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The Power of Open Data and Crowdsourcing Analytics

Paul Derstine

April 20, 2018

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Crowdsourcing, a combination of “crowd” and “outsourcing” first coined by Wired magazine in 2005 and fueled by the Internet, is a powerful sourcing model that leverages the depth of experience and ideas of a public group rather than an organizations own employees. In The Importance of CrowdSourcing Matt H. Evans points out that “Crowdsourcing taps into the global world of ideas, helping companies work through a rapid design process. You outsource to large crowds in an effort to make sure your products or services are right.” The advantages of using crowdsourcing are claimed include improved costs, speed, quality, flexibility, scalability, or diversity. It has been used by start-ups, large corporations, non-profit organizations, and to create common goods. Wikipedia maintains a list of crowdsourced projects.

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Get Ready for Analytics Summit 2018

Paul Derstine

April 13, 2018

 

BLOG_Analytics Summit 2018Elder Research will participate in the 7th annual Analytics Summit 2018, Hosted by The University of Cincinnati Center for Business Analytics on May 14th-16th, 2018 at the Sharonville Convention Center in Cincinnati, Ohio. This year’s event will feature three high-profile keynote speakers, four technical one or two-day training sessions, one managerial half-day forum, and five analytics tracks with three presentations each.

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What is Data Wrangling and Why Does it Take So Long

Mike Thurber

April 6, 2018

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Data wrangling is the process of gathering, selecting, and transforming data to answer an analytical question.  Also known as data cleaning or “munging”, legend has it that this wrangling costs analytics professionals as much as 80% of their time, leaving only 20% for exploration and modeling. Why does it take so long to wrangle the data so that it is usable for analytics?

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Choosing the Right Analytics Problem

Miriam Friedel

March 30, 2018

 

BLOG_Choosing the Right Analytics ProblemIf your organization is new to data science and predictive analytics, it can be difficult to know where to start. In our two decades of experience at Elder Research, we have found that there is often a mismatch between what companies think they should do with analytics versus what will provide the most value. While the specific problem to tackle varies by industry and business, we have found that choosing the right problem and focusing on a few key guidelines at the outset helps us deliver business value and gain support for analytics from key stakeholders.

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Improving Unemployment Insurance Claim Fraud Detection

Isaiah Goodall

March 23, 2018

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The U.S. unemployment insurance (UI) system is run and funded primarily by the individual states with oversight and support from the U.S. Department of Labor. Individual state UI programs are entrusted with ensuring benefits are paid promptly and accurately to eligible claimants, preventing improper payments (both over- and under-payments), and ensuring that employers properly classify their workers and pay their contributions promptly and accurately.

Elder Research designed and deployed an automated fraud detection solution for the New York Department of Labor Unemployment Insurance Integrity Center of Excellence. The tool was estimated to have identified 1200 claims annually before the current investigative process with annual projected savings of $972,000 in recoverable and nearly $392,000 in non-recoverable overpayments.

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Building a High-Functioning Analytics Team

Cory Everington

March 16, 2018

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This is the third in a series of blogs where Data Scientists Cory Everington and Anna Godwin discuss five Analytics Best Practices that are key to building a data-driven culture and delivering value from analytics. In this installment Cory discusses the benefits of team building and its impact on successful data science projects.

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Goodhart’s Law, Evolving Threats, and Model Monitoring

Stuart Price, Ph.D.

March 9, 2018

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In 1975 Charles Goodhart, a chief economic advisor of the Bank of England, posited “When a measure becomes a target, it ceases to be a good measure”. This idea came to be known as Goodhart’s Law and is recognized as a risk associated with key performance indicators (KPI) and implementing analytics. Any metric applied to a competitive or adversarial system will change behavior if it is perceived to make decisions that affect the system. If your adversary has a good chance of figuring out your metric, how can you keep your system from being gamed? 

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Analytics Assessment: Blueprint for Effective Analytics Programs

Robert Pitney

March 2, 2018

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If you are like me, when you first heard of analytics and its ability to benefit businesses, non-profits, and government agencies, you felt invigorated and excited, having to hold back the urge to shout “Charge!”  As a data scientist, I found this excitement to be well-founded: analytics powerfully leverages data to address important, existential questions facing any organization: 

  • Are we effectively meeting our customers’ needs?
  • Are there more efficient ways to reach our strategic goals?

