T. E. Lawrence, known to the world as Lawrence of Arabia, died as a retired Royal Air Force (RAF) mechanic living under an assumed name. Lawrence is most famous for leading the Arab Revolt in World War I against the Turkish Empire. After the war, he wrote the Seven Pillars of Wisdom, published in 1927. However, by the 1930s, Lawrence retreated out of the public eye in what he termed a ‘mental suicide’. The legendary war hero, author and archaeological scholar succumbed to injuries suffered in a motorcycle accident and died on this date in 1935.

Lawrence’s role of bridging the British war effort with the Arabian goal of independence introduces today’s topic of bridging the data analytics gap in compliance by better aligning data teams to compliance. In a October 2020 MIT Sloan Review article, entitled “To Succeed With Data Science, First Build the ‘Bridge’”, authors Roger W. Hoerl, Diego Kuonen, and Thomas C. Redman posited “there is a designed-in structural tension between business and data science teams that needs to be recognized and addressed. Structural problems demand structural solutions, and we see a way forward through a data science bridge: an organizational structure and leadership commitment to develop better communication, processes, and trust among all stakeholders.” I have taken their concepts and placed them into the compliance context.

What I found perhaps most interesting about the authors approach was identifying the issue as a structure problem and therefore a structural solution would hold sway. Structural solutions do not always come to the forefront of the Chief Compliance Officer’s (CCO) mind when considering compliance issues. However, they are one more tool which can be used to improve the overall effectiveness of a compliance program and can certainly work to more fully operationalize a compliance program.

Interestingly, the problem began over 100 years ago with Thomas Edison. Edison believed that invention needed to be siloed from the factory. That dynamic is still in play with data scientists usually divorced from both business operations and compliance. The authors believe this is a critical mistake as “by separating the [data] lab from the factory, all too often the lab becomes isolated, making it the proverbial ivory tower.”

The authors believe that a powerful answer lies in creating a data science bridge, spanning this gap and connecting the data scientists and their work “resolving some of the essential strains and enabling the introduction of more, and more useful, data-driven innovations into” business operations and compliance. This data science bridge would have four major responsibilities:

• Developing and maintaining high-bandwidth, bidirectional communication channels between the data scientists and compliance. This includes developing a common language, identifying and clarifying which compliance innovations are most needed and ensuring that feedback is provided and understood.
• Developing and operating a process by which data analytic insights are provided to the compliance function. This may include embedding newly minted algorithms into compliance systems to more fully operationalize compliance.
• Selecting and allocating needed resources. People and funding would be assigned to prioritized innovative opportunities, including compliance.
• Building trust. Between the business operations and compliance, the data scientists and senior management, mitigating the tension created by any siloes.

The authors go to state that in order to build a sturdy, sustainable bridge, there are several questions that organizations must ask themselves. A starting point is reaching agreement on an operational definition of data science and data analytics. They believe that defining the terms is important for enabling productive dialogue between the data scientists and the compliance function. Some of the questions the authors suggest could include the following:

• What current lab projects would most benefit from better compliance connections and could serve as test cases for the bridge?
• Who in compliance needs to be on board first? Who in the data lab?
• Are there candidates to lead the bridge from within both compliance and the data lab respected enough by the other functions to be effective?
• How do we align the bridge to the compliance function’s strategic interests?

The authors believe, “In many cases, the leader of the data science lab has the most to gain and could be the player to reach out to the factory leader to initiate dialogue.” Initially, a footbridge, or informal connector between the lab and factory, can be a good starting point. Data scientists and CCOs “can take the initiative to discuss the concept across organizational boundaries and go to senior leadership with specific proposals. (They should abide by the principle of bringing senior leaders a solution rather than a problem.)”

Interestingly, the authors believe that compliance professionals should not have to wait for top-down direction either. They believe such personnel can “begin a dialogue to discuss how to foster better cooperation. A series of discussions on addressing the tension also constitutes the beginnings of a footbridge.” The authors do caution that “in order to achieve a sustainable solution, top-down direction needs to intersect with bottom-up, and a structural solution will be required. This means creating the bridge organizationally: that is, naming a senior executive to lead the project, and funding the improvement initiative. Only the CEO can do this.”

With the Department of Justice (DOJ) mandate of moving from risk assessment to continuous monitoring to continuous improvement, as laid out in the 2020 Update to the Evaluation of Corporate Compliance Programs, the time for such an approach is now. Only by “taking structural steps to address the deeply entrenched frictions can organizations expect to reap the full benefits of their investments in data science.” The authors end by intoning “Now is certainly the time to act. Senior leaders have a unique opportunity to resolve a previously unrecognized and debilitating tension, thereby putting their data science initiatives on a new and more productive path.”

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