Category: Data Management
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Anonymize Data for Better Executive Analytics
Reading articles about data anonymization makes it clear that it is not an entirely effective security measure (here and here), but still part of a robust security capability, and required if your organization is affected by GDPR. (I use “anonymization” as a general term encompassing techniques that de-identify personal data within a given data set.) But there’s a positive…
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Toward an Analytics Code of Ethics
In data management and analytics, we often focus on correcting apparent inability and unwillingness on the part of business leaders to effectively gather and capitalize on data resources. With that perspective, we often see ethics as a side issue difficult to prioritize given the scale and persistence of our other challenges. At least that was…
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Data Integration Benefits? They’re Obvious.
“At least 84 percent of consumers across all industries say their experiences using digital tools and services fall short of expectations.”* That quote headed a recent article by David Roe on the role of data integration in digital workplace apps. However, the opening quote reflects the pervasive dearth of integrated data among the companies most of…
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Meaningful Requirements Start Successful Data Projects
To me, development projects fail or succeed in the first few weeks. Once a project starts off in the wrong direction, momentum and expectations tend to prevent a return to the proper path. With today’s wealth of database options each addressing exciting new possibilities, the right choice for the application’s data foundation plays a large…
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Start Data Quality Improvements with a New Definition
What is Data Quality anyway? If you are a data professional, I’m sure someone from outside our field has asked you that question, and if you’re like me you’ve fallen into the trap of answering in data-speak. To my listener, I’d guess that the experience was similar to having a customer service rep who has just…
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Sound Data Culture Enables Modern Data Architectures
Modern data architectures, by enabling data analytics insights, promise to drive order of magnitude value gains across many business sectors (here, here, and here). Not so long ago, big data presented a daunting challenge. Although tools were plentiful, we struggled to conceptualize the architecture and organization within which to capitalize on those tools. Now solid…
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Leader’s Data Manifesto Annual Review: “It’s About the Lopez Women”
A year ago I recounted proceedings from the 2017 EDW World conference, which included release of the Leader’s Data Manifesto (LDM). Last week’s EDW World 2018 served as a one-year status report on the Manifesto. The verdict: there’s still a long way to go, but speakers and attendees report dramatic progress and emergence of shared…
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The PDDQ Framework: Lean Data Quality for Patient Records
For most of us it may have slipped under the radar, but in December a groundbreaking Patient Demographic Data Quality framework was jointly released by a US government agency and the CMMI Institute. In response to findings that many “safety-related events were caused by or related to incorrect patient identification”, the Office of the National Coordinator…
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The Practical Metadata Business Case
Even now the business case for a metadata tool seems unclear and difficult to quantify, but it isn’t impossible. We in the data management business tend to devalue solutions that don’t clearly derive from a coherent top-level view. We seek applications defined from an enterprise architecture, database designs from an enterprise data model, and data…
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Reporting Database Design Guidelines: Dimensional Values and Strategies
I recently found myself in a series of conversations in which I needed to make a case for dimensional data modeling. The discussions involved a group of highly skilled data architects who were surely familiar with dimensional techniques but didn’t see them as the best solution in the case at hand. I thought it would…