More and more credit unions realize how data analytics can help them in driving their business, in engaging their members in a new way in cutting down the cost and bringing efficiencies all over.

For a data analytics program in any credit union to function at the highest level, you must have the right mix of talent and roles. This article shares an overview of different roles and responsibilities within the data analytics program (not including data governance). Your program may or may not have all the roles listed below, or they may exist with another title. But these are the roles to consider when you are building a data analytics program.

In many ways, the data analytics program is like a software development project as there are some aspects of architecture, design, programming, standards, testing, acceptance testing, rollout, etc., which can be equated to building those data pipelines from the source systems to the target or the data warehouse or to italic whatever it is.

Data Analytics Program Lead – Most data analytics programs have a leader who defines a vision and strategy in close collaboration with the business stakeholders. They seek to understand the aspirations and expectations of business leaders and how data analytics can help them solve specific problems and drive their business.

Data Architect – This role is not to be confused with an enterprise architect. Data Architects are responsible for designing data warehouse technical architecture, how will it integrate with all the data sources, how many databases are needed, and how will they be set up? Their storage, compute, and memory requirements are performance, availability, security, etc.

Data Modeler – handles defining the data warehouse schema. Based on the business needs, they decide the schema standard for the data warehouse, whether it is Kimball, Inmon, Data Vault method, or something else. Furthermore, they decide and document how the data from different sources will be brought into and organized in the data warehouse.

Data Engineers - are responsible for building the data pipelines and bringing the data from the source applications into the data warehouse. In addition, some senior data engineers are responsible for defining and documenting the data pipelines development coding standards to ensure higher quality code that is modular, reusable, readable, and overall higher quality. Based on the ETL technology being used, some of them may have a software development background.

Data Analysts – are good at data wrangling, cleaning and preparing, and making it usable for analysis. Many times, they are good SQL query writers.

Business intelligence Analysts – We commonly see other titles for this role, such as report writers, Data Analysts, etc. These folks are responsible for building reports and dashboards based on the business users' requirements

Project Managers – are responsible for project managing the overall data analytics program, including the communications within the team as well as with stakeholders, all aspects of the delivery of the data warehouse to the business, such as release management, change management, and all aspects of making data warehouse as a service to the business stakeholders including incident management, SLAs definition, training and education management, etc. Some bigger teams may spill this function into two. One focuses on the data analytics team, and the other on the stakeholders.

Business Analysts - This is a role whose importance many times is overlooked. Business Analysts are working closely with the business leaders to understand their objectives and how data can help them succeed and enable them to use the data warehouse. For example, suppose business requirements dictate any new data or changes to the data warehouse. In that case, they gather those requirements and pass them to the data architect/modeler/engineers to ensure that they are implemented in future releases. In some organizations, they also lead the user acceptance testing before a new data warehouse version is released and lead the education and training sessions to enable the business to use the data warehouse.

Data Scientists - the people responsible for building advanced AI/ML models such as propensity models, predictive models, etc., e.g., Identifying the members who are likely to be your following product, who are likely to default, and who like to churn, etc. There are hundreds of use cases out there, and these are the people who are responsible for building these models and testing and validating them.

Machine Learning Engineers – These people take the AI/ML models developed by the data scientists and put them into action. Some effort is needed in taking those models and putting them in production, operationalizing them, and making sure that appropriate pipelines are built and integrations are built so that the results of those models are exposed to users in the way and form that is most useful to them.

You may not need all these roles on day one. It very much depends upon your specific environment, priorities, and road map. If you need to discuss this, feel free to contact me. We are happy to help discuss any of these roles or see how you can best work or structure your data analytics team so that it is a high-functioning team.