Analytics roles in a data-driven organization
Every mature organization with a digital presence needs to have the right people to be able to support certain tasks with the appropriate analytics roles and complete digital transformation projects successfully. These projects are an absolute necessity if they want to have a data-driven culture in the organization. But which are the roles needed? Analytics is getting more complicated each day, especially in the past few years with the wider adoption of machine learning and artificial intelligence techniques. Talent is hard to find and skills are harder to group under specific job roles/titles. It is important for every organization to have a correct hierarchy of employees if you want them on their top game. Hiring for instance a large number of skilled data scientists will be of little use, if you don’t have the necessary teams to support them with high quality data sets.
The right way to approach the talent issue is to think about analytics talent as a set of skill sets and roles. Most of the roles and their capabilities will overlap and it’s OK. But it’s important to have a clear definition of each role with detailed job descriptions up to the organizational processes for each one of them. Only after carefully taking an inventory of the talent currently available and the roles they can server, you can also identify any gaps and aim for the right candidates.
The following diagram, from McKinsey&Company is a great source, showing you the most important roles for any data-driven organization, their basic responsibilities and how interact/overlap with each other. Clicking on the Venn diagram will let you find more details on the responsibilities of each role.
All roles are split into 3 main categories:
- Business skills
- Technology skills
- Analytics skills
If there’s one analytics role that can do the most to start unlocking value, it is the analytics translator. This sometimes overlooked but critical role is best filled by someone on the business side who can help leaders identify high-impact analytics use cases and then “translate” the business needs to data scientists, data engineers, and other tech experts so they can build an actionable analytics solution. Translators play a critical role in bridging the technical expertise of data engineers and data scientists with the operational expertise of marketing, supply chain, manufacturing, risk, and other business managers. In their role, they help ensure that deep insights generated through sophisticated analytics translate into impact at scale in an organization.