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.

Read More »

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.

Read More »

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.

Read More »

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.

Read More »

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.

Read More »

Can Process Robots Deliver Digital Transformation?

By Thomas H. Davenport, Feb 27, 2018

One of the fastest-growing areas of artificial intelligence—at least if that term is defined broadly—is “robotic process automation,” a set of capabilities for the automation of digital tasks. RPA, as it is often called, has some valuable functions, but digital-centric companies may need more intelligence and process simplification to than RPA can currently provide.

Read More »

Augmenting Self-Driving Cars with Human Capabilities

By Thomas H. Davenport, Jan 09, 2018

In a previous piece I wrote about an MIT conference suggesting that fully autonomous vehicles are not just around the corner. In the short run, then, we’ll be riding in increasingly smart vehicles, but we humans will still be expected to be in charge. And even after full autonomy is available, there may well be some human role in the process beyond catching some Zs behind the wheel or watching videos on a mobile screen.

Read More »

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.

Read More »

2018 Analytics Predictions and Priorities

By Bill Franks, Thomas H. Davenport, Robert Morison, Dec 07, 2017

Available to Research & Advisory Network Clients Only

Each year, the International Institute for Analytics takes time to focus on the latest analytics trends and the most pressing analytics challenges currently facing organizations. We gather the basis for our predictions from our day-to-day work supporting and advising analytics leaders and programs. Our insights arise from the breadth of expertise and cross-industry perspectives we receive every day from our clients, partners, and members of the IIA expert network. This is our 8th annual look forward into the upcoming year.

Read More »

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.

Read More »