Should data analytics be used as a tool to uncover new insights from company data, or should it be used to answer or solve a specific business inquiry or problem? This question is central to any analytics project design. But like most important questions, the answer depends on several factors and is not altogether clear.

As terms like “big data”, “data science” and “artificial intelligence” continue to bleed into many aspects of our lives, a lot of companies are excited and more than willing to jump on to the data analytics highway, often without clear direction or purpose. The notion is that even without clear direction, a seasoned data scientist can use sophisticated analytical tools to uncover powerful business insights from web-scale data and internal caches, and in turn, drive company success. While such an outcome is possible, it is not likely.

Many successful analytics endeavors begin with a specific business question or problem in hand. Quantitative tools are then used to efficiently and accurately answer the question or solve the problem. A more precise understanding of the business problem immediately informs the analytics team about what data are needed to arrive at a solution. This small bit of clarity not only helps direct data collection, data maintenance, and data generating initiatives, it also ensures that your company has the data required to quantitatively address some of the most important and pertinent business needs for the foreseeable future.

But for companies looking to use data as a way to ask new questions or to discover unexamined business problems, exploratory data analysis may be valuable. While perhaps risky, analytics projects and initiatives designed to generate more questions than they answer can lead to unexpected knowledge, and valuable business insight. For example, if a company is unaware of costly staffing inefficiencies, exploratory data analysis is one of the few ways to unexpectedly illuminate the issue, and at the same time provide a solution. If problems are never identified, then they can never be resolved.

So, should analytics be used to solve a specific problem, or should it be used to uncover new insights? Of course, the answer is that both avenues can be valuable, and they serve different purposes and pose different risks and rewards. We can think of the dichotomy in the context of a highway, let’s call it our data highway.  Imagine getting in your car and driving along the highway without a clear destination or purpose. Along the way, you might see some new and interesting things. A new restaurant, a new park maybe. You might also see nothing at all of interest, and your time might have been wasted. What’s worse is that you’ve already paid for the gas. If you had started your journey on the data highway with clear destination, you might eventually get there. If there are road blocks, at least you’ve identified them and can, as a consequence, chart alternative routes.

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Photo of Eric J. Felsberg Eric J. Felsberg

Eric J. Felsberg is a Principal in the Long Island, New York, office of Jackson Lewis P.C. and the National Director of JL Data Analytics Group.

As the National Director of JL Data Analytics Group, Mr. Felsberg leads a team of multi-disciplinary lawyers…

Eric J. Felsberg is a Principal in the Long Island, New York, office of Jackson Lewis P.C. and the National Director of JL Data Analytics Group.

As the National Director of JL Data Analytics Group, Mr. Felsberg leads a team of multi-disciplinary lawyers, statisticians, data scientists, and analysts with decades of experience managing the interplay of data analytics and the law. Under Mr. Felsberg’s leadership, the Data Analytics Group applies proprietary algorithms and state-of-the-art modeling techniques to help employers evaluate risk and drive legal strategy. In addition to other services, the team offers talent analytics for recruitment, workforce management and equity and policy assessments through predictive modeling, partners with employers in the design of data-driven solutions that comply with applicable workplace law, manages and synthesizes large data sets from myriad sources into analyzable formats, provides compliance assessment and litigation support services including damage calculations, risk assessments, and selection decision analyses, and offers strategic labor relations assistance including determination of long term costs of collective bargaining agreements, review of compliance with collectively bargained compensation plans and assessment of the efficacy of training programs. The JL Data Analytics Group designs its service delivery models to maximize the protections afforded by the attorney-client and other privileges.

Mr. Felsberg also provides training and daily counsel to employers in various industries on day-to-day employment issues and the range of federal, state, and local affirmative action compliance obligations. Mr. Felsberg works closely with employers to prepare affirmative action plans for submission to the Office of Federal Contract Compliance Programs (OFCCP) during which he analyzes and investigates personnel selection and compensation systems. Mr. Felsberg has successfully represented employers during OFCCP compliance reviews, OFCCP individual complaint investigations, and in matters involving OFCCP claims of class-based discrimination. He regularly evaluates and counsels employers regarding compensation systems both proactively as well as in response to complaints and enforcement actions.

Mr. Felsberg is an accomplished and recognized speaker on issues of workplace analytics and affirmative action compliance.

While at Hofstra University School of Law, Mr. Felsberg served as the Editor-in-Chief of the Hofstra Labor & Employment Law Journal.