This week in 1971 the seminal George Harrison 3-disc set, All Things Must Passbegan a 7-week run as the Number 1 album on the Billboard charts. It was his first post-Beatles album and it was truly an overwhelming amount of music that had been floating around in his head since his Beatles days. Harrison told Dick Cavett in an interview, ““I had such a lot of songs mounting up that I really wanted to do, but I only got my quota of one or two tunes per album”. Boy did he.
Jayson Greene, writing in Pitchfork.com, said, “Given his own studio, his own canvas, and his own space, Harrison did what no other solo Beatle did: He changed the terms of what an album could be. Rock historians mark All Things Must Passas the first “true” triple album in rock history, meaning three LPs of original, unreleased material.” Moreover, “The album was a collaborative party in itself, a gathering in which Eric Clapton, Ringo, Billy Preston, future Yes drummer Alan White, and even a young Phil Collins, playing bongos on “Art of Dying,” were given space. Having been elbowed out of a room too many times before, it seemed, he was staunchly unwilling to do the same to others.”
Harrison’s masterpiece informs today’s topic of using data science to communicate compliance insights. In the most recent Harvard Business Review (HBR), Scott Berinato writes, in an article entitled “Data Science and the Art of Persuasion”, that most companies are not getting the value from data science initiatives and prescribes ways to remedy this phenomenon. Last year, at Compliance Week 2018, Hui Chen said on a panel that she expected the compliance team of the not-so-distant future would have a data scientist. As with most of her pronouncements, she was way ahead of the crowd. Over the next few blog posts, I will be laying out why every Chief Compliance Officer (CCO) not only must have a data scientist on their team but also how to use such a talent for maxim value.
You must start with the premise that most CCOs and compliance professionals are legally trained, usually without any data analytics classes in law schools still operating under the Socratic Method. Even if a stat class is thrown in somewhere along the way in undergrad, grad school or even through some business school outreach to law students, that does not begin to prepare someone to understand the insights available through advanced data analytics. The key is to build a better data science operation. Here Berinato has four suggestions, with the over-arching theme of defining the talents you need to understand and communicate the data.
You must begin by understanding that the unpacking of data and creation of insights is a skill. One can learn this skill, through schooling or perhaps be innately talented in it, but it is a skill rather than a role. Some of these skills include those of a project manager. Berinato notes, “A good project manager will have great organizational abilities and strong diplomacy skills, helping to bridge cultural gaps by bringing disparate talents together at meetings and getting all team members to speak the same language.”
Drawing no doubt upon a frontier tradition, the next skill listed is “data wrangling” which includes finding, cleaning and structuring data. Here Berinato writes, “People with wrangling talent will look for opportunities to streamline operations—for example, by building repeatable processes for multiple projects and templates for solid, predictable visual output that will jump-start the information-design process.” Next is a very large skill of data analysis which the author believes is the “ability to set hypotheses and test them, find meaning in data, and apply that to a specific business context is crucial—and, surprisingly, not as well represented in many data science operations as one might think.” This is largely because this skill is not equated to simple coding and math but is more liberal arts in its basis. Not surprisingly, these are skills that lawyers are trained for; such as critical thinking and context setting. He cited Michael Correll for the following, ““It’s impossible to consider data divorced from people. Liberal arts is good at helping us step in and see context. It makes people visible in a way they maybe aren’t in the technology.””
Next is subject matter expertise. While there are still some lawyers who think CCOs do not need to know how to read a spreadsheet; fortunately such Neanderthal thinking has largely left the compliance profession as it moves towards operationalizing compliance into organizations. The reason is straightforward, “People with knowledge of the business and the strategy will inform project design and data analysis and keep the team focused on business outcomes, not just on building the best statistical models.” Berinato cites to Joaquin Candela, “who runs applied machine learning at Facebook, has worked hard to focus his team on business outcomes and to reward decisions that favor those outcomes over improving data science.”
The next two skills are needed to communicate the information. The first is design, which the author relates to the development and execution of “systems for effective visual communication.” This means “they understand how to create and edit visuals to focus an audience and distill ideas. Information-design talent—which emphasizes understanding and manipulating data visualization—is ideal for a data science team.” Combined with this design or visualization skill is that of storytelling, which Berinato believes is “an extremely powerful human contrivance and one of the most underutilized in data science.” The ability to tell a story, whether through a blog post or podcast, can “help to close the gap between algorithms and executives.” He concludes by stating, “It is decidedly not about turning presenters into Stephen Kings or Tom Clancys. Rather, it’s about understanding the structure and mechanics of narrative and applying them to dataviz and presentations.”
Tomorrow we will consider how these skills work together to create greater and more insightful intelligence going forward.
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© Thomas R. Fox, 2019