Computers

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Handbook of Biochips

Handbook of Biochips

Integrated Circuits and Systems for Biology and Medicine
edition:Hardcover
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Digital Playgrounds

Digital Playgrounds

The Hidden Politics of Children's Online Play Spaces, Virtual Worlds, and Connected Games
edition:eBook
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The Platform Economy and the Smart City

The Platform Economy and the Smart City

Technology and the Transformation of Urban Policy
edition:Paperback
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Coding Democracy

Coding Democracy

How Hackers Are Disrupting Power, Surveillance, and Authoritarianism
edition:Paperback
also available: Hardcover
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Shifting the Balance

Shifting the Balance

How Top Organizations Beat the Competition by Combining Intuition with Data
edition:Hardcover
also available: eBook Audiobook
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Excerpt

 

Experience and a little PMJ

Data isn’t just spreadsheets or clicks of a mouse. Customer input through surveys and focus groups, Nielsen set-top rating systems, and even experience, the lessons you learned in the past, can be data points. "Experience is critical in the armed forces," says Royal Canadian Air Force Lieutenant Colonel Michael Whalen. The RCAF and other defense units have a term for experience — PMJ, or professional military judgement, and it is called on for decisions ranging from ordering supplies to when a solider may find themselves under fire and experience may be their best judge of action to take. "When my butt is on the line, when my aircraft is under fire, I want to be the one that makes the decision," Whalen says.

In sports, they call it understanding tendencies. Before you could track the exit velocity of a batted ball or designed infield shifts for pull hitters, you had scouts who watched rookies take a few hundred cuts, or high school coaches sending in reports on prospects. The data and analytics of today, though, are lightyears beyond handwritten scouting reports or Xs and Os on a chalkboard. It is not only far more advanced, with enormous amounts of data points (big data) but is predictive and prescriptive (suggesting course of action).

One of the earliest and easiest examples of predictive analytics is the FICO score created to assess the credit-worthiness of an individual. Developed in the early 70s, FICO is a model that uses massive amounts of credit data to enable a bank to quickly anticipate a creditors ability to repay a loan. While there has been significant criticism around the inherent bias of FICO, it is a simple example of the early use of big data and predictive analytics.

 

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