Why Predictions Are Not Enough

By Bill Franks, Jan 12, 2017

Predictions Are The Starting Point…

Almost by definition, advanced analytics or data science initiatives involve applying some type of algorithm to data in order to find patterns. These algorithms are typically then used to generate one or more of the following:

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When Big Data Can’t Predict

By Bill Franks, Dec 08, 2016

Most people think that in the age of big data, we always have more than enough information to build robust analytics. Unfortunately, this isn’t always the case. In fact, there are situations where even massive amounts of data still don’t enable even basic predictions to be made with confidence. In many cases, there isn’t much that can be done other than to recognize the facts and stick to the basics instead of getting fancy. This challenge of big data that can’t be used to predict seems like an impossible paradox at first, but let’s explore why it isn’t.

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As analytics are embedded more and more deeply into processes and systems that we interact with, they now directly impact us far more than in the past. No longer constrained to providing marketing offers or assessing the risk of a credit application, analytics are beginning to make truly life and death decisions in areas as diverse as autonomous vehicles and healthcare. These developments necessitate that attention is given to the ethical and legal frameworks required to account for today’s analytic capabilities.

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I recently had someone ask me, “For years we’ve talked about changing analytics from 80% data prep and 20% analytics to 20% data prep and 80% analytics, yet we still seem stuck with 80% data prep. Why is that?” It is a very good question about a very real issue that causes many people frustration.

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Breaking Analytics Out Of The Box - Literally

By Bill Franks, Sep 08, 2016

The lines between open source and commercial products are blurring rapidly as our options for building and executing analytics grow by the day. The range of options and price points available today enable anyone from a large enterprise to a single researcher to gain access to affordable, powerful analytic tools and infrastructure. As a result, analytics will continue to become more pervasive and more impactful.

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Embrace Analytic Athleticism

By Bill Franks, Aug 11, 2016

I see a strong parallel between athleticism and analytic capability. I also see a strong parallel between learning to speak multiple languages and learning to work within differing analytic environments. I’ll explain what I mean by both of these statements in this blog in the hope that it will help make the path forward seem clearer and less intimidating.

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Is your organization doing all it can to modernize your data collection and analytics processes? Barely a decade ago, networks like AMC had virtually no information on consumers. Today, they are able to capture information at a level not possible until very recently.

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Much like the Fibonacci sequence appears repeatedly in nature, there are recurring patterns in data that, once recognized, can improve both our analytics and our efficiency in creating them.

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We’re all generating a lot of data about ourselves and how we live day to day. There are personal fitness devices, preferences and opinions expressed on social media, details on when we’ve come and gone from the house from our security systems, and more. It isn’t just data that companies are collecting from us, but data that we are directly generating ourselves. What should we be willing to do with it and at what price?

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Approach Big Data Analytics Like A Lego Kit

By Bill Franks, Apr 14, 2016

When it comes to Lego’s pre-packaged building kits, there is one aspect that ties closely to the needs of big data analytics. Namely, Lego provides consumers kits that use a combination of both custom pieces and standard pieces to create awesome models which children (and some adults!) can’t wait to get their hands on.

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