A Revolution in Analytical Technology

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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Tom Davenport
Push Your Analytics Out to Customers

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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Deep Learning: Einstein or Savant?

Once you dig into deep learning, you’ll find that as opposed to being a generally brilliant algorithm akin to Albert Einstein, it is much more akin to a savant like the famous movie character from Rain Man. In other words, a deep learning process is really smart at a specific task or two, but not smart at all for anything else.

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Getting Real About Autonomous Cars

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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5 Things New Analytics Leaders Should do to Succeed

Any new leader in any field will have to face several challenges in the first few months on the job if he or she is to succeed. IIA co-founder Tom Davenport and I discussed some of the challenges analytics leaders face and what they can do to ensure success. While the action steps apply broadly, we focused on how they apply specifically within the realm of analytics.

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Move Your Analytics Operation from Artisanal to Autonomous

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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A New Chapter in the Analytics Journey

Every individual and enterprise travels a unique journey in the pursuit of analytics. In my case I could never have predicted how my journey would unfold when I first entered the workforce over 20 years ago. The rise of analytics as a strategic imperative and the explosion of career opportunities within the field far surpass what I expected coming out of school. I feel very lucky to have had such a terrific journey so far and will be interested to look back 20 years from now and see what happens from here.

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Beyond the Black Box in Analytics and Cognitive

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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