Research

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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Cognitive on the Continent

By Thomas H. Davenport, Oct 19, 2017

There is little doubt that the United States is the most active market for cognitive technologies, but it is hardly the only one. There is also considerable interest in the technology in Europe, and a number of projects are underway in relatively sophisticated organizations.

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The breadth of analytics has certainly increased in recent years. So, too, has the pool of people who dip their toe into creating analytics of one sort or the other. The trends toward democratization of data and self-service analytical capabilities are powerful and both have driven a lot of value for organizations in recent years. At the same time, it is possible to go too far.

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Resuscitating a lifelong dream with data science

By Sarmila Basu, Oct 05, 2017

Thousands of people die every year from a common infection that you can get when you go to the hospital. It’s called Clostridium Difficile 101 (or CDIFF for short), and people get infected with it 500,000 times per year, and devastatingly, it kills 29,000 people per year in the U.S. How are data scientists like those on my Data and Decision Sciences team in Microsoft IT able to help?

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