Reinvention in the Age of Analytics - A Decade’s Worth of Insights

By Thomas H. Davenport, Oct 31, 2017

Available to Research & Advisory Network Clients Only

2017 Analytics Symposium - Chicago Session Recording

Tom Davenport and Jeanne Harris discuss what’s changed since their seminal book, Competing on Analytics, was published 10 years ago and what do leaders need to do to stay ahead of the game in the next decade.

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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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For the many years that I have been researching IT, there has always been a clear distinction between certain types of applications. These classic distinctions, however, are breaking down—in large part because of emerging technologies like the Internet of Things (IoT) and artificial intelligence (AI).

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A Revolution in Analytical Technology

By Thomas H. Davenport, Aug 22, 2017

It’s been 10 years since Jeanne Harris and I published our book, Competing on Analytics, and we’ve just finished updating it for early-fall (2017) re-publication. We realized during this process that there have been a lot of changes in the world of analytics.

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Are Analytics Truly Self-Service?

By Thomas H. Davenport, Jul 25, 2017

I have been thinking about some of the changes over the last decade in analytics, coinciding with the revised and updated release of my book with Jeanne Harris, Competing on Analytics. The book is ten years old, and much has changed in the world of analytics in the meantime. In updating the book (and in a previous blog post about the updates), we focused on such changes as big data, machine learning, streaming analytics, embedded analytics, and so forth. But some commenters have pointed out that one change that’s just as important is the move to self-service analytics.

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5 Things New Analytics Leaders Should Do to Succeed

By Bill Franks, Thomas H. Davenport, Jul 19, 2017

Available to Research & Advisory Network Clients Only

There is a fair amount of management research suggesting that the first 90 days or so are the most important time of a leader’s tenure. It’s when you establish your reputation and it determines what people start to think about you in your role. It’s often hard to change those first impressions. Therefore, IIA held a webinar to discuss this very important period for senior analytics leaders like a Chief Analytics Officer, Chief Data Officer, VP of analytics, or similar senior role. This paper captures the key elements of the discussion between Bill Franks and Tom Davenport, which focused on five essential things new analytics leaders should do to set themselves up for success.

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Push Your Analytics Out to Customers

By Thomas H. Davenport, Jun 29, 2017

Analytics and big data have penetrated most large organizations by now, and are helping to improve many internal decisions. But they can also have a major impact on the decisions of customers or citizens. This applies not only to decisions about what products to buy, but also to decisions about safety and crime.

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Getting Real About Autonomous Cars

By Thomas H. Davenport, Jun 01, 2017

I attended the MIT Disruption Timeline Conference on AI and Machine Learning. There was interesting content on a variety of topics, but a primary focus was on when specific AI capabilities might become generally available. One particular technology addressed was autonomous vehicles. The key question was when 50 percent of vehicles on US roads would be fully autonomous.

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Move Your Analytics Operation from Artisanal to Autonomous

By Thomas H. Davenport, May 02, 2017

Many organizations today are wondering how to get into machine learning, and what it means for their existing analytics operation. There are many different types of machine learning, and a variety of definitions of the term. I view machine learning as any data-driven approach to explanations, classifications, and predictions that uses automation to construct a model. The computer constructing the model “learns” during the construction process what model best fits the data. Some machine learning models continue to improve their results over time, but most don’t. Machine learning, in other words, is a form of automating your analytics. And it has the potential to make human analysts wildly more productive.

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Beyond the Black Box in Analytics and Cognitive

By Thomas H. Davenport, Apr 04, 2017

There is a growing crisis in the world of analytics and cognitive technologies, and as of yet there is no obvious solution. The crisis was created by a spate of good news in the field of cognitive technology algorithms: they’re working! Specifically, a relatively new and complex type of algorithms—deep learning neural networks (DLNN)—have been able to learn from lots of labeled data and accomplish a variety of tasks. They can master difficult games (Go, for example), recognize images, translate speech, and perform many more tasks as well as or better than the best humans.

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