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Predictive Analytics

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3 Tips to Make You a Genius Forecaster

October 13, 2014 by Paul Barsch
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Forecasting is hard, and even those who sometimes get it right, often fail on a continuous basis. But fear not, there are three steps you can take to drastically improve your forecast accuracy, but you’ll have to be willing to put in the work, and possibly put your ego aside to get there.[read more]

Who Gets the Call When Your Analytics Process Crashes?

October 10, 2014 by Bill Franks

Analytics matters.

I recently had a meeting with one of the largest companies in the world, where we discussed concerns about ongoing maintenance and, more importantly, ongoing repair required for analytics processes. The conversation helped solidify in my mind a major disconnect that often occurs when organizations deploy an analytics process into a production setting. Let’s walk through that disconnect here.[read more]

Descriptive, Predictive, and Prescriptive Analytics Explained

October 8, 2014 by Ray Major
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Analytics explained.

With the flood of data available to businesses regarding their supply chain these days, companies are turning to analytics solutions to extract meaning from the huge volumes of data to help improve decision making.[read more]

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Fine-Tuning Manufacturing Operations with Big Data and Hadoop

October 6, 2014 by Sameer Nori

Manufacturing pperations and Hadoop.

Your organization is a lean, mean Six Sigma machine. The corporate culture is centered on continuous improvement, with everyone well versed in Kaizen. Your supply chain is well oiled, which should provide assurance about product quality. And yet you wonder: is it possible to improve operations even further?[read more]

How Biostatistics and Spatio-Temporal Modeling Can Be Used to Protect Human Health

October 2, 2014 by Lillian Pierson

Air pollution. 

In an exclusive interview for Statistics Views, Dr. Chien gives an overview on his research, on how his research can be used to improve human health conditions in Taiwan, and on his favorite statistical method for analyzing space-time data related to particulate matter air pollution.[read more]

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Hadoop in Advertising & Media: Is Data Analytics Making Old Media New?

October 2, 2014 by Sameer Nori

Hadoop in advertising and media.

Imagine the chance to truly connect with a customer. Imagine if you knew what movies and television shows they watched, and not only when, but how often. Imagine knowing what screen they watched it on and how they shared it socially. Imagine knowing what they like, or dislike, and knowing it in real-time.[read more]

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5 Big Data Hadoop Use Cases for Retail

September 30, 2014 by Sameer Nori

Hadoop for retail.

How does Apache Hadoop help retailers? Apache Hadoop is not just a data storage and processing framework - it is an transformational platform, especially for retailers who want to analyze customer data and improve revenues and the general customer experience.[read more]

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5 Ways Big Data Impacts the Insurance Industry

September 26, 2014 by Gil Allouche
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Insurance.

As technology improves, so too does the ability to predict future diseases. And predicting future diseases is extremely important for health insurance companies. With big data, companies are now finding ways to monitor patients’ current health status while still looking at their potential for future diseases.[read more]

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Gaining an ‘Unfair Advantage’ with Predictive Analytics

September 25, 2014 by Kristen Paral

Predictive analytics.

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.[read more]

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Is Big Data the Silver Bullet for Advanced Supply Chain Analytics?

September 25, 2014 by Ray Major

Analytics.

Leveraging “Big Data” is coming up more and more as a “must have” in conversations I’m having with clients and prospects, not only in the food and beverage industry, but in other industries that have supply chain applications as an integral part of their operations.[read more]

Big Data and Ford's Faster Horses

September 15, 2014 by Arent van 't Spijker

Horse? Or horseless carriage?

In the late 19th century, a New York City planner put out a warning that by 1950 the city would be completely uninhabitable. The problem, as he saw it, was that at the current growth rates the city would not be able to sustain the growing number of horses.[read more]

Intelligence Input = Sales Output

September 3, 2014 by Ray Major

Test of time.

In my experience many tech professionals, especially the executive ranks, consider themselves forward-thinkers, early adopters, one or two steps ahead, dispensers of wisdom. “If companies or consumers would just take our advice and buy our product or license our app, businesses would run better, people would be happier.” Or some variation on that theme.[read more]

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Derailing Your Supply Chain BI Project

September 1, 2014 by Ray Major

Derailing a project.

Remind people that data doesn’t kill dreams. A Supply Chain Business Intelligence initiative is not a funeral service for their creativity. Quite the opposite, in fact. The insights they gain from BI will pave the way for the boldest, most creative options you’ve ever considered as a business.[read more]

Untangling the Retail Supply Chain with Real-Time Analytics

August 26, 2014 by Dale Skeen

Untangling the retail supply chain.

Retail supply chains are longer and more tangled than ever before – the complexity of the data sets and the management of far-flung suppliers coupled with high customer expectations around service and reliability are taxing traditional approaches to supply chain management to their limits.[read more]

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Spark the Flame: The Power Behind Real Time Analytics

August 26, 2014 by Gil Allouche

Real time analytics/Deviantart.net 

Real time data analytics is like a burning fire. Consider this analogy. Once a spark catches, several chemical and physical changes occur. The fuel (data) is processed while heat (or data outcomes) is simultaneously emitted. Once the process of data streaming begins, it is seamless and very efficient. The process will continue to be efficient as long as data is fed into the system.[read more]

 

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