Atlanta 2018 Analytics Symposium Video: Tom Davenport and Kathy Koontz

By Thomas H. Davenport, Kathy Koontz, Oct 24, 2018

The AI Advantage: A Fireside Chat with Tom and Kathy

In advance of Tom Davenport’s new book The AI Advantage: How to Put the Artificial Intelligence Revolution to Work, coming out this Fall, IIA’s Executive Director of its Analytics Leadership Consortium Kathy Koonz discuss some of the real vs hyped business benefits of AI and where to start considering the smart money investments to stay on the front end of this next wave of analytics advancement.

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Is Machine Learning Analytics or AI?

By Thomas H. Davenport, Oct 04, 2018

One of the definitional debates that bedevils the artificial intelligence (AI) field is whether machine learning is an AI-based method or technology. Or is it just an analytics-based activity? After all, it is statistical in nature, and attempts—as virtually analytical methods do—to fit a line or curve to a set of data points. So is it really AI? And what difference does it make

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DELTA Plus Model & Five Stages of Analytics Maturity: A Primer

By Thomas H. Davenport, Sep 11, 2018

Available to Research & Advisory Network Clients Only

The purpose of this research brief is to summarize the key elements of DELTA Plus and Five Stages of Analytics Maturity, and discuss how these two frameworks can be used to understand analytical maturity in your organization. Two new components were added to the DELTA model, creating the DELTA Plus model. The DELTA Plus Model Framework encompasses the five foundational elements of a successful analytics program (Data, Enterprise, Leadership, Targets, and Analysts) and introduces two new elements (Technology and Analytical Techniques) required for high performance.

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Three More Reasons to Embrace Automated Machine Learning

By Thomas H. Davenport, Jul 19, 2018

Automated machine learning is good for your company’s analytics function. AutoML has the potential to transform not only machine learning, but the practice of analytics in general. This blog discusses the benefits of AutoML in three different categories.

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Don’t Be Hit by the Analytics Backlash

By Thomas H. Davenport, Jun 19, 2018

Analytics leaders and practitioners need to be prepared both to defend analytics and AI where appropriate, ensure that you’re not contributing to issues like how to prevent algorithmic bias, what industries would be least likely to do harm with analytics, and how to reduce the societal damage from AI.

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How to Outflank the Competition With Analytics

By Thomas H. Davenport, May 24, 2018

CIOs can help drive business value by following the lead of high-performing companies that use advanced analytical techniques and data-driven insights to rise above their competitors.

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Will Data Scientist Continue to Be the Sexiest Job?

By Thomas H. Davenport, May 08, 2018

Back in 2012 I wrote (with D.J. Patil, who went on to become the Chief Data Scientist in the White House) an article in Harvard Business Review called “Data Scientist.” Nobody remembers the title or much about the content of the article, but many remember the subtitle: “Sexiest Job of the 21st Century.” At the time (and still today), these jobs paid well, were difficult to fill, and required a very high level of analytical and computational expertise. But a more accurate subtitle might have been “Sexiest Job of the 2010-2019 Decade,” because I am not sure how much longer data scientists will be in great demand.

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84.51° Builds a Machine Learning Machine for Kroger

By Thomas H. Davenport, Apr 24, 2018

Machine learning is a great way to extract maximum predictive or categorization value from a large volume of structured data. The idea is to train a model on a one set of labeled data and then use the resulting models to make predictions or classifications on data where we don’t know the outcome. The approach works well in concept, but it can be labor-intensive to develop and deploy the models. One company, however, is rapidly developing a “machine learning machine” that can build and deploy very large numbers of models with relatively little human intervention.

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Enterprise AI Primer: Build on Your Strengths

By Thomas H. Davenport, Kris Hammond, Apr 16, 2018

Available to Research & Advisory Network Clients Only

This brief is based on the premise that there’s a general confusion when it comes to AI impact, strategy, investment options, and even terminology. A significant factor is that for many companies, AI can and should be viewed as a natural progression of their existing business analytics capabilities. We believe that positioning AI as a natural evolutionary outgrowth of analytics, thus benefitting from already established analytics capabilities, provides the best and easiest path for most companies to successfully “step into” AI.

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

By Thomas H. Davenport, Doug Gray, Mar 22, 2018

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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