Research

Dueling Schooling on Big Data and Analytics

By Thomas H. Davenport, Jul 22, 2014

There’s been a lot of discussion about the shortage of quantitative analysts and data scientists in this world, and many people wonder where they will all come from. Today I have good news and bad news for you. The good news is that there are a rapidly growing number of educational institutions that are offering courses, concentrations, and degree programs in analytics and big data.

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Is Big Data Good or Evil?

By Bill Franks, Jul 09, 2014

With all of the lawsuits working through the courts and all of the scary possibilities being discussed in the media, it has led some people to assume that big data is inherently evil. Once you believe that big data is evil, a natural response is to try and shut down the collection and analysis of big data to the maximum extent possible. While big data certainly has risks, it would be a classic case of throwing out the baby with the bathwater if the use of big data is shut down.

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The Analytics of Things

By Thomas H. Davenport, Jul 07, 2014

The press and blogosphere are full of references to “The Internet of Things” (TIoT) or even “The Internet of Everything.” It’s great to connect inanimate objects to the Internet, of course. But that’s only a first step in terms of doing something useful with all those connected devices. “The Analytics of Things” are just as important, if not more so.

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Many organizations are focused on driving analytics as a foundation for competitive advantage. Often overlooked in this discussion is the importance of establishing a foundation for analytics through the process of data readiness and data cleansing.

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Bill Franks, an IIA faculty member and Chief Analytics Officer for Teradata, was recently featured in a webinar discussing approaches to making big data more actionable and profitable by utilizing data visualization tools and strategies. The talk highlighted the important opportunities and level of insight that big data and analytics can provide organizations and shared how visualization tools can better support decision making and lead to discovery of new insights.

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Pursuing innovative analytics through a portfolio funding model isn’t about removing accountability or financial discipline. It is about applying accountability and financial discipline in a way that accounts for the realities of the situation. It is also about providing leeway to the analysts tasked with discovery and innovation to truly try new approaches to improving a business through analytics.

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Is There a Leadership Vacuum for Big Data?

By Thomas H. Davenport, Jun 03, 2014

If big data and analytics are the powerful business resource that I think they are, they need someone to champion and oversee their usage in organizations. The problem is that many organizations don’t really have someone in charge of these capabilities. There is in many companies a leadership vacuum for big data and analytics.

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Holes in the Sidewalk of Analytics

By Dwight N. McNeill, May 27, 2014

The field of analytics has fallen into a few big holes lately that represent both its promise and its peril. These holes pertain to privacy, policy, and predictions.

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Quick! Invest in this CBIMBDMLAAS Company

By Bill Franks, May 08, 2014

The new company will be focused on Cloud-Based In-Memory Big Data Machine Learning Analytics as a Service (CBIMBDMLAAS). I challenge readers to find another premise to build a business around that captures as many of the hot trends in the market today as that term does. Just being able to say that mouthful with a straight face is almost certainly worth a first round of funding in the low millions of dollars today as long as even a cursory business plan and light prototype is used to support it. I will have those soon (I promise), but I need your money first to develop the idea further.

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When Machine Learning Isn’t Learning

By Bill Franks, Apr 10, 2014

Terms come in and out of vogue on a regular basis. In recent years, the use of the term Machine Learning has surged. What I struggle with is that many traditional data mining and statistical functions are being folded underneath the machine learning umbrella.

There is no harm in this except that I don’t think that the general community understands that, in many cases, traditional algorithms are just getting a new label with a lot of hype and buzz appeal. Simply classifying algorithms in the machine learning category doesn’t mean that the algorithms have fundamentally changed in any way.

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