Category: Data Management
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Data Quality, Evolved
Data quality doesn’t have to be a train wreck. Increased regulatory scrutiny, NoSQL performance gains, and the needs of data scientists are quietly changing views and approaches toward data quality. The result: a pathway to optimism and data quality improvement. Here’s how you can get on the new and improved data quality train in each of those…
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A New Direction for Data at #EDW17
Obviously, data management is important. Unfortunately, it is not prioritized in most organizations. Those that effectively manage data perform far better than organizations that don’t. Everyone who needs data to do his/her job must drive change to improve data management. That was the theme of the recent Enterprise Data World (EDWorld) conference this week. This year’s EDWorld event might be the…
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No Silver BI Bullet: Tableau Edition (It’s a good thing!)
For complex work, a very simple app requires a very smart user. That point was driven home to me in Tableau Fundamentals class this week. I don’t see that as bad news at all. Not so long ago I wrote a piece that attempted to inject a bit of reality into the claims then made…
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Levels of Trust in Data Governance: It’s Not All or Nothing
The term “trust” implies absolutes, and that’s a good thing for relationships and art. However, in the business of data management, framing trust in data in true or false terms puts data governance at odds with good practice. A more nuanced view that recognizes the usefulness of not-fully-trusted data can bring vitality and relevance to…
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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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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…