Tag: Books

Jim Harris 1
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

Jim Harris 0
Measurement and disestimation

In his book Proofiness: How You’re Being Fooled by the Numbers, Charles Seife coined the term disestimation, defining it as “the act of taking a number too literally, understating or ignoring the uncertainties that surround it. Disestimation imbues a number with more precision that it deserves, dressing a measurement up as absolute

Jim Harris 2
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

Jim Harris 0
Bring the noise, boost the signal

Many people, myself included, occasionally complain about how noisy big data has made our world. While it is true that big data does broadcast more signal, not just more noise, we are not always able to tell the difference. Sometimes what sounds like meaningless background static is actually a big insight. Other times

Jim Harris 0
The low ethics of high-frequency trading

Imagine if your ability to feed your family depended upon how fast you could run. Imagine the aisles of your grocery store as lanes on a running track. If you can outrun your fellow shoppers, grab food off the shelves and race through the checkout at the finish line, then

Jim Harris 0
Innovation needs contamination

In his book Where Good Ideas Come From: The Natural History of Innovation, Steven Johnson explained that “error is not simply a phase you have to suffer through on the way to genius. Error often creates a path that leads you out of your comfortable assumptions. Being right keeps you in

Jim Harris 0
In algorithms we trust

In previous posts, I pondered the evolution of problem solving that is being data-driven by our increasing reliance on algorithms, which some mistrust as a signal that we’re shifting from human to artificial intelligence (AI). Would you like to play a game? “Slowly but surely,” John MacCormick explained in his book Nine Algorithms that Changed the

Jim Harris 0
Are you smarter than an algorithm?

“As the amount of data goes up, the importance of human judgment should go down,” argued Andrew McAfee in his Harvard Business Review blog post about Convincing People NOT to Trust Their Judgment, which is what he sees as the biggest challenge facing big data. “Human intuition is real,” McAfee

Jim Harris 1
Behavioral data quality

For decades, data quality experts have been telling us poor quality is bad for our data, bad for our decisions, bad for our business and just plain all around bad, bad, bad – did I already mention it’s bad? So why does poor data quality continue to exist and persist?

Jim Harris 0
Unleashing the data quality ideavirus

In a previous post, I urged you to prevent the spread of the data zombie virus. However, not all viruses are bad. In fact, there are even viral outbreaks that can be good for your organization. One of my favorite books is Unleashing the Ideavirus, where author Seth Godin explains

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