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.
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.
By Thomas H. Davenport, Feb 28, 2017
While humans may be ahead of computers in the ability to create strategy today, we shouldn’t be complacent about our dominance. As a society, we are becoming increasingly comfortable with the idea that machines can make decisions and take actions on their own. We already have semi-autonomous vehicles, high-performing manufacturing robots, and automated decision making in insurance underwriting and bank credit. We have machines that can beat humans at virtually any game that can be programmed. Intelligent systems can recommend cancer cures and diabetes treatments. “Robotic process automation” can perform a wide variety of digital tasks.
By Thomas H. Davenport, Feb 07, 2017
In H. G. Wells’s classic The War of the Worlds, the narrator pauses a moment to rue the fact that he didn’t react sooner to the arrival of an “intelligence greater than man’s”—in his case, Martians landing on earth. Comparing himself to a comfortable dodo in its nest, he imagined those ill-fated birds also dithering as hungry sailors invaded their island: “We will peck them to death tomorrow, my dear.” And what about you? As intelligent technologies take over more and more of the decision-making territory once occupied by humans, are you taking any action? Are you sufficiently aware of the signs that you should? To help you get the head start you may need, here are the signs that it’s time to fly the nest. All of them are evidence that a knowledge worker’s job is on the path to automation.
By Thomas H. Davenport, Jan 24, 2017
Technologists and business folks alike overstate the shortage of data scientists. They just need to know where to look. Here are some common excuses companies use for not employing data scientists, and why they’re no longer valid.
By Thomas H. Davenport, Daniel Magestro, Robert Morison, Dec 20, 2016
Available to Research & Advisory Network Clients Only
Each year, the International Institute for Analytics takes a step back from the day-to-day work of supporting and advising analytics leaders and programs, to focus on the latest trends and the most pressing challenges currently facing organizations. We have a unique advantage in this endeavor, given the breadth of expertise and cross-industry perspectives we receive every day from our clients, partners, and members of the IIA faculty and expert network.
By Thomas H. Davenport, Dec 20, 2016
For the great majority of years in the past decade, Chief Information Officers named “business intelligence and analytics” as their top focus in Gartner Inc. annual surveys of technology priorities. That set of technologies moved to number one in the survey in 2006 and stayed there until 2009. It fell to fifth in 2010 and 2011, but was back on top in 2012 and has stayed there ever since.
By Thomas H. Davenport, Dec 13, 2016
Recently on this site, one of us wrote about the new product development analytics used by Netflix. In a nutshell, the company classified the key attributes of past and current products or services and then they modeled the relationship between those attributes and the commercial success of the offerings. This produced a predictive model that provides the company with guidance about how likely a new product or service is to be successful.
By Thomas H. Davenport, Dec 01, 2016
Many times when I speak with analytics managers or business people interested in analytics, they tell me that performing some analytics on data is not the primary problem they have. “We have to get the analytics integrated with the process and the systems that support it,” they say. This issue, sometimes called “operational analytics,” is the most important factor in delivering business value from analytics. It’s also critical to delivering value from cognitive technologies – which, in my view, are just an extension of analytics anyway.
By Thomas H. Davenport, Nov 22, 2016
The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization’s business needs requires a deep understanding of their capabilities. If popular culture is an accurate gauge of what’s on the public’s mind, it seems everyone has suddenly awakened to the threat of smart machines.