming data governance. Calling it data enablement, data trust, data utilization, and many other names to try and avoid the […]


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Author: Dr. John Talburt

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May 17 2023
Data Speaks for Itself: The Challenge of Data Consistency
Data quality management (DQM) has advanced considerably over the years. The full extent of the problem was first recognized during the data warehouse movement in the 1980s. Out of this, dimensional frameworks were developed for expressing data quality requirements and the development of DQM models such as the ISO 8000 Part 61: Data quality management: […]


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Author: Dr. John Talburt

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