Author: Bob Lambert
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Five Thoughts On Data Management Maturity
Recently I’ve had the opportunity to dig deeply into the CMMI Data Management Maturity model. Since its release, the DMM model has emerged as the de facto standard data management maturity framework (I’ve listed other frameworks at the end of this post). I’m deeply impressed by the completeness and polish of the DMM model as…
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No More Enterprise Data Sinks – An Agile Data Warehousing Manifesto
Over the past year I’ve reviewed what seem like countless plans for enterprise data warehouses. The plans address real problems in the organizations involved: the organization needs better data to recognize trends and react faster to opportunities and challenges; business measures and analyses are unavailable because data in source systems is inconsistent, incomplete, erroneous, or…
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Assumptions: A Key to Technical Leadership
There’s an unfortunate and rather rude saying about assumptions that I’ve found popular among IT folks I’ve worked with. I say unfortunate because, to me, assumptions that are recognized early and handled the right way are a key to successful projects. Technical players who use assumptions well can help set projects on the right path…
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Manage DATA, People, Process, and Technology
A quick Google search seems to reveal if you manage People, Process, and Technology you’ve got everything covered. That’s simply not the case. Data is separate and distinct from the things it describes — namely people, processes, and technologies — and organizations must separately and intentionally manage it. The data management message seems a tough…
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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 correcting data after it is already in the system. This makes sense in a data science or warehousing context, which is often where quality problems surface. To quote William…
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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 organizations’ data management cycles, I’d venture to say that all have a big chunk of big data, NoSQL, unstructured data, and more in their five-year…
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What is Big Data Creativity and How Do You Get It?
In a recent Smart Data Collective post, Bernard Marr cites creativity as a top big data skill, but what is creativity? His point is, since big data applications are often off the beaten IT path, big data professionals must solve “problems that companies don’t even know they have – as their insights highlight bottlenecks or inefficiencies in…
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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 below, please comment here or shoot me an email if you have comments or questions. BusinessEndOfDataModeling20141030
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Get Business Requirements Right by Resolving Many-to-Manys
Logical data modeling is one of my tools of choice in business analysis and requirements definition. That’s not particularly unusual – the BABOK (Business Analysis Body of Knowledge) recognizes the Entity-Relationship Diagram (ERD) as a business analysis tool, and for many organizations it’s a non-optional part of requirements document templates. In practice, however, data models…
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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 field (“Metadata Repositories,” TDWI Conference 1995). He described and portrayed in graphics three types of overloaded data. Almost 20 years later, overloaded data…