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Do We Have the Right Data?

Jeff Deal

March 22, 2019

BLOG_Do We Have the Right Data

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

BLOG_Transaction Classification Aids Credit Risk Assessment

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

BLOG_Data Engineering with Discipline

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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The Problem with Random Stratified Partitioning

Mike Thurber

February 15, 2019

 

 

BLOG_The Problem with Random Stratified Partitioning-1

When an organization invests in data science, they need to have confidence that the predictive models will be robust; that is, actually work when applied to future cases. However, this critical requirement is too often poorly addressed. Schoolbook answers are partly to blame. Consider this innocuous quote from Investopedia:

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Fraud, Anomaly Detection, and the Interplay of Supervised and Unsupervised Learning

Peter Bruce

February 8, 2019

 BLOG_Fraud, Anomaly Detection, and the Interplay of Supervised and Unsupervised Learning-2

Mike Thurber, Lead Data Scientist and fraud specialist at Elder Research, presented Elder Research's fraud detection methodology at Predictive Analytics World for Government last year. Consider the scenario of detecting fraudulent insurance claims, such as the audacious "accidental" death scheme in the 1944 noir film Double Indemnity.

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Group Optimization – An Application of the Nash Equilibrium

Michael Lieberman

February 1, 2019

BLOG_Group Optimization_Nash Equilibrium

The Nash Equilibrium—see A Beautiful Mind—in economics and game theory is defined as a stable state of a system involving multiple participants, where no one can gain by a change of strategy if the strategies of the others remain unchanged. More simply, it is a maximized state where no other players will agree if one player changes her position. In terms of economics or business, it is the most equitable, though not always the most obvious, solution to a multi-party conflict.

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The Data Speak: The World Is Getting Better

Peter Bruce

January 25, 2019

Blog_Data Doesn't Lie

In the visualization below, which line do you think represents the United Nation’s forecast for the number of children in the world in the year 2100?

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Supervised vs. Unsupervised Machine Learning

Gerhard Pilcher

January 18, 2019

BLOG_Supervised vs. Unsupervised Machine Learning

Analytics and Machine Learning techniques are important decision-making tools. Analytics asks questions from data and gets answers, largely for predicting what is likely to happen.  But, it’s easy to get confused by terminology such as supervised and unsupervised learning. What exactly is unsupervised learning? How does it differ from supervised learning, and why are these terms important? 

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Why Data Literacy in the C-Suite Matters

Will Goodrum, Ph.D.

December 21, 2018

BLOG_Why Data Literacy in the C-Suite Matters

In one recent week, I heard about the need for increased “data literacy” from executives at a major insurance company, leaders at a large consumer packaged goods manufacturer, and senior administrators at a preeminent research university. All these leaders understand the need for a comprehensive data strategy and want to create one for their organizations. Yet, they are finding it difficult to effectively communicating their strategy to employees who are not used to working in a data-driven environment, and who don’t understand how to implement that strategy in their day-to-day work.

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A Strategic Guide to Developing Your Analytics Team

Ozan Ersoy

December 14, 2018

BLOG_A Strategic Guide to Developing Your Analytics Team-1

Good data scientists are hard to find! This is in spite of the rapid growth of University master’s degree programs and commercial boot camps. Those sources do produce a much-needed supply for the data scientist demand projected over the next decade. However, becoming a good data scientist takes more than what these programs can provide. 

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How to Automate Machine Learning Model Tuning

Trent Bradberry

December 7, 2018

BLOG_How to Automate Machine Learning Model Tuning-1

I’ve long heard that “a watched pot never boils,” but when I am heavily invested in the outcome of a process, I still tend to monitor it intently. In my impatience I begin to wonder if my attention is worse than useless, and is actually impeding the progress of the process! When considering machine learning models, it may be true. Let me explain.

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Top 3 Lessons Learned While Drinking from the Data Science Firehose

Sam Ballerini

November 30, 2018

BLOG_Top 3 Lessons Learned While Drinking from the Data Science Firehose

I had one hard requirement during my job search: wherever I ended up, I wanted to drink from the "data science firehose." I wanted to work alongside seasoned data scientists with diverse skillsets and an unadulterated passion for solving problems with data. I wanted to leave the office after my first day asking myself, “How in the world am I going to keep up with these people?” And that's exactly what I've gotten at Elder Research.

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Monte Carlo Simulation - a Venerable History

Peter Bruce

November 16, 2018

BLOG_Monte Carlo Simulation

One of the most consequential and valuable analytical tools in business is simulation, which helps us make decisions in the face of uncertainty.

