Research

The Post-Algorithmic Era Has Arrived

By Bill Franks, Dec 14, 2017

Last week, IIA hosted our annual Predictions and Priorities webinar, as well as the associated research brief. When we sat down to determine what we should focus on this year, Tom Davenport and I both immediately raised a trend that we’ve recently been discussing with organizations. After reconciling our semantics, we realized that we were both excited about the same base trend. I want to reiterate it here as I think it is a critical trend to understand and adapt to. Namely, “the post-algorithmic era has arrived”.

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What We Talk About When We Talk About AI

By Thomas H. Davenport, Dec 12, 2017

What do we call the collection of technologies that make up what we used to call “artificial intelligence?” This conundrum reminds me of a Raymond Carver short story (and book) called What We Talk About When We Talk About Love. Artificial Intelligence (AI) isn’t quite as ambiguous a concept as love, but it’s moving in that direction.

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Numbers don’t lie, and they don’t judge. They don’t care if you’re a woman, or where you grew up, or if you speak with an accent. These are some of the thoughts I’ve had after speaking at the Grace Hopper Celebration of Women in Computing Conference and the Women in Statistics and Data Science Conference. After my presentations, I met with diverse groups of young women who are entering fields rooted in math and science. They wanted to know how I got here, what kind of training I’ve had, how I got on the management track, and how I came to lead a data analytics team at Microsoft.

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Data-driven decision-making: who doesn’t think it is a good idea? But it typically has a rough go in the real world of enterprise management, in part because the data itself often proves unreliable. For much of my business life IT has been tasked with building systems that could represent a single source of the truth. Unfortunately, that quest proved to be right up there with the holy grail and the fountain of youth—at best, aspirational, at worst, delusional. Today we have an opportunity to make a great leap forward, however, because for the first time in history we have broad access to high-volume data from a variety of sources that, when matched against each other, dramatically increase the probability of something like truth, and do so in a time window that is actionable. Part 3 of the blog series.

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Data-driven decision-making: who doesn’t think it is a good idea? But it typically has a rough go in the real world of enterprise management, in part because the data itself often proves unreliable. For much of my business life IT has been tasked with building systems that could represent a single source of the truth. Unfortunately, that quest proved to be right up there with the holy grail and the fountain of youth—at best, aspirational, at worst, delusional. Today we have an opportunity to make a great leap forward, however, because for the first time in history we have broad access to high-volume data from a variety of sources that, when matched against each other, dramatically increase the probability of something like truth, and do so in a time window that is actionable. Part 2 of the blog series.

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Why Blockchain And Analytics Don’t Mix Well

By Bill Franks, Nov 09, 2017

The concept of a blockchain is quite a phenomenon in recent times. It has quickly risen from a relatively obscure idea known mostly within some small circles to one that is being discussed as having potential to literally change some of the fundamentals of the world’s economic systems.

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Data-driven decision-making: who doesn’t think it is a good idea? But it typically has a rough go in the real world of enterprise management, in part because the data itself often proves unreliable. For much of my business life IT has been tasked with building systems that could represent a single source of the truth. Unfortunately, that quest proved to be right up there with the holy grail and the fountain of youth—at best, aspirational, at worst, delusional. Today we have an opportunity to make a great leap forward, however, because for the first time in history we have broad access to high-volume data from a variety of sources that, when matched against each other, dramatically increase the probability of something like truth, and do so in a time window that is actionable. Not everyone, of course, has access to all the sources, so to kick things off let me present a framework of the possible, within which each organization can determine what its actual will be. Part 1 of the blog series.

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In order to compete in the age of digital disruption, companies must find ways to create exponential changes in speed to market and time to value. To achieve these changes, many companies have replaced a “permission based” product development process with a “show the customer what’s possible” approach.

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Earlier this month, IIA held its 8th Analytics Symposium and awarded the 2017 ANNY Excellence in Analytics Award to Cisco Systems in a packed house at the Gleacher Center in Chicago. As at our spring Symposium in Silicon Valley earlier in the year, the fall Symposium agenda featured a marquee line up of analytics and data leaders from major firms like Morgan Stanley, UPS and Southwest Airlines, and concluded with a fireside chat with Tom Davenport and Jeanne Harris celebrating the launch of the 10th anniversary edition of Competing on Analytics.

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Today’s business executives are increasingly applying pressure to their Human Resources departments to “use predictive analytics”. This pressure isn’t unique to Human Resources as these same business leaders are similarly pressuring Sales, Customer Service, IT, Finance and every other line of business (LOB) leader, to do something predictive or analytical. It’s easy to waste time on predictive projects that deliver little value. What follows are 5 important steps to follow when selecting a predictive project.

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