Does your company need extra chief officers?

.@philsimon on title inflation.

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As the butter churns in Bangladesh

“Correlation does not imply causation” is a saying commonly heard in science and statistics emphasizing that a correlation between two variables does not necessarily imply that one variable causes the other. One example of this is the relationship between rain and umbrellas. People buy more umbrellas when it rains. This […]

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DaaS Is BaaS

.@philsimon on the proliferation of "as a service" terms.

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Big data, Hadoop, and the Internet of Things walk into a conference

The panel moderator looks out over the audience. It’s a large crowd. For the first time ever, Big Data, Hadoop, and the Internet of Things are appearing on stage together. The conversation has just begun, so let’s listen in for a minute. Big Data: “…and people have been trying to […]

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The celebrity of data: Big data goes big time in your organization

We were once oblivious to data. It was in the background. Just noise. The “byproduct” of applications that we used every day. A nuisance that screwed up every system migration or install. Now, we wonder, who’s seeing our data? How might they use it? We constantly check and review our […]

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The celebrity of data: Taking data to the mainstream

[ce·leb·ri·ty], noun. the state of being well known Media exposure, good or bad, is the surest way to gain celebrity.  Just ask any child actor gone bad in Hollywood. They know. Lately data has been getting more than its fifteen minutes of fame. And good or bad, I think it’s awesome. We’re […]

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Errors, lies, and big data

My previous post pondered the term disestimation, coined by Charles Seife in his book Proofiness: How You’re Being Fooled by the Numbers to warn us about understating or ignoring the uncertainties surrounding a number, mistaking it for a fact instead of the error-prone estimate that it really is. Sometimes this fact appears to […]

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In defense of the indefensible

.@philsimon on those who minimize the importance of data.

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Facebook and the myth of big data perfection

@philsimon says that perfection is elusive.

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The Chicken Man versus the Data Scientist

In my previous post Sisyphus didn’t need a fitness tracker, I recommended that you only collect, measure and analyze big data if it helps you make a better decision or change your actions. Unfortunately, it’s difficult to know ahead of time which data will meet that criteria. We often, therefore, collect, measure and analyze […]

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