PREDICTIONS AND PRIORITIES FOR 2017

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.

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IT Organizations: The Shoemaker’s Analytical Children

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.

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Improve New Product Development with Predictive Analytics

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.

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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.

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Just How Smart Are Smart Machines?

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. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making. Yet managers we talk to don’t expect to see machines displacing knowledge workers anytime soon — they expect computing technology to augment rather than replace the work of humans. In the face of a sprawling and fast-evolving set of opportunities, their challenge is figuring out what forms the augmentation should take. Given the kinds of work managers oversee, what cognitive technologies should they be applying now, monitoring closely, or helping to build?

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Finance Must Ramp Up Role as Analytics Leader

By Thomas H. Davenport, Nov 15, 2016

The fictional crime-solver Sherlock Holmes once referred in a conversation to “the curious incident of the dog in the night-time.” A Scotland Yard detective replied, “The dog did nothing in the night-time.” Holmes retorted, “That was the curious incident.” In the field of analytics, the equivalent of the dog that didn’t bark is the relatively low level of adoption of advanced analytics in finance and accounting functions. Despite being a quantitative field by nature, finance has trailed other functions like marketing, supply chain, operations, and even human resources in employing advanced analytics to make key decisions.

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There is widespread agreement that the Internet of Things will be a transformative factor in the business use of information. The prospect of billions of connected devices promises to transform home activities, transportation, industrial operations, and many other aspects of our lives. The bad news about the IoT is that we have a lot of work to do before we are ready for it.

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How to Help Humans Work Better With Smart Machines

By Thomas H. Davenport, May 25, 2016

Perhaps the most important leadership issue is preparing your employees for roles in which they augment smart machines, and vice-versa. There will be new jobs involving implementation and oversight of these technologies—getting them installed, monitoring their daily performance, and improving them over time. Employees with some aptitude need to be groomed for such roles.

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The Logical Step Past Analytics Is Cognitive

By Thomas H. Davenport, May 11, 2016

Many people and companies seem to think of “cognitive computing” as a separate area from analytics. Most large organizations today have significant analytical initiatives underway, but they think of the cognitive space as being an exotic science project. One executive told me, “We have no desire to win Jeopardy,” an allusion of course to the IBM Watson project from 2011. But cognitive computing is not just about Watson, and it’s not an exotic science project.

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Business First, Analytics Second?

By Thomas H. Davenport, Feb 18, 2016

You may feel that “business first” is an obvious approach to take with analytics, but I assure you that it is anything but ubiquitous. It means that business objectives drive the business domain to which analytics are applied (what I have usually called “targets”), there are business objectives in place before the analytics are generated, and business considerations constrain the time and expense that are devoted to the analytical exercise. That may sound less fun than analysts running wild in an analytical sandbox, but it is generally the most effective and efficient approach to analytics.

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