Just as he did in 2013, in 2015 and in 2016, Kenyan-born British cycling star Chris Froome crossed the finish line in Paris wearing the Yellow Jersey as this year’s winner of the Tour de France. As reported by Chris Chavez, in Sport Illustrated online journal, “He took the victory without winning any of the 21 stages. He is Britain’s first four-time winner of the Tour. Froome previously won the Tour in 2013, 2015 and 2016.” So, for the fourth time, a tip of the cycling helmet to Chris Froome.
Chavez further noted that Froome “is the fifth rider to win four Tour de France titles and next year he could join Jacques Anquetil, Eddy Merckx, Bernard Hinault and Miguel Indurain as the five-time champions.” While he has certainly not been as flashy as many champions or even stage winners he has done the work to be the Sky Team leader and now four-time champion. I thought about Froome’s win as I read an article in today’s New York Times (NYT) opinion section, entitled “Let’s Get Excited About Maintenance!”, by Andrew Russell and Lee Vinsel. The article began with the current state of infrastructure in the United States, citing a wide variety of public transit, roads, bridges, airports and sewers all of which the authors described as being in “decrepit condition.”
Yet the authors did not focus on the lack of Presidential or Congressional action to fund such projects as the issue. They focused on “a deeper problem, one that is too seldom discussed: Americans have an impoverished and immature conception of technology, one that fetishizes innovation as a kind of art and demeans upkeep as mere drudgery. When Americans talk about technology, they often use “innovation” as a shorthand. But “innovation” refers only to the very early phases of technological development and use. It also tends to narrow the scope of technology to digital gadgets of recent vintage: iPhones, social media apps and so on. A more expansive conception of technology would take into account the diverse array of tools, including subways and trains, that we humans use to help us reach our goals.”
The authors go on to point out that there is a large financial benefit to the maintenance profession as “corporations like General Electric and Boeing make heavy investments in tools and procedures for predictive maintenance, since their success depends on the reliability of their products and the existence of orderly routines to follow when things break down. Even in the digital industries, where the gospel of innovation is sacrosanct, the kings of “disruption” — Netflix, Amazon — keep their customers happy only through reliable and well-maintained data and distribution networks.”
All of this is significant to the compliance practitioner. One of the areas of inquiry in the Department of Justice’s (DOJ’s) Evaluation of Corporate Compliance Programs (Evaluation) is Continuous Improvement, Period Testing and Review. The Evaluation asked whether or not the company had taken the information obtained through auditing, monitoring and testing and used it to update the compliance program going forward.
One way to consider using the information you obtain to engage in continuous improvement is to use some of the concepts laid out in an eBook, entitled “Planning for Big Data – A CIO’s Handbook to the Changing Data Landscape”, by the O’Reilly Radar Team, with a chapter by Alistair Croll, entitled “The Feedback Economy”, which I have adapted for the compliance practitioner.
It is based on the concept that information itself is not the greatest advantage but using that information to prevent, detect and remediate is. Croll draws on military theory to illustrate his concept of a feedback loop. It is the OODA loop, which stands for observe, orient, decide and act. The success of OODA is in large part “the fact it’s a loop” so that the results of “earlier actions feedback into later, hopefully wiser, ones.” For the Chief Compliance Officer (CCO) or compliance practitioner this means that if your compliance program is able to collect and analyze information better and you can act on that information faster; you can then use it maintain your compliance program.
The first step is data collection about your compliance program. Once the data comes in it must be ingested and cleaned. If it comes into your organization in an unstructured format, you will need to cut it up and put into the correct database format for use. A key insight from Croll is the issue of platforms, which are the frameworks used to crunch large amounts of data more quickly. His key insight is to break up the data “into chunks that can be analyzed in parallel” so the data can be considered and acted upon more quickly. Another technique he considers is “to build a pipeline of processing steps, each optimized for a particular task.”
Another important component is machine learning and its importance in the data supply chain. Croll observes, “we’re trying to find signal within the noise, to discern patterns. Humans can’t find signal well by themselves. Just as astronomers use algorithms to scan the night’s sky for signals, then verify any promising anomalies themselves, so too can data analysts use machines to find interesting dimensions, groupings or patterns within the data. Machines can work at a lower signal-to-noise ratio than people.”
Yet Croll correctly notes that as important as machine learning is in big data collection and analysis, there is “no substitute for human eyes and ears.” Yet for many CCOs or compliance practitioners, displaying the data is most difficult because it is not generally in a readable form. To say lawyers are not as proficient as other corporate types in excel or similar tools would be to state the obvious, yet that is about as sophisticated as many practitioners can get. It is important to portray the data in more visual style to help convey the “dozens of independent data sources” into navigable 3D environments.
Of course, having all this data is of zero use unless you act on it. This is a key insight from the Evaluation; that you must use the data to maintain your compliance program. But it does take a shift in compliance thinking to use such data. Once again lawyers are particularly ill suited to consider such information for reasons as diverse as training and temperament. This is but another reason why compliance has evolved to Compliance 2.0, Compliance 3.0 and beyond. Big data allows you to make a quicker assessment of the impact of measured risks. It advocates “fast, iterative learning.”
Croll ends his chapter by noting that the “big data supply chain is the organizational OODA loop.” But unlike the OODA loop, it is more than simply about the loop and plugging information as you move through it. He believes “big data is mostly about feedback”; that is, obtaining the impact of the risks you have accepted. For this to work in compliance, a company’s compliance discipline needs to both understand and “choose a course of action based upon the results, then observe what happens and use that information to collect new data or analyze things in a different way. It’s a process of continuous optimization”.
As you work to operationalize your compliance program, use the Evaluation to begin to ask questions. Then layer over the concept of maintenance as not only prolonging the lifecycle of your compliance program but making it more robust and effective. In the case of Froome, this type of approach has led to the rarified air of four Yellow Jerseys. As Russell and Vinsel noted in their NYT piece, maintenance can be the key to a successful infrastructure program. Finally, never forget that compliance is a part of the infrastructure of your organization and it must be properly maintained.
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