Miss The Right Connections At Your Own Peril

By Bill Franks, Nov 12, 2014

While connection analytics won’t solve all of your organization’s problems, it can probably help solve some of them better. Given that it isn’t widely adopted yet, there is a chance to get a competitive advantage by putting it to use first. Ignore connection analytics at your own peril!

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I recently had a meeting with one of the largest companies in the world, where we discussed concerns about ongoing maintenance and, more importantly, ongoing repair required for analytics processes. The conversation helped solidify in my mind a major disconnect that often occurs when organizations deploy an analytics process into a production setting. Let’s walk through that disconnect here.

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Prepare to be Amazed by Immersive Intelligence

By Bill Franks, Sep 11, 2014

The range of immersive visual worlds that can be created is limited only by the types of data that exist and how that data can be utilized. In other words, it is virtually limitless.

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Many organizations fall victim to what I’m about to discuss and a fundamental shift in how organizations think about and fund analytics is required to address it. Today, the systems used to facilitate analytics within most organizations are owned by IT, which means that IT owns the budget to purchase and maintain the systems

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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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Inquiry: Relationships Between Analytics Teams and IT Resources and Partners

By Bill Franks, Jul 09, 2014

Available to ERS Clients only

What should be considered when establishing relationships between analytics teams and IT resources and partners?

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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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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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