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Data Roles

There are many Data roles across an organisation that touch, manage or use Data, thus it’s important for us to define these roles and how they interact with the Data Lifecycle. These roles have been broken down into two major groups:

  • Data Management Roles
  • Data Usage or Consumer Roles

Data Management Roles​

People that are actively part of the creation, preparation and management of Data within the Lifecycle. These roles are responsible for Data Governance across the Organisation.

Functional Flows

Data Owner​

The Data Owner is a senior business leader responsible for setting the data strategy and holding financial authority over decisions related to data within their domain. Typically, this is the head of the function or business area that creates or produces the data.

It’s important to note that the specific title may vary by organisation—some may refer to this role as a General Manager, Vice President, or similar. Regardless of the title, the Data Owner is the individual with the authority to approve funding and make key decisions within that function.

In CryspIQ®, when adding a source message, the Business Function field must be specified. This directly assigns ownership of the source data to the corresponding business function, ensuring clear accountability from the outset.

Data Stewards​

Data Stewards are business professionals recognised as Subject Matter Experts (SMEs) within their specific data domain. Typically embedded within a business function, they possess deep knowledge of the data and play a key role in defining, maintaining, and improving data quality across the organisation.

They work closely with stakeholders to ensure data is accurate, consistent, and fit for purpose.

In CryspIQ®, when adding a source message, the Data Steward’s name and email address must be provided. This action directly assigns responsibility for the quality of the data being created or produced, promoting clear ownership and accountability.

Technical Stewards​

Technical Data Specialists are professionals who work within specialised technical disciplines related to data, such as Data Engineering, Integration, Database Administration, Data Modelling, Architecture, Metadata Management, and Data Operations. This group also includes external data providers or producers responsible for supplying or managing data.

These individuals typically work within the systems or applications where the data resides and operate under the guidance of the Data Steward, providing hands-on support for the day-to-day technical activities required to manage and maintain data quality.

In CryspIQ®, this is the Data Administrator role which can be assigned when the user is set up. Tasks and responsibilities are delegated by the Data Steward as needed.

Usage Roles​

These roles are responsible for discovering, analysing, and using data to generate insights that drive value for the organisation.

They apply a range of analytical techniques—such as statistical analysis, data mining, and machine learning—to uncover patterns and trends. Some of these roles also develop automated algorithms and models, including predictive and AI-driven solutions, which generate new data based on their findings.

Analysts​

Data Analysts are professionals who use data primarily for descriptive analytics, which focuses on understanding and summarising past events.

This type of analysis answers the question: “What happened?”, making it the most basic and widely used form of data analysis.

The majority of business users and data consumers typically fall into this category, using dashboards, reports, and visualisations to gain insights from historical data.

Specialists​

Specialist Analysts focus on diagnostic analytics, which seeks to understand the reasons behind past events or performance outcomes. The primary question they aim to answer is: “Why did it happen?”

This group includes roles such as Business Intelligence (BI) specialists, and experts in tools like Power BI, Tableau, QlikView, and other reporting platforms.

In some cases, these specialists develop custom algorithms to support deeper analysis. When these algorithms are productionised, they may generate new