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Simple, Attractive, and Wrong: An Introduction to Linearity Bias

Will Goodrum, Ph.D.

November 2, 2018

BLOG_Introduction to Linearity Bias

This blog is 3rd in a series of short posts where we explore common biases that can impair analytics projects.

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Top 3 Objectives Before Starting an Analytics Project

Gerhard Pilcher

October 26, 2018

BLOG_Top 3 Objectives Before Starting an Analytics ProjectUnderstanding the organization’s business objectives and requirements, converting this knowledge into a definition of a problem, and developing a preliminary plan to solve that problem is crucial to the successful application of analytics and machine learning. In order to construct a successful model, the data scientist must understand how the business functions and how it will use the data. Even the most technologically advanced analytics model will produce trivial and possibly misleading results if it is disconnected from the purposes and goals of the business.

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Machine Learning: It is a Mistake to Lack Relevant Data

John Elder, Ph.D.

October 19, 2018

BLOG_It's a Mistake to Lack Relevant DataIn his Top 10 Data Science Mistakes Dr. 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 #0 you will learn that the less probable the interesting event is, the more data it takes to obtain enough to generalize a model to unseen cases, and why some projects probably should not proceed until enough critical data is gathered to make them worthwhile.

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Finding Fraud When No Cases are Known

Carlos Blancarte

October 12, 2018

BLOG_Finding Fraud When No Cases are Known

Fraud detection is about finding needles in haystacks and requires reliably labeled instances of fraudulent (needle) and non-fraudulent (straw) behavior. A predictive model can be trained using these labels to learn the underlying patterns in the input variables that best separate fraud from non-fraud cases, and thereby estimate the fraud-likeness of any future case. Typically, the interesting cases are very scarce, in which case we might have to carefully up-sample the rare class and/or down-sample the abundant class to help the model pay enough attention to the rare class to be useful. But what do we do when labels are not just rare, but are completely absent?

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Hiring a Data Analytics Consultant

Jeff Deal

October 5, 2018

BLOG_Hiring Data Analytics Consultants-1In the earliest days of data analytics, our new clients would typically say, “Solve this problem for us.” As they saw the enduring power of analytics, their request then became, “We want to launch our own data analytics capability. Will you help us set it up?” However, growing an analytics capability from scratch is a huge challenge, and today more companies appreciate its difficulty. 

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Who Invented the Null Hypothesis?

Peter Bruce

September 28, 2018

BLOG_Who Invented the Null Hypothesis

For many students, statistics is a troublesome subject, and the root of that trouble can be traced to the concept of the null hypothesis. In these days of big data, machine learning, and predictive analytics, formal hypothesis testing has receded in relative importance. Nonetheless, it retains considerable inertia and ability to cause difficulty - even in data science circles.

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Workshops, Workshops, Workshops!

Paul Derstine

September 14, 2018

BLOG_PAWgov Workshops

Predictive Analytics World for Government, the premier analytics and AI conference for government, starts next week. Keynote speakers include David Williams, USPS Board of Governors, Tom Davenport, a world-renowned analytics thought leader, author, and industry expert, and Dr. John Elder, Chairman and Founder of Elder Research, author and analytics practitioner. Elder Research will teach three workshops at the conference, offering insight on Machine Learning Methods, Deadly Analytics Mistakes, and Data Science for Managers.

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Fluency in The Language of Data Models

Michael Lieberman

September 7, 2018

BLOG_Fluency in The Language of Data Models

My job as a data scientist and research strategist is getting easier. Over the past 50 years, statisticians have developed a number of practical models that are highly effective to explain consumer patterns and predict consumer behavior. As new forms of computing power and information technology provide every increasing descriptions of individual-level purchasing tendencies, these models offer great value for business managers.

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Data Science, Statistics, and the "Method of Moments"

Peter Bruce

August 31, 2018

 

BLOG_Data Science, Statistics, and the Method of Moments

I got my introduction to statistics via resampling, working with Julian Simon, an early resampling pioneer. Demonstrating this "brute force" computer method to my father, I saw that he was vaguely offended by its inelegance.

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Are We Using Machine Learning?

Gerhard Pilcher

August 24, 2018

BLOG_Are We Using Machine Learning

In the midst of a recent engagement an executive suddenly asked, “Are we using Machine Learning?”. This caught us off-guard; working in the field for many years, we use the “learning sciences” virtually every day to solve hard problems. Machine Learning (ML), Data Science (DS) and Artificial Intelligence (AI) are exciting and very powerful; still, we’re happy to use conventional techniques whenever they’re the best choice to solve the client’s challenge. 

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