Tag: Strategy
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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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Get the Big Picture: Effective High-Level Diagrams
I believe that early, effective big picture diagrams are key to application development project success. According to the old saw, no project succeeds without a catchy acronym. Maybe so, but I’d say no project succeeds without a good big picture diagram. The question: what constitutes a good one? To me good high-level diagrams have four key characteristics:…
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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,…
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A New Framework for Data Management?
I hold a strong prejudice that IT paradigms are useful for about 30 years. The PC was dominant from 1980 to 2010, “online” mainframe systems from 1970 to 2000, and so on. If that’s the case then time’s up for Bill Inmon’s data warehousing framework. So far no widely held pattern has emerged to help…
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What Driving Dogs Tell Us About Learning
Recently the BBC posted this video. On first view it is just funny, but watching those dogs learn to drive really reminded me of personal experiences with IT teams making big learning transitions. To represent those real situations let’s consider a fictional team of SQL developers facing the daunting task of deploying a functional Hadoop-based…
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Skills 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 interactions may demand a big data solution. Deployment of data visualization tools to users may dictate redesign of warehouses and marts. The data architect is a key…
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The gnarly, subtle-seeming data quality question
I’ve posted a couple of articles at my company’s blog site that reflect my view on data quality efforts: Yes, there is a business case for improving data quality, and I’ve got real business value examples. If you look for real money where you anecdotally know there are data quality problems, you’ll likely find it…
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Abstracting 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 line: “the bottom line is Google and Facebook have socialized data in order to capitalize data as a true corporate asset.” The post…
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Data quality and data governance lessons from national health care
Who would want to be a national health care administrator? Who would want the responsibility for managing health care and formulating health policy for tens or hundreds of millions of people? It seems obvious that such decisions would rely on quality data. A recent interview impressed upon me how much data managers can learn from a…
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Business requirements up front
“Our goals can only be reached through a vehicle of a plan, in which we must fervently believe, and upon which we must vigorously act. There is no other route to success.” – Pablo Picasso It is an old story: about 30% of IT application projects succeed, 45% are “challenged,” and the other quarter fail…