Last week I began what I thought would be a two-part exploration on creating better data science in the compliance function. However as usual, I got carried away and two blogs post morphed into three. This series is based upon a recent Harvard Business Review (HBR) article, entitled “Data Science and the Art of Persuasion”, by Scott Berinato. Previously, I introduced the problem, explored how to build a set of talents to a better integrate a data science operation in your compliance function and considered how to implement those talents into an integrated team. Today, I want to bring it all home by tying it together.
Unless your company has a data science department, most probably the talent needed will be dispersed throughout your organization. The author relates, “design talent may report to marketing and subject-matter experts may be executives reporting to the CEO. Nevertheless, it’s important to give the team as much decision-making power as possible.” As a Chief Compliance Officer (CCO) you will want to operationalize this process as much as possible by having the stakeholders be those who are closely connected to or responsible for business goals as the aim of this work is a more efficiently run business to increase profitability for the company. However the author cautions, “Ideally you can avoid the responsibility-without-authority trap, in which the team is dealing with several stakeholders who may not all be aligned.”
There can be multiple leaders throughout the life cycle of a project and, as the author notes, “Who leads and who supports will depend on what kind of project it is and what phase it’s in. For example, in a deeply exploratory project, in which large volumes of data are being processed and visualized just to find patterns, data wrangling and analysis take the lead, with support from subject expertise; design talent may not participate at all, since no external communication is required.” Yet when it comes time to prepare a report for the board on evidence for a recommended strategy adjustment, the storytelling and design lead will most probably move to the fore. As the CCO you should be ready to move the pieces around depending where you are in the lifecycle of the project.
The author believes the working from home mantra will not work for this type of project as team members should work in the same physical space during the of project. However this is not always feasible so you should set up a virtual space for communication and collaboration. The author believes the collaborative process is critical and it may well be that when you have such disparate subject matter expertise on one project, that face-to-face collaboration can not only make the project more robust but it may be required. He advocates, using the ““paired analysis” techniques, whereby team members literally sit next to each other and work on one screen in a scrumlike iterative process. They may be people with data wrangling and analysis talent refining data models and testing hypotheses, or a pair with both subject expertise and storytelling ability who are working together to polish a presentation, calling in design when they have to adapt a chart.”
This colocation also helps the next requirement which is that it is very much a team process. The author cited to Eric Colson, the Chief Algorithms Officer at Stitch Fix, who said, “The crucial conceit in colocation is that it’s one empowered team. At Stitch Fix our rule is no handoffs. We don’t want to have to coordinate three people across departments.” As the compliance lead, you should create a priority to ensure that your team has all the skills they needed to accomplish your goals with limited external support. To do this Colson, “tries to hire people many would consider generalists who cross the tech-communication gap. He augments this model with regular feedback for, say, a data person who needs help with storytelling, or a subject expert who needs to understand some statistical principle.”
Finally, the author believes that you can reuse what works. He wrote, “Think of this as a group of people who combine their design talents and data wrangling talents to create reusable code sets for producing good data visualizations for the project teams. Such templates are invaluable for getting a team operating efficiently.” This can work to create a reusable template which can be customized for the output of any particular compliance risk or issue you are considering.
The author concludes by noting that the “presentation of data science to lay audiences—the last mile—hasn’t evolved as rapidly or as fully as the science’s technical part. It must catch up, and that means rethinking how data science teams are put together, how they’re managed, and who’s involved at every point in the process, from the first data stream to the final chart shown to the board.” For the CCO, this will require extensive out of the box thinking to help you not only understand the data and analytics but think through how to present it in the most efficient manner to your leadership.
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