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The brave new world of predictive analytics


 Guest post by Arthur Cole

The recent hype surrounding predictive analytics might make it seem like an entirely new form of data mining, but the science dates back more than 300 years. According to Tadd Wood, chief data scientist at Contemporary Analysis, in 1689, shipping and trading by sea was risky business for banks. Insurance company Lloyd's of London began collecting and disseminating data that underwriters could use to assess the risks of financing sea voyages. When risk was high, the banks would collect a premium. Today, predictive analytics empowers enterprises to collect and analyze enormous volumes of raw data and turn it into valuable insights in record time.

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Leveraging advanced computing platforms and database technology to interpret current events and chart the future is already emerging as one of the key applications for Big Data and the Internet of Things—a driving force in the race to scale enterprise infrastructure to levels undreamed of only a few short years ago. Increasingly, experts are starting to value brains over brawn—or brains on par with brawn—when it comes to building a predictive analytics data ecosystem. "Predictive analytics can be complex," says Brad Nelson, chief field technologist at HPE, "and even more so with Big Data. Part of the difficulty is understanding the algorithms and when to use them." Out-of-the-box analytics platforms reduce complexity, but there are requirements to consider.

Emerging platforms require new methodologies to produce the highest quality results, particularly in the preparation of data. As Forrester's Mike Gualtieri noted in Enterprise Apps Today, "Predictive models are only as accurate as the data fed into them, and over time they may degrade or increase their effectiveness."

In traditional settings, data prep can take up to three-quarters of the overall project time, as data scientists must manually calculate aggregate fields, strip extraneous characters, and merge disparate data sources. In a predictive setting, algorithms are self-adjusting as new data is added, while Agile application development continuously improves performance of increasingly complex data loads. "Great technology," Nelson says, "extrapolates the complexities involved and makes it easy for the end user."

Integrating analytics and business operations

To take full advantage of predictive analytics, it must be built into the business ecosystem—fully integrated into business operations. According to an HPE white paper, "It is no longer sufficient to produce robust analytics. Organizations that want to see measurable business results from analytics must focus on embedding analytics and insights into day-to-day operations to enable analytically driven decision-making—fast, automated, and operational." Operational analytics is the integration of predictive analytics into business operations, applications, and machines, empowering enterprises to analyze, measure, and monitor data to continuously improve business operations. Unlike ad hoc analytics, which focuses on individual projects, operational analytics is applied across the organization for wide-scale business outcomes.

Predictive analytics is another example of a long-standing discipline that is not only improved by recent advancements in computing technology, but also reimagined on a fundamental level. While in the past organizations could use analytics to determine probabilities, emerging platforms will remove virtually all guesswork from the the data mining process, resulting in greater synchronicity between supply and demand, risk and reward, and cost and benefit. Getting to the truth of a situation will still require smart people to ask the right questions in the right way, but at least the capability to definitively ascertain what is real and what is not is finally within our grasp.

For an up-close look at what's coming in Big Data analytics, register for Hewlett Packard Enterprise's Big Data Conference 2016 in Boston.


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Jan 30-31, 2018
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