Partner, Dynamic Ideas, Senior Lecturer MIT
Jordan Levine is a partner at Dynamic Ideas, an organization committed to spreading powerful ideas in the areas of analytics and operations research. His focus there is on creating digitally accessible training and education products that make analytics accessible to business leaders. He is also a lecturer at the Massachusetts Institute of Technology in the Sloan School of Business.
After serving as a communications officer in the United States Marine Corps, Jordan spent seven years at McKinsey & Company, where he served as the global learning and development lead for analytics. There, he architected a strategy and oversaw a learning team that engaged around 4,000 McKinsey colleagues per year at the executive, manager, and technical talent levels.
Jordan holds a master’s in engineering from the Massachusetts Institute of Technology (MIT) and a mathematics degree from the United States Naval Academy.
Getting to know...
You do a lot of work to ‘harmonize the data language’ among executives. Would you say a bit about what this means?
A common failure in the delivery of a data and analytics product is a mismatch between what was expected and what eventually gets delivered.
This outcome is often a function of the following sequence of events: 1) An executive team gets excited to apply a new technology. 2) The executive team provides an unclear scope of work to their analytics team. 3) The analytics team begins execution without rigorously defining the end-state through a blueprinting process. 4) Months later, an executive team is underwhelmed by a product that they neither recognize nor know how to engage with.
One root cause of this sequence is a lack of harmonized language between the business leadership asking for the product and the technical team building the product. Business executives tend to focus on three key outcomes: revenue, cost and risk. Technical teams often focus their energy in the domains of data structures, algorithms and engineering.
Both sides must make effort to meet in the middle with a clear, aligned set of objectives, priorities and definitions.
You’re an advocate for simplifying data challenges. What are the biggest barriers to achieving this?
There is often a feeling that, once a technical team starts work, the business leadership has the right to step away and wait for results. This is an abdication of responsibilities from both the business team and the technical team.
The sophistication of the data models and algorithms used to solve a problem should only be sufficiently complex to solve the stated problem. Effort must also be taken to steer the technical team to produce an answer that is sufficiently rigorous, but not over-engineered. This is the role of the business team – ad the business team can only provide the requisite guidance if they are involved from start to the finish of the project.