The rise of analytics and data science executives has received a lot of attention in recent years. Similarly, there has been substantial focus on the analysts and data scientists who get the work done. Both types of roles are required if success is to be achieved. However, is an important layer missing? I think so and will discuss why and what that layer is in this blog.
Defining the Gap That Exists
In large companies, much focus has been placed at the executive end of the organization. While a CAO or VP of Data Science may be able to define and prioritize important analytics, that person won’t realistically be able to directly manage the execution of those requests on a daily basis. For example, a CAO or VP will, by necessity, be constantly drawn into the politics and ever-changing priorities that exist at the corporate level. There are many distractions, that while important and valid, take away from the ability to focus on day to day tactical execution. After all, these roles are described as “senior management”, not “senior execution” and the issues that keep an executive up at night are different from those at lower levels.
On the other end of the spectrum, there has been a lot of progress in better defining the roles required for execution of analytics and data science initiatives. As I’ve written about in the past, there are now specialist roles that span different parts of the analytics process as well as different specialties within data science. This is all terrific progress, but the reality is that many of the people in these roles are either relatively junior or are experienced resources who are primarily interested in the technical execution of projects as opposed to worrying about staffing issues, budget planning, and corporate politics.
While the executives set the priorities and make the call as to what analytics are most needed, the hands-on resources handle gathering data, coding, and delivering the analytics. However, for the organization to actually run smoothly and effectively, we can’t forget the layers of management between these organizational chart extremes. Outside of typical management hierarchies, there is one role that more organizations need to consider adding to their team.
What Is A Chief Data Scientist?
The early generations of analytics and data science executives were people who had technical backgrounds with substantial coding and model building experience. There was not as much focus placed upon general management and strategic skills. In today’s world, management skills and a strategic viewpoint are at least as important as technical skill, if not more important, for an analytics executive. This is due to the size, scale, and broad reach of today’s analytics and data science organizations within large companies.
If today’s analytics executives are selected less on their technical skills than in the past and if they must focus their time on a lot of issues outside of execution, then how can executional success be achieved? It is necessary to have someone take the lead in the area of technical leadership. This is where a Chief Data Scientist enters the mix.
A Chief Data Scientist is someone with a lot of experience and success when it comes to building and deploying models within the organization. While senior in tenure and experience, someone in this role prefers to remain hands-on and involved with execution as opposed to moving up the traditional managerial ranks. Most important, as one of the most experienced and skilled technical resources in the organization, a Chief Data Scientist can play the critical role of ensuring that the organizations analytics and data science strategy is effectively translated into tactical execution at the lower levels of the organization. Unlike an analytical translator who serves as a bridge between business stakeholders and the analytics organization, a Chief Data Scientist is a translator who bridges strategy and execution within the analytics organization itself.
The Concept Behind a Chief Data Scientist Role Is Not New
History often repeats itself and successful formulas tend to be used over and over. Roles similar to a Chief Data Scientist have been crucial to the success of other fields in the past. For example, in many organizations, a Chief Technology Officer (CTO) reports to the Chief Information Officer (CIO), drives innovation, and ensures the organization is keeping up with the latest trends. While CTOs are very senior, many have few (if any) direct reports and little direct budget ownership. However, they are always highly influential and typically have direct access to the most senior executives in the company.
While the CIO deals with the corporate politics and drives the IT strategy, the CTO both influences the strategy and also serves as the bridge to the broader IT organization in getting things done. This sounds a lot like how the relationship between a CAO and a Chief Data Scientist is defined above. There are other examples following the same model such as technical fellows within engineering communities.
If your analytics and data science organization has not yet put in place (or even considered putting in place) a Chief Data Scientist to help lead the execution and implementation of your analytics strategy, it is time to think about how creating such a role can be an important step in guiding your organization to the next level.
Originally published by the International Institute for Analytics