As of today, the Houston Astros (at 2-1) are above .500 in wins and losses for the first time since July 2009. So we celebrate that most important of baseball metrics, the wins and losses.

One of the joys of baseball is the almost innumerable metrics available to the fan, fantasy league manager or just plain stat freak. Batting Average (BA), On-Base Percentage (ONP), Slugging Percentage (SP), On-Base Slugging Percentage (OBSP), and Earned Run Average (ERA) are but a few. Indeed there are full glossaries to define the various statistical measurements used in baseball now. The use of statistics in baseball was made most popular by Bill James and his development of his work, “Baseball Abstract” and more recently popularized in the book by Michael Lewis made into the Oscar-nominated movie “Moneyball” starring Brad Pitt.

However, in the world of Foreign Corrupt Practices Act (FCPA) and other anti-corruption compliance metrics, many compliance practitioners are still in the infancy of using statistics to help inform and measure a compliance program. A recent article in the April 2012 issue of the Harvard Business Review, entitled “Good Data Won’t Guarantee Good Decisions” by Shvetank Shah, Andrew Horne and Jaime Capella, explores this issue and comes to a conclusion which bedevils many compliance practitioners. It is that “most companies have too few analytic-savvy workers.” The article then goes on to provide guidance on how to develop them. The authors begin by identifying four main problems which they believe prevent companies from utilizing the data that they generate.

Four Problems

1.      Analytics skills are concentrated in too few employees within the company. The authors note that “when a new form of analytics enters the workplace” companies will hire experts to manage and interpret it. They somehow believe that this expert knowledge will ‘trickle down’ throughout the ranks of the company. However, the experts who install the technology to generate the data may not even train before their consulting assignment is over. Even if they put on training, company employees may well not use the technology or data very often and the training may not be retained. If the experts are not kept onboard for ongoing mentoring or data interpretation, the company will not know how to use the data.

2.      Company IT functions need to spend more time on the “information” quotient of IT and less time on the “technology” quotient. The authors believe that most IT functions were developed in conjunction with Finance, Human Resources (HR), Supply Chain or other departments within an organization “where the business needs are clearly defined, stable and relatively consistent over a wide group of users.” Though, in other groups or departments such as compliance, there may be diverse data or a group of compliance practitioners which cannot fully articulate their specific needs. The authors believe that most IT departments cannot meet such “anthropological skills and behavioral understanding.”

3.      The problem is not that there is too much information but that it is too hard to locate. Even if IT can collect the appropriate data, the authors believe that many within organizations do not have a “coherent, accessible structure for the data” which the company has collected. They liken the situation as one similar to a “library without a card catalogue” (how’s that for old school!). Even with the rise of mobile computing platforms such as tablets or smartphones, it is now harder to manage analytic content.

4.      Senior management is not trained to manage information as well as they are trained to manage talent, capital and brand. Here the authors seem to be the most critical. They almost scream out when they say, “Management needs to wake up to the fact that their data investments are providing limited returns because their organization is under-invested in understanding the information.” They believe that too few senior managers treat data as something that the company’s IT department should handle and analyze. Conversely senior management considers its time too valuable to make sure that the appropriate information is shared appropriately across the organization.

Three Prescriptions

Does any of the above sound like problems in your organization? The authors deliver three prescriptions which they believe can help to overcome the issues that they identify as impediments to the use of data analytics. First and foremost is training. The authors believe that companies should spend more time and money training employees on the use of statistics. Recognizing that almost anyone with any type of business degree from a college or university had some type of statistics course, the authors believe that companies should offer refreshers or build upon this base. But they point out that training should not end with classroom courses or refreshers. Ongoing coaching is equally important to provide follow up and answer the inevitable questions that arise in the day-to-day use of new analytics.

The second change that the authors urge companies to make is how to “more efficiently incorporate information into decision making.” They point out that some of the best data-driven companies “have formalized the decision-making process, setting up standard procedures so that employees can obtain and correctly use the most appropriate data.” This should be reinforced by a company through its rewards system in the form of employee reviews and specific job objectives but a key is make certain employees are not penalized for making “diverse contributions, challenges and second-guesses” to data.

The third prescription is for a company to provide its employees with the right tools to use the data. The authors believe that “half of all employees find that information from corporate sources is in an unusable format.” The authors believe that the best practice for a company is to employ improved data “filtering and better visualization.” However, the authors caution that whatever tools are used, the unfiltered data needs to be available if an employee wants or needs to drill down into the raw data.

The authors have presented a framework which a Compliance Department can begin to think about what metrics it wants to evaluate, how to set up a program to train the department’s compliance practitioners to use the data. A Compliance department needs to ensure that it provides closure for any gaps that it might have in the interpretation of data and to keep pace with the information which it imports into its compliance program. If you need more help, consider reading “Moneyball” to see how the Oakland Athletics’ used analytics to help build a record setting team.

This publication contains general information only and is based on the experiences and research of the author. The author is not, by means of this publication, rendering business, legal advice, or other professional advice or services. This publication is not a substitute for such legal advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified legal advisor. The author, his affiliates, and related entities shall not be responsible for any loss sustained by any person or entity that relies on this publication. The Author gives his permission to link, post, distribute, or reference this article for any lawful purpose, provided attribution is made to the author. The author can be reached at

© Thomas R. Fox, 2012