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EBook: Leading a Data Analytics Initiative

Eric Siegel

December 8, 2017

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Our latest EBook draws from Mining Your Own Business, A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics  written by industry experts Jeff Deal and Gerhard Pilcher.  The EBook includes Chapter 3 - Leading a Data Analytics Initiative which covers the key challenges and considerations for business leaders employing analytics to provide data-driven insight.

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Building Bridges: A Framework for Navigating Resistance To Analytics Results

Will Goodrum

December 1, 2017

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Deploying advanced analytics is a transformative process in any organization. Sometimes, new findings upend long-held beliefs and disrupt established business processes. This can engender a hostile reaction to the changes introduced by advanced analytics. When deep-held worldviews are threatened, emotional responses to defend the status quo are to be expected, even though they run counter to the facts. Such emotional reactions by stakeholders may block the successful adoption of analytics throughout the organization. How can analytics practitioners successfully press the case for change when emotions trump facts?

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Changing the Curve: Women in Computing

Jennifer Dutcher

November 24, 2017

BLOG_Women in Computing.jpgWhat do the first computer programmer, the patent holder for spread spectrum wireless communications, and the author of the first assembly language have in common? All were women, as are 34 percent of today’s web developers and 23 percent of programmers.

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Analytics Help Identify the Early Stages of a Stroke

Nathan French

November 17, 2017

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In many medical emergencies, such as a stroke, survivability requires fast diagnosis and treatment. But diagnosis may depend on a test that uses bulky, expensive equipment, such as the radiological imaging test that serves as a “gold standard” stroke test. That test is impractical in the field though, so a reliable portable test would be of great value. Data science offers a solution. Through the information embedded in a biological quantity known as gene expression, a data model can efficiently classify whether a patient is currently undergoing a stroke. This blog will discuss, specifically, the use of k-Nearest Neighbors (KNN) and Principal Component Analysis (PCA) to isolate a small number of genes whose combined expression levels might indicate a stroke is in progress. This can provide an alternative way to identify stroke victims, with lower equipment requirements than traditional radiological imaging. 

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Predictive Maintenance Optimizes Gas Well Production

Mike Thurber

November 13, 2017

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To offset the fluctuations in the cost of oil, the oil and gas industry looks for improved efficiencies in all parts of its production chain. Predictive analytics leverages the large volumes and variety of historical well data to find critical patterns to improve performance, reduce losses, enable operators to be more proactive in field operations, and reduce operational costs.

This article describes how sensor analytics and predictive maintenance helped to prioritize gas well intervention to reduce downhole freeze events and reduce remediation cost.  

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COPS: A Data-driven Solution for Pharmacy Fraud Detection

Isaiah Goodall

November 3, 2017

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Prescription drug fraud is a costly problem for health insurance providers, but identifying perpetrators can be extremely difficult. Staying ahead of ever-evolving fraud risks and proactively identifying and investigating active threats can be a challenge for insurance providers, Pharmacy Benefit Managers, Managed Care Organizations, and regional pharmacies. The many different actors and schemes involved, varying state regulations and oversight, and compliance with privacy laws all contribute to the challenge of detecting and preventing prescription drug fraud.

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Made-to-Measure Analytics in the Automation Age

Will Goodrum

October 27, 2017

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In recent months, Artificial Intelligence (AI) has leapt off the pages of Science Fiction and into the headlines. The Economist  focused an entire Technology Quarterly on the dramatic advances made possible through Machine Learning. Recent articles by Tom Davenport in Harvard Business Review and Deloitte have pushed moving beyond the “artisanal,” human-driven analytics of the past toward a bountiful, automated future. With talented Data Scientists scarce but vital, the value proposition for AI seems to be clear. So, should companies hire Data Scientists (especially consultants) if the computer can do all the work?

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Improving Workers' Compensation Fraud Detection

Isaiah Goodall

October 20, 2017

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Excessive or redundant medical services, medical coding errors, improper billing, as well as outright fraud, continue to be significant challenges for health insurers. The National Health Care Anti-Fraud Association (NHCAA) estimates that the financial losses due to health care fraud are in the tens of billions of dollars each year. The 2017 National Healthcare Fraud Takedown conducted by the Department of Health and Human Services Office of Inspector General was the largest health care fraud takedown in history with about $1.3 billion in identified false billings to Medicare and Medicaid.

Healthcare fraud is very difficult to detect because a variety of nuanced methods are employed, investigative evidence is often buried in text documents, and there can be collusion among network providers.

A national workers’ compensation insurance provider was interested in using analytics to help reduce provider fraud, waste, and abuse (FWA).  The main goal was to identify questionable provider practices and to prioritize work for investigators.  Second, it was important to measure the impact of the actions taken to further reduce the losses and promote best practices among providers.

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It is a Mistake to Rely on One Technique

John Elder, Ph.D.

October 13, 2017

BLOG_its-a-Mistake-to-Rely-on-One-Analytics-Technique.jpgIn his Top 10 Data Mining Mistakes John Elder shares lessons learned from more than 20 years of data science consulting experience. Avoiding these mistakes are cornerstones to any successful analytics project. In this blog about Mistake #2 you will learn about the dangers of relying on a single technique and some of the benefits of employing a handful of good tools.

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Nested Cross Validation: When (Simple) Cross Validation Isn’t Enough

Wayne Folta

October 6, 2017

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Several scientific disciplines have been rocked by a crisis of reproducibility in recent years [1]. Not long ago, Bayer researchers found that they were only able to replicate 25% of the important pharmaceutical papers they examined [2], and an MIT report on Machine Learning papers found similar results. Some fields have begun to emerge from their crises, but other fields, such as psychology, may have not yet hit bottom [3] [4].

We might imagine that this is because many scientists are good at science but not so adept with statistics. We might even imagine that we Analytics practitioners should have fewer problems because we are good at statistics. As a matter of fact, we find ourselves with an equivalent issue: predictive models that underperform once deployed. We have a powerful tool to prevent an underperforming model in Cross Validation (CV), but the ubiquity of CV in our modeling tools has led many Analysts to misunderstand how to properly use CV or appropriately create CV partitions, leading to lower-performing models.

This article will address the proper use and partitioning of CV to help us avoid these crises of under-performance in our own projects.

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