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Credit Models are Winning and I’m Keeping Score!

Aric LaBarr, Ph.D.

February 23, 2018

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Classification scorecards are a great way to predict things because the techniques used in the banking industry specialize in interpretability, predictive power, and ease of deployment. The banking industry has long used credit scoring to determine credit risk—the likelihood a particular loan will be paid back.  A scorecard is a common way of displaying the patterns found in a classification model—typically a logistic regression model. However, to be useful the results of the scorecard must be easy to interpret. The main goal of a credit score and scorecard is to provide a clear and intuitive way of presenting regression model results. This article briefly discusses what scorecard analysis is and how it can be applied to score almost anything.

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Top Mistakes when Backtesting Investment Strategies

John Elder, Ph.D.

February 16, 2018

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A market index provides a tough hurdle to beat for any investment strategy. Employing an index is almost always better than a strategy that systematically picks a subset of its space or time (i.e., does portfolio-picking, or market timing ). The cost of a predetermined index is low, since no thought is required, and the long-term results over the last century of major market indices have been impressive.  So, the argument goes:  “Why waste your money paying for expensive managers who may only beat the market by luck?”

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Be a Data Detective

Ryan McGibony

February 9, 2018

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You’ve probably heard it before – analytics professionals working directly with data spend as much as 80% of their time on data preparation, leaving only 20% for actual analytics and modeling. There are several common terms for the activities making up this 80%, including data “cleaning,” “wrangling,” or “munging,” with perhaps the highest-profile example being “data janitor work,” as discussed in The New York Times. The consensus seems to be that this work is undesirable, a necessary evil we must endure to get to the “cool” parts of data science. The practitioners quoted in the Times article lament the countless hours they pour into data prep, and the author entices the reader with the possibility of automating the process. While anyone who works in predictive analytics would welcome the chance to cut down on prep work, we should consider the downsides of adopting this attitude in the practice of data science.

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Surf’s Up: Riding the Big Data Wave

Will Goodrum, Ph.D.

February 2, 2018

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Surfing requires a combination of skill, balance, strength, and awareness. A surfer only has so much control over where they are headed. It’s less about a specific destination, and more about catching the wave and seeing where it takes you.

Solving problems with data is (surprisingly) a lot like surfing. If the data and problem goal do not match, it is like trying to point a surfboard straight toward the shore — it won’t likely take you where you want to go. So, like deciding which wave to ride, how do you know if you’ve picked the right problem?

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Fraud Analytics: Tech Can Make Fraud Detection Affordable for SMEs

Harpreet Singh Dua

January 26, 2018

BLOG_Fraud Analytics-Tech Can Make Fraud Detection Affordable for SMEs-1.jpgFraud analytics is an emerging tool of the 21st century as it relates to detecting anomalies, red flags, and patterns within voluminous amounts of big data, which is quite challenging to analyze. The use of fraud analytics does not always have to be complex and costly for small and medium-sized enterprises (SMEs) to afford. While technology has played a key role in increasing opportunities to commit fraud, the good news is that it can also play a major role in developing new methods and strategies that can be used to detect and prevent fraud.

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Picking Favorites: A Brief Introduction to Selection Bias

Will Goodrum, Ph.D.

January 19, 2018

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In this series of short blog posts, we explore common biases that beset analytics projects. Bias can seriously impair the success of analytics in an organization, so understanding what to watch for is crucial. In this second post, we discuss a manifestation of one of the most prevalent and significant kinds of statistical biases: selection bias. We describe what it is, how it pervasive it may be, some specific examples of how it may manifest, and how to mitigate it.

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Why Every Business Needs Fraud Analytics

Paul Derstine

January 12, 2018

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From pharmaceutical, healthcare and financial claims, to insurance and product warranty claims, identifying and monitoring fraud is a priority for many organizations. Not only is a monetary loss at stake, but there is also potential damage to a company's brand, reputation, and trust. However, it can be a challenge for companies to understand and stay ahead of ever-evolving fraud risks and proactively identify and investigate active threats—let alone to navigate the new world of data analytics to assist in the process.

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