Industries

Healthcare


Improve clinical care and patient outcomes, reduce fraud, and manage financial risk.

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Healthcare

Insurance


Prioritize claims, reduce risk and cost, prevent fraud, and improve customer experience.

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Insurance

Financial Services


Improve regulatory compliance, detect fraud, assess risk, and enhance customer value.

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Financial Services

Software & Technology


Analyze sensor/log data to enhance product design and user experience, and reduce attrition.

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Software & Technology

Government


Prevent fraud, waste, and abuse, improve program integrity, and deliver return on investment.

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Government

Defense & Intelligence


Detect and prevent threats, improve security, prioritize caseload, and mitigate risk.

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Defense & Intelligence

Case Studies

Government

US Postal Service


Elder Research partnered with the U.S. Postal Service Office of Inspector General to develop and deploy a custom solution to identify and prioritize questionable contracts and healthcare claims for investigation. Leads generated were 74% actionable, resulting in over $11 million in recoveries, restitutions, and cost avoidance in the first year.

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Commercial

DentaQuest


Elder Research developed a provider risk scoring model that enabled targeted intervention with low quality providers and reduced per patient cost by nearly 20 percent.

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Commercial

nTelos


By applying advanced techniques for modeling and visualizing customer records, Elder Research created a combined data and text mining solution to increase marketing efficiency and reduce churn. The model improved targeted messages which resulted in higher profitability for nTelos, a regional mobile phone carrier.

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Featured Clients & Partners

Events

News

Dr. Aric LaBarr Teaching a Course on Product Demand Forecasting in R at Datacamp

Director and Senior Scientist  Dr. Aric LaBarr designed and is the instructor for a course in Product Demand Forecasting in R at Datacamp. The course unlocks the process of predicting product demand through the use of R and students will learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example. 

Elder Research Team Wins Data Story Telling Component of Charlottesville Open Data Challenge

Anna Godwin, Halee Mason, Cory Everington, Danny Brady, and Kazlin Mason, aka Team HACK’D, won the Data Story Telling component of the Charlottesville Open Data Challenge. The team presented at the Tom Tom Founders Festival Machine Learning Conference as one of two finalist. Using open data, the goal of the Challenge was  to engage the growing data science community within Charlottesville and the surrounding areas to help the City better understand pedestrian use of the Downtown Mall. For the data storytelling portion, each team was asked to use the data to craft a narrative and visualizations that explain what is happening in the data (trends, anomalies, outliers, etc.). The goal of data storytelling is to enlighten members of the target audience to insights that would not be clear without charts or graphs. The team also finished in fourth place for the Best Predictive Model component of the challenge. View the winning story here.

Trent Bradberry, Ph.D. Receives Award from the Journal of Neural Engineering

Trent Bradberry, Ph.D., received an Outstanding Reviewer Award from the Journal of Neural Engineering. The annual award is given to reviewers who are judged by the editorial team to have provided high quality neuroscientific/engineering reviews of manuscripts under consideration for publication. Trent is one of only 24 award recipients for 2017 selected from an international community of reviewers. Journal of Neural Engineering was created to help scientists, clinicians and engineers to understand, replace, repair and enhance the nervous system.

Elder Research Sponsors Tom Tom Festival Applied Machine Learning Conference

Elder Research will be a Theme Sponsor for the Applied Machine Learning Conference on April 12, 2018 at the Violet Crown theater in downtown Charlottesville. Machine Learning is a technology that helps make sense of the massive amounts of data and, in today’s world, it is the key to survival for businesses.  This day-long conference will convene researchers, entrepreneurs, and practitioners who use big data and machine learning applications on a wide variety of topics.

EBooks

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Mining Your Own Business

This eBook includes Chapter 3 from industry experts Jeff Deal and Gerhard Pilcher’s book Mining Your Own Business, A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics. Chapter 3 titled “Leading a Data Analytics Initiative” covers the key challenges and considerations for business leaders employing analytics to provide data-drive insight.

The Ten Levels of Analytics

Every technical project involves some sort of analytics, ranging from simply reporting key facts, to predicting new events. In this eBook we define ten increasingly sophisticated levels of analytics so that teams can assess where they stand and to what they aspire. The eBook clarifies definitions of three types of analytic inquiry and four categories of modeling technology and illustrates these levels with examples using tabular data representations commonly found in spreadsheets and single database tables. Additionally, the Levels are extended to encompass emerging data types such as time series, spatial data, and graph data, by providing data complexity as second dimension for categorization alongside algorithmic sophistication.

Top 10 Data Mining Mistakes

In two decades of mining data from diverse fields, we have made many mistakes, which may yet lead to wisdom. In this eBook, we briefly describe, and illustrate from examples, what we believe are the “Top 10” mistakes of data mining, in terms of frequency and seriousness. Most are basic, though a few are subtle. All have, when undetected, left analysts worse off than if they’d never looked at their data.

Resources

Featured Video

Gerhard Pilcher discussed the gap between the promise of analytics to transform a company and the actual results, and how adaptability and intuition by leadership can help close that gap.

Target Shuffling is a process for testing the statistical accuracy of data mining results. It is particularly useful for identifying false positives, or when two events or variables occurring together are perceived to have a cause-and-effect relationship, as opposed to a coincidental one. 

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Book Overview

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Mining Your Own Business is a practical guide for organizational leaders and top-level executives that demonstrates how to harness the power of data mining and predictive analytics, and avoid costly mistakes.

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