I've written before on this site about the Netflix data advantage. The company isn't exactly forthcoming about its data, a certainly tenable position these days. After all, data is a major source of its competitive advantage. Now, thanks to an astonishing article by Alexis Madrigal in The Atlantic, laypeople now possess a much
In 1964, when the American radio astronomers Arno Penzias and Robert Wilson were setting up a new radio telescope at AT&T Bell Labs, they decided to point it towards deep space where they expected a silent signal that could be used to calibrate their equipment. Instead of silence, however, what they heard
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
It is easy to consider data migration as a movement problem. After all, we need to get our data from A to B with as little effort and cost as possible. With this viewpoint, many practitioners commence mapping and linking the source and target systems together to form an elaborate
My previous post was inspired by what Andrew McAfee sees as the biggest challenge facing big data: convincing people to trust data-driven algorithms over their expertise-driven intuition. In his recent VentureBeat blog post, Zavain Dar explained that the real promise of big data is that it will change the way
Dylan Jones says one way to improve data accuracy is to increase the frequency and quality of reality checks.
“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
Big data seems like a daunting challenge because, as data management professionals, we have been taught by experts and learned from experience that we always have to dive deep into data in order to discover meaningful business insights, solve business problems, and support daily business operations. However, it’s possible to
One of the most common questions I get asked by our members on Data Quality Pro is, “Can you do more articles on data quality dimensions?” Part of the reason for this request is when people first start getting involved with data quality, they invariably buy data quality books and
Jim Harris recently penned an interesting article describing what happens to data quality at the top of the bell curve. The central theme of the article explains how, as we strive for greater levels of quality, we hit diminishing returns. For example, the cost of sending an engineer down a
