The data and analytics mission has shifted from one of risk mitigation to one of the creation of business value with data assets. Among the top priorities for data teams are data quality, reliability, and access, as well as the enhancing of decision making and the driving of product and business innovation. For many organizations, the role of Chief Data Officer is still new, and the most effective positioning of data capabilities (data teams) inside the organization has yet to be defined. Where the role of the Chief Data Officer is undefined, the scope, priorities, roles, and responsibilities of data teams can quickly become inflated. It seems that once a data team is established, regardless of how small the associated budget, resources, and team, even the most minor data-related problem that comes up is regarded as falling within the data team’s remit.

The challenge of finding the right balance between defining and aligning priorities, while leaving a buffer zone of flexibility for new things that come up, is common for almost any operating model. To achieve clarity of roles and responsibilities, senior leadership should act quickly and decisively to determine the data-driven ambitions of their enterprise, and to set the scope and actively communicate the purpose of their data and analytics teams. Key questions that have to be faced are as follows:

• How can the data and analytics function be organized inside the organizational model?
• Where should the data and analytics function be located inside the organizational model?
• What is the right degree of centralization for data and information management?
• How should responsibility be divided between business areas and data teams?
• How do you fill the data and analytics skills gap?

Several options are available for senior leadership when it comes to the decision on where to locate a data and analytics team within their organization. For instance, the team could be located either within central or local management, or either within or outside the IT department. As the data value chain requires cross-functional skills, the split between business, data, and IT duties must be clearly defined.  Furthermore, the data and analytics skills gap presents a hiring challenge for employers. Some organizations consider working with an external D&A service provider because they lack the right resources or are unable to develop or improve their current talent pool. Such a provider can support the organization by temporarily filling the D&A role deficit and/or upskilling or reskilling existing resources.

Further to the challenges discussed above, organizations must define the roles and responsibilities for the core team along the data value chain. While emerging roles like in the domain of data ethics are gaining importance, there are a number of other roles that are essential for data and analytics teams. These roles are listed below.

Chief Data Officer

The chief data officer is part of the senior leadership team and is responsible for data governance, data value creation, data quality, and data reliability. The chief data officer’s targets are aligned closely with business goals. While being accountable for the data team, the chief data officer serves the business with data and analytics outcomes to help the business reach its objectives. The chief data officer is the data and analytics leader, and in some organizations the title can vary as ‘data and analytics head’ or ‘data and analytics director’.  

Data Scientist

The data scientist holds a computer science, statistics, or economics degree. This role is responsible for modelling complex business problems, discovering insights with statistical methods, and applying visualization techniques. The analytics-related outcomes of the data scientist are more concerned with predicting future events and recommending the best actions to take next.

Data Engineer

The data engineer is responsible for creating, managing, and operating data pipelines that are needed by the business. The data engineer models and scales databases to ensure the flow of data within the organization. This role requires business and IT skills for providing data and analytics use cases with the right data. Technical skills are required to work on complex IT infrastructures and business skills are needed to understand the analytics context and be able to create the data pipeline and deploy the insights thus gained in business applications.

Data Steward

The data steward has close proximity to the business and holds a key role in data governance. This role is responsible for enforcement of data policies, ensuring data quality, and monitoring of information assets. The data stewards sit within business domains and have extensive business knowledge. They amend and manage metadata for data quality and data governance purposes. This role demands extensive business domain knowledge and some IT skills to understand basic data modeling concepts, data warehouses, and data architectures.

Many other roles have emerged in the past 5 years, with data broker, ML validator, data literacy coach, and decision engineer, being just a few. However, due to recent global events (e.g., Covid or the conflict in Ukraine), there is set to be a convergence of many different roles and a rise in citizen roles.  In so-called citizen roles, key data and analytics skills will be shifted to business users, self-service analytics will be embraced, and the overheads of expensive data teams will be reduced.