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Investment Modeling Grounded In Data Science

John Elder, Ph.D.

July 26, 2019

BLOG_Investment Modeling Grounded In Data Science

Elder Research has solved many challenging and previously unsolved technical problems in a wide variety of fields for Government, Commercial and Investment clients, including fraud prevention, insider threat discovery, image recognition, text mining, and oil and gas discovery. But our team got its start with a hedge fund breakthrough (as described briefly in a couple of books1,2), and has remained active in that work, continuing to invent the underlying science necessary to address what is likely the hardest problem of all: accurately anticipating the enormous “ensemble model” of the markets.

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The Persuasion Paradox – How Computers Optimize their Influence on You

Eric Siegel

July 5, 2019

DR. Data Show

How do computers optimize mass persuasion – for marketing, presidential campaigns, and even healthcare? And why is there actually no data that directly records influence or persuasion, considering it’s so important? And what’s the ideal technique for optimizing your dating life and for getting more people to wash their hands in public restrooms? It’s time for Dr. Data’s persuasion paradox “Groundhog Day”-inspired geeksplanation.

Persuasion modeling requires a “deep geek dive” – but it’s as important as it is fascinating.


Note: This article is based on a transcript of The Dr. Data Show episode. View the video here.


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Healthcare Analytics: Exploration vs. Confirmation

Peter Bruce

June 21, 2019

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Perhaps the most active application of analytics and data mining is healthcare.  This week we look at one success story, the use of machine learning to predict diabetic retinopathy, one story of disappointment, the use of genetic testing in a puzzling disease, and a basic dichotomy in statistical analysis.

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Data is Not Oil. It is Land.

Will Goodrum, Ph.D.

June 7, 2019

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It has become common to talk about data being the new oil. But a recent piece from WIRED magazine points out problems with this analogy. Primarily, you must extract oil for it to be valuable and that is the hard part. Framing data as oil is not illuminating for executives trying to value their data assets. Oil is valuable, marketable, and tradable. Without significant effort, data is not. Data has more in common with land that may contain oil deposits than it does with oil.

Framing data as a real asset may help executives understand its value.

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Tiger vs. Jack – Asking the Right Questions

Daniel Brannock

May 24, 2019

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One of the most fundamental contributions we can make as consultants is to help our clients ask the right questions of their data. We’re often asked to help solve problems that turn out to be too broadly or too narrowly defined—or are not aligned with achieving business goals or eliminating pain points.

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A Deep Dive into Deep Learning

Peter Bruce

May 10, 2019

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On Wednesday, March 27, the 2018 Turing Award in computing was given to Yoshua Bengio, Geoffrey Hinton and Yann LeCun for their work on deep learning. Deep learning by complex neural networks lies behind the applications that are finally bringing artificial intelligence out of the realm of science fiction into reality. Voice recognition allows you to talk to your robot devices. Image recognition is the key to self-driving cars. But what, exactly, is deep learning?

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Discovering The Efficacy Of A New Drug

John Elder, Ph.D.

April 26, 2019

BLOG_Discovering The Efficacy Of A New Drug

The company had invested hundreds of millions of dollars investigating a new potential drug to treat a mental ailment, and they had zeroed in on a compound that showed promise but the compound was not passing the statistical tests required by the FDA. Elder Research was hired to examine the data and determine the drug’s viability. 

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What's Brewing at Predictive Analytics World

Paul Derstine

April 12, 2019

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Join us in Las Vegas on June 16-20 for the largest Predictive Analytics World event to date, Mega-PAW, with seven tracks of sessions covering the commercial deployment of machine learning across industry sectors. The only conference of its kind, Predictive Analytics World delivers vendor-neutral sessions across verticals such as banking, financial services, e-commerce, entertainment, government, healthcare, manufacturing, high technology, insurance, non-profits, publishing, and retail.

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Automating Network Entity Detection

Ryan McGibony

April 5, 2019

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Elder Research developed an automated data pipeline to cleanse data and feed a data visualization tool used to identify and explore document preparer network relationships. The solution enabled the client to automate significant portions of work, make data-driven decisions, prioritize resources, and gain new business value from the data.

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Can Data Analytics Really Deliver 1300% ROI?

Paul Derstine

March 29, 2019

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With the right business problem, data, and applied techniques predictive analytics can deliver exceptional return on investment (ROI) by, for example, assessing and managing risk, detecting and preventing fraud, optimizing workflow and business processes, and prioritizing resources.  Predictive analytics provides business leaders with data-driven insight to determine which customers to contact for marketing, which tax returns to audit, which debtors to approve for increased credit limits, which patients to clinically screen, which customers are likely to leave, which persons of interest to investigate, and which equipment to inspect for impending failure.

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Do We Have the Right Data?

Jeff Deal

March 22, 2019

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In our experience the mistake of “waiting for perfect data” probably kills more projects than any other. Here’s a typical scenario:

The project starts out well. The management team defines the goals, calculates the potential return on investment, develops a project plan, gets a budget approved, assembles the team, and launches the project. The trouble starts with a desire to make sure that the data is in “good” condition. 

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Transaction Classification Aids Credit Risk Assessment

Carlos Blancarte

March 15, 2019

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A significant transformation is currently underway in the lending market. Banks are competing to provide lending decisions in a single day, with a vastly simplified customer experience as their primary way of growing market share. The key technology allowing this transformation is being able to accurately automate the credit decision; that is, to use advanced analytics to estimate a customer’s probability of default, affordability, and financial position to make the credit decision quickly.

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RADR: A Powerful Visual Risk Analytics Tool

Victor Diloreto

March 8, 2019

BLOG_RADR-A Powerful Visual Risk Analytics Tool

The Risk Assessment Data Repository (RADR) is a powerful risk analytics platform used to enhance productivity in the investigation of fraud, waste and abuse. This server-based, data analytics product  fuses data from multiple sources, with sophisticated predictive and machine learning risk modeling, and an intuitive visual interface. RADR enables proactive identification of risk—namely fraud, waste, and abuse behaviors—and simplifies the investigative process. RADR provides visualizations for risk propensity and their related data so that managers, auditors, investigators, and analysts can easily access data on high-risk items and focus on the highest ROI cases.

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Data Engineering with Discipline

Victor Diloreto

March 1, 2019

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As a data science consultancy, we frequently run into difficult data infrastructure challenges at our clients across multiple industries. To solve a business problem or get decision-making insights from data, we often must start by helping to clean up and organize the data architecture so we can build data science and machine learning (ML) models. This process of getting the data ready for the application of the science is called data engineering.

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5 Key Reasons Why Analytics Projects Fail

Peter Bruce

February 22, 2019

BLOG_5 Key Reasons Why Analytics Projects Fail

With the news full of so many successes in the fields of analytics, machine learning and artificial intelligence, it is easy to lose sight of the high failure rate of analytics projects.  McKinsey just came out with a report that only 8% of big companies (revenue > $ 1 billion) have successfully scaled and integrated analytics throughout the organization. In some ways, the very notable successes of analytics and data science contribute to the high failure rate, as ill-prepared organizations flock to implement projects.  There are various reasons for failure, and all are instructive.

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