By Christina Ho and Trent Bradberry

Peter Drucker, who was recognized as the world’s greatest management consultant, famously said, “If you can’t measure it, you can’t improve it.” This quote is relevant for addressing the Environmental, Social, Corporate Governance (ESG) challenge.

The History of ESG

ESG are non-financial factors used to assess the sustainability and societal impact of an investment. It represents a fundamental shift from the 20th to the 21st century in how social responsibility is perceived to impact a company’s financial performance.


by Jordan Barr, Lead Data Scientist, Elder Research

Introduction

Have you ever built a predictive model only to discover that your best result was not good enough to deploy? The threshold for “good” usually depends on the situation. It could mean, “Will we make a profit if we deploy this model?” or “Are we likely to enjoy a more favorable outcome by implementing this model?” You likely employed one or more of the most popular algorithms such as decision trees, logistic regression, or even random forests only to discover that none of them met your performance requirement when evaluated on unseen…


by Gerhard Pilcher

Data Scientist is an unusual term. If you “Google” the words, data means “facts and statistics gathered together for reference or analysis” and scientist means “a person who is studying or has expert knowledge of one or more of the natural or physical sciences.”

Dr. Michael Rappa, founder of the Institute of Advanced Analytics at NC State University, prefers the term Analytic Professional, which I think better captures the role of a person that experiments with the application of analytic algorithms on data to enhance the mission or business value of information.

Dr. John Elder, founder of…


by Colin Thomas

Data Engineering is the discipline of designing, building, and maintaining a robust infrastructure for collecting, transforming, storing, and serving data for use in machine learning, analytic reporting, and decision management.

Managing a successful data engineering project requires balancing three different aspects: pragmatism, principles, and practice:

  • The pragmatic aspect considers the constraints of a project, such as budget, existing tools, and minimum viable solutions.
  • The principled aspect focuses on fully understanding the problem and developing robust solutions that follow best practices.
  • The practiced aspect draws on expertise derived from repeated application of a canonical set of techniques to…


By John F. Elder and Dean Abbott

An interview with Dean Abbott and John Elder about change management, complexity, interpretability, and the risk of AI taking over humanity.

After the KNIME Fall Summit, the dinosaurs went back home… well, switched off their laptops. Dean Abbott and John Elder, longstanding data science experts, were invited to the Fall Summit by Michael Berthold to join him in a discussion of The Future of Data Science: A Fireside Chat with Industry Dinosaurs. The result was a sparkling conversation about data science challenges and new trends. Since switching off the studio lights, Rosaria Silipo…


by William Goodrum, Ph.D.

A 25-Year History of Innovation

Data Science and Machine Learning have been top topics in recent years in popular media and the private and public sectors, which Elder Research, as a pioneer in these fields, has welcomed. In the early years, it was rare to encounter people who fully appreciated the power of the technology. By focusing on delivering high-quality services and top value to our clients, rather than the latest technology itself, we built our firm’s reputation, as well as that of DS/AI/ML. Those who are new to Data Science may run into the hype and overinflated results that can…


by John F. Elder

Editor’s Note: John Elder has occasionally joked that the risk of pulling in a PhD to help with a problem is it will inspire them to work on one even more interesting. Today, he became that person. He was originally asked to help with a simple question on a Request For Information (RFI) about defining metrics of success for a project. Our colleague requesting aid originally thought the request was a bit general, as in “how would we develop metrics for a new problem?”. By the time they realized the RFI’s goal was very specific (e.g…


Arguably the most vital modeling phase is validation; the model has to work on new, never-before-seen data or it is worthless. The problem is much greater than most researchers are aware. Most experiments — indeed, most published scientific papers based on inducing results from data — are believed to be irreproducible; that is, they can’t be verified (get similar results) when independently repeated by a different team following the same procedures. (See, for instance, the landmark paper by JPA Ioannidis (2005) “ Why Most Published Research Findings are False”, PLoS Med 2(8): e124.) In fact, in 1995 Science magazine picked…


by John Elder, Ph.D., Founder & Chairman, Elder Research

The blog Recidivism, and the Failure of AUC published on Statistics.com showed how the use of “Area Under the Curve” (AUC) concealed bias against African-Americans defendants in a model predicting recidivism, that is, which defendants would re-offend. There, a model varied greatly in its performance characteristics depending on whether the defendant was white or black. Though both situations resulted in virtually identical AUC measures, they led to very different false alarm vs. false dismissal rates. …


by Will Goodrum, Ph.D., Director of Research and Development, Elder Research

Your company has made it a strategic priority to become more data-driven. Good! A major anticipated component of this transition is to implement new data technology (e.g., a data lake). Resources are thrown at identifying source systems and pulling information into a new, analytically-focused data repository or an even bigger data lake. Time is spent creating an ETL pipeline to move data from one place to another. Web endpoints are created to facilitate access for data customers. Dashboards are created that show information available in this centralized and optimized…

Elder Research, Inc.

A leading consulting company in data science, machine learning, and AI. Transforming data and domain knowledge to deliver business value and analytics ROI.

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