Starting off a series of organisation articles to discuss different roles that you may need in your organisation to become datadriven. As always, what roles you need is dependent on your company and what you want to achieve.
Lets start out with the roles for the data team. In general I think that the data team should be able to handle end-to-end use cases. From sourcing data and transforming it to analysing and presenting the data. There are many other surrounding roles to be successful, I will cover those in upcoming articles.
Data Team
So, the responsibility of the data team is to execute on use cases and implement the needed functionality. What kind of people do you need on your team? And so I don’t repeat myself on each and every role. All of the roles work together and communicate together. There is no sequence and handoffs. Everyone is in the car for the ride.
Business Translator – We need to start at the beginning, what is the business problem you are trying to solve? The role of the business translator is to facilitate the understanding of what the use case actually need. To work closely with business experts and with engineers and architects to make sure that the goal and the details of the use case are understood and communicated by all. Usually they also dig a lot into the data and data definitions to make sure that the right data is used in the correct manner.
Data Engineer – The data engineers main responsibility is to source the right data, build data transformations and data models that fulfils the business needs. Keeping both the use case and the long-term data model in mind. The DE also makes sure that the data pipelines are robust and production ready to minimize maintenance and incidents.
Data Analyst – The data analysts main responsibility is to make sure the business has the right analysis available. Depending on the maturity of the business this will range from ready-made dashboards into foundations in a tool for business to build self-service on top of. It may also result in a data model that the business can use to build their own dashboards and reports on. The important part is that the data analyst is responsible for making sure that the business has the right analysis for the use case at the end.
Data Scientist – (May be permanent or for a single use case) The data scientist has the main responsibility to create the advanced analytics needed for a specific use case. This may be a machine learning model, a statistical model or something else. The important part is the responsibility to solve the business problem, not the technology. The data scientist also has a very important role when it comes to advanced analytics since the area can be difficult to grasp and therein lies the need to facilitate and guide the business and the rest of the team to the right solution.
ML Engineer – This role is optional depending on the role of the team, if the main focus for the team is to work with ML models and predictive analytics then this role may be very valuable. It may also be included when needed and not be a permanent role in the team. The main responsibility is to make sure that the ML models can operate as well as possible. This includes making sure that there is a good MLOps stack, optimize models and feature creation, etc.
Scrum master / Delivery Lead – Depending on the organisation this role may have different names and operate in different manners. However this role has the responsibility to make sure that the cases for the data team can be delivered. This includes making sure there is a prioritized and estimated backlog, that the right people are committed to the use case both in the team and outside the team. It also includes removing obstacles and coordinating with other teams during the planning and execution.
In the upcoming articles we will look at the following:
2. Surrounding roles – Roles directly interacting with the data team
3. Platform and facilitating roles – Roles making sure that the data teams have the right foundation to stand on.
4. Data Transformation roles – Roles that make sure that the transformation is ongoing
5. Data Governance and Data Management roles.