Monthly Archives: August 2012

Decision Services: IBM Tackles the Full Spectrum of Decision Management

Originally posted August 31, 2012) When James Taylor and I wrote the book “Smart (Enough) Systems: How to Gain Competitive Advantage by Automating Hidden Decisions,” we focused mainly on those kinds of decisions that are managed by a “decision service.” … Continue reading

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The Fallacy of the Data Scientist Shortage

There is no question that the USA (in fact, most of the world) would be well-served with more quantitatively capable people to work in business and government. However, the current hysteria over the shortage of data scientists is overblown. To … Continue reading

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YABADaS: Yet Another Blog About Data Science

In a comment to Dan Woods’ article “Amazon’s John Rauser on What Is a Data Scientist?,” (http://www.forbes.com/sites/danwoods/2011/10/07/amazons-john-rauser-on-w… ) “nyhacker” commented: “So far there is a standard obstacle to such work: Business is still organized as it was in Henry Ford’s day … Continue reading

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Data Scientist Title Is a Convention

Daniel Tunkelang is the Chief Data Scientist at LinkedIn and disagrees with my definition of a data scientist, presumably from my blog, “What Is a Data Scientist (and What Isn’t?”). I said that, “the term ‘Data Scientist’ is an over-reaching title.” … Continue reading

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What Is a Data Scientist (And What Isn’t)?

The perception among organizations over the past five years is that more quantitative methods, with or without Big Data, are critical to success. The problem is that most commercial organizations have little to no depth in these disciplines. On the … Continue reading

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Analytics ROI: Not About Saving Time

We hear a lot these days about how analytics can accelerate a calculation that used to take ten hours and do it in six minutes. For me, this is a very poor argument for analytics. After all, is an analyst … Continue reading

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What Will We Call Big Data in 2015?

 It is pretty much agreed now that the “big” in big data is a relative term. If the volumes of accessible, usable, actionable data continue to grow at current rates, then big data will be big for years to come. … Continue reading

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