Tag: Data Management
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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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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…
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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 reasons under these main points: Our data is relational We need better querying We have access to better resources Summing up: “The bottom…
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DIY Data Dictionary: ODBC Reporting from the ERwin Metamodel
Application developers and business people accessing relational databases need data dictionaries in order to properly load or query a database. The data dictionary provides a source of information about the model for those without model access, including entity/table and attribute/column definitions, datatypes, primary keys, relationships among tables, and so on. The data dictionary also provides…
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Guiding Principles for Data Enrichment
The data integration process is traditionally thought of in three steps: extract, transform, and load (ETL). Putting aside the often-discussed order of their execution, “extract” is pulling data out of a source system, “transform” means validating the source data and converting it to the desired standard (e.g. yards to meters), and load means storing the…
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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 when recent a news report differentiated between “front end” and “back end” problems. The back end problems were data issues, like a married applicant with two…
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Data Management: So Easy a Caveman Can Do It?
I recently stumbled upon one of The Martin Agency’s hilarious Geico caveman ads and wondered, rather geekily, why they didn’t do one about data analysis. I think if a caveman suddenly arrived in the 2010s he or she would see parallels between his life and the activities of today’s knowledge worker. When I thought it through,…