It used to be that a doctor was a doctor for the most part. Even a century ago, unless you lived in a large city, people likely had a town doctor who handled most every type of ailment and guided most any type of treatment. Given the limited medical knowledge and lack of sophisticated treatment options during this time, these generalists could often provide a level of care that was comparable to the best available. Today, that is no longer true in medicine and a similar trend is playing out in analytics.

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Last month, I wrote about why simply making predictions isn’t enough to drive value with analytics. I made the case that behind stories of failed analytic initiatives, there is often a lack of action to take the predictions and turn them into something valuable. It ends up that identifying and then taking the right action often leads to additional requirements for even more complex analyses beyond the initial effort to get to the predictions! Let’s explore what that means.

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Why Predictions Are Not Enough

By Bill Franks, Jan 12, 2017

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: Predictions about future events. For example, who is most likely to respond to a given offer? Forecasts of future results. For example, what sales can we expect from the upcoming promotion? Simulations of various scenarios. For example, what will happen if I shift some of my budget from paid search to television advertising?

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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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