Posts tagged Tom Davenport
In Praise of “Boring AI”

The press, consultants, and IT market research firms would have us believe that AI is the most exciting technology available today. And it is—but in part because there aren’t a lot of exciting alternatives. Blockchain isn’t turning out to be the revolutionary technology it promised to be, and it will take a long time to come to fruition. Similarly, the Internet of Things is taking forever, in part because there are way too many standards, and none of them is sufficiently influential. It’s not surprising, for example, that the vendor C3 changed its name from “C3 IoT” to “C3 AI.”

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On AI, Deflate Both the Positive and Negative Hype

It seems to be inevitable. Any popular technology or approach to business change apparently has to involve a large amount of breathlessly positive media-driven hype, and then must be followed by potshots and disparagement. The positive press usually lasts for a couple of years or so, and then authors, journalists, and speakers who seek attention realize that they can’t get much of it by jumping on the optimistic bandwagon. They then begin to criticize the idea. Both the positive and negative hype are typically equally unrealistic.

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Welcome to the Age of Explainability

Modern life has been good for those who understand and can develop analytics. From sex to soccer, data and algorithms are having an increasing impact on how important decisions are made. But it hasn’t been as pleasant for those who don’t understand algorithms but are still subject to the decisions they help to make.

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The All-Inclusive Analytics Organization

Whatever your favorite taxonomy or category of analytics, I’m here to argue that it doesn’t matter from an organizational standpoint. That is, I think all the categories should be supported by one organization. I’ll spend the rest of this post arguing why an inclusive approach is more effective.

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AI Is Destroying Traditional Business Thinking

We all have seen a shift in the companies and industries that have larger market value. The change from manufacturing to digital companies as the hot organizations has not happened overnight. Yet the speed in which they have climbed the ranks is faster than most could have imagined.

In the article below by Thomas H. Davenport, Barry Libert, and Megan Beck they answer the question; “So what do you do if you are a leader of a company and want to make the shift from the product and services economies of the last economy to the scale and economics of the current one based on platforms, networks and AI?”

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Do You Need a Ph.D. to Run Analytics or Data Science?

While we are supportive of companies’ efforts to hire quantitative Ph.D.’s to practice data science, we believe that most firms are better off hiring people with other types of training and general management skills to manage analytics and data science groups. Why? Because there are a series of traits that make for effective managers of such groups, and most Ph.D.’s don’t tend to have them. We describe ten of those traits in this blog, and the reasons why they are unlikely to be found in the average doctoral degree holder. The list of traits may be useful for anyone seeking to hire a leader of analytics or data science functions-whether they are considering Ph.D.’s or not.

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Effective Operational Analytics is About More than Analytics

Many times when I speak with analytics managers or business people interested in analytics, they tell me that performing some analytics on data is not the primary problem they have. “We have to get the analytics integrated with the process and the systems that support it,” they say. This issue, sometimes called “operational analytics,” is the most important factor in delivering business value from analytics. It’s also critical to delivering value from cognitive technologies – which, in my view, are just an extension of analytics anyway.

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Just How Smart are Smart Machines?

The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization’s business needs requires a deep understanding of their capabilities. If popular culture is an accurate gauge of what’s on the public’s mind, it seems everyone has suddenly awakened to the threat of smart machines.

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