Jim Harris is a recognized data quality thought leader with 25 years of enterprise data management industry experience. Jim is an independent consultant, speaker, and freelance writer. Jim is the Blogger-in-Chief at Obsessive-Compulsive Data Quality, an independent blog offering a vendor-neutral perspective on data quality and its related disciplines, including data governance, master data management, and business intelligence.
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
We sometimes describe the potential of big data analytics as letting the data tell its story, casting the data scientist as storyteller. While the journalist has long been a newscaster, in recent years the term data-driven journalism has been adopted to describe the process of using big data analytics to
While big data is rife with potential, as Larry Greenemeier explained in his recent Scientific American blog post Why Big Data Isn’t Necessarily Better Data, context is often lacking when data is pulled from disparate sources, leading to questionable conclusions. His blog post examined the difficulties that Google Flu Trends
Teller, the normally silent half of the magician duo Penn & Teller, revealed some of magic’s secrets in a Smithsonian Magazine article about how magicians manipulate the human mind. Given the big data-fueled potential of data science to manipulate our decision-making, we should listen to what Teller has to tell
Were you a mother who listened to classical music during your pregnancy, or a parent who played classical music in your newborn baby’s nursery because you heard it stimulates creativity and improves intelligence? If so, do you know where this “classical music makes you smarter” idea came from? In 1993, a
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
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
“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
