Gaining an ‘Unfair Advantage’ with Predictive Analytics

September 25, 2014
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Predicting the future is no longer just science fiction.

Data tools today are smarter, faster and more intuitive than ever before, and data scientists are making what was once viewed as futuristic capability a reality in today’s organization.

Predicting the future is no longer just science fiction.

Data tools today are smarter, faster and more intuitive than ever before, and data scientists are making what was once viewed as futuristic capability a reality in today’s organization.

Predictive analytics is now the most popular form of deeper insight into an enterprise, using enormous amounts of data to predict an outcome. Different from traditional analytics where users decide (often manually) which data is important, predictive analytics and data science doesn’t pre-judge the data. Every shred of an organization’s Big Data is collected and considered in an automated manner before algorithms are run to reveal the most beneficial trends.

Leading companies are using predictive analytics to generate insights and see results like what customers will buy next, when to expect a slump in sales and endless other outcomes. These companies are reshaping their organizations and even entire industries in the process.

Netflix is using predictive analytics to learn what movies viewers will rent next. Amazon is stocking warehouses using predictive analytics based on what consumers will buy next. The Nest thermostat is even using it to learn patterns over time, adjusting your home’s temperature automatically based on collected data. And Infogix customers use it to prevent fraud before it happens.

Too good to be true? Perhaps. But this is due to the challenges that arise when looking at the volume of existing data and the number of companies claiming to provide predictive analytical tools against that data. There’s a data deluge, and if you look at how analytics has kept pace, it’s like sucking water out of a waterfall with a straw, and then looking at the sample with a microscope.

With data science, it’s important to fundamentally challenge the way data is collected, leaving traditional methods in the dust while making way for innovation. Data collection needs to be end-to-end, automated and continuous, and data driven.

The proverbial “data swamp” is filled with structured, unstructured, relational, textual, log files and every other type of data in existence. In practice, these sources are all siloed and should be. However, few companies truly have the capability to combine them in a controlled manner to generate truly profitable insights.

Predictive models can be very powerful and, admittedly, might not exist for every question asked by business intelligence. When predictive analytic models work, however, they give businesses an unfair advantage.

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