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.
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.
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.
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.
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.
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.
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.
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.
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.
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.