Posts tagged predictive analytics
Push Your Analytics Out to Customers

Analytics and big data have penetrated most large organizations by now, and are helping to improve many internal decisions. But they can also have a major impact on the decisions of customers or citizens. This applies not only to decisions about what products to buy, but also to decisions about safety and crime.

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Rise of the Strategy Machines

While humans may be ahead of computers in the ability to create strategy today, we shouldn’t be complacent about our dominance. As a society, we are becoming increasingly comfortable with the idea that machines can make decisions and take actions on their own. We already have semi-autonomous vehicles, high-performing manufacturing robots, and automated decision making in insurance underwriting and bank credit. We have machines that can beat humans at virtually any game that can be programmed. Intelligent systems can recommend cancer cures and diabetes treatments. “Robotic process automation” can perform a wide variety of digital tasks.

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Moving Beyond Predictions – Second Order Analytics

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

In recent times, I have read a number of articles lamenting the frequent lack of value resulting from large scale analytics and data science initiatives. While I have seen substantial value driven from many efforts, I have also seen examples where the results were very poor. My belief is that oftentimes the problems can be boiled down to one basic mistake. Namely, thinking that generating predictions, forecasts, or simulations is enough. It is not.

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IT Organizations: The Shoemaker's Analytical Children

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

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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When Big Data Can't Predict

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. 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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