Use conceptual data modeling in requirements definition
I’ve often thought that conceptual data modeling was an underused tool in the arsenal available to requirements analysts, and in a recent conversation I found that many were surprised that it would be...
View ArticleGet an early start for on-time data modeling
I’m a data modeler, so I enjoyed Jonathon Geiger’s recent article entitled “Why Does Data Modeling Take So Long”. But why does he say it like it’s a bad thing? Mr. Geiger’s bottom line is exactly...
View ArticleAbstracting and recombining all the way to the bank
In the past I’ve never understood what people really mean they say “think outside the box” but Jim Harris, in a recent OCDQ blog post, helped me figure it out. Mr. Harris ends with this provocative...
View ArticleA QlikView QuickStart: first steps for learning QlikView desktop
QlikTech’s QlikView reporting and analysis tool is among a new class of Business Intelligence (BI) software tools. As Ben Harden reported in a recent blog post, BI vendors like SAP, Microsoft, and IBM...
View ArticleBig Data opportunities and NoSQL challenges
As a relational database professional I couldn’t help but feel like something would be lost with the emergence of the new Big Data/NoSQL database management systems (DBMS). After about two years of...
View ArticleSelected data modeling best practices
Recently I was in a conversation about data modeling standards. I confess that I’m not really the standards type. I understand the value of standards and especially how important it is to follow them...
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Lessons from the puppy poster
In some presentations, I assert that top-down data modeling should result in not only a business-consistent model but also a pretty well normalized model. One of the basic concepts behind normalization...
View ArticleSkills of the Data Architect
One common theme in recent tectonic shifts in information technology is data management. Analyzing customer responses may require combing through unstructured emails and tweets. Timely analysis of web...
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Data Design Matters
As important as it is, data modeling has always had a geeky, faintly impractical tinge to some. I’ve seen application development projects proceed with a suboptimal, “good enough”, model. The resulting...
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Relational DB Pros: The Times They Are A-Changin’
Recently I read a thoughtful post at the PASS Business Analytics Conference site discussing how different the world is now for database professionals. Author Chris Webb focuses on the data science side...
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Thoughts on Healthcare Data Quality
The well-publicized problems with healthcare.gov are disturbing, especially when we remember they might result in many continuing without health insurance. But it seemed a step in the right direction...
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To SQL or to NoSQL?
Recently there was a great post at Dzone recounting how one “tech savvy startup” moved away from its NoSQL database management system to a relational one. The writer, Matt Butcher, plays out the...
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A Field Guide to Overloaded Data
At the very first TDWI Conference, Duane Hufford described a phenomenon he called “embedded data”, now more commonly called “overloaded data”, where two or more concepts are stuffed into a single data...
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Lynchburg SQL Server User’s Group 10/30
Yesterday I had the pleasure of presenting “The Business End of Data Modeling” for the Lynchburg SQL Server User’s Group. It was a great time, thanks for having me out! I’ve linked the presentation...
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A Short List of Accessible Big Data Training Options
As you’ve read on this site and many others, the database world is well into a transition from a relational focus to a focus on non-relational tools. While the relational approach underpins most...
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GIGO: Data Quality Guidelines for Application Development
There’s consensus among data quality experts that, generally speaking data quality is pretty much bad (here, here, and here). Data quality approaches generally focus on profiling, managing, and...
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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...
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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...
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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...
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Data Quality Remains Poor Until Leadership Makes it a Priority
Recent studies report that data entry typos are the largest source of poor data quality (here and here). I suppose that might be true, but my experience says otherwise. From what I’ve seen, operational...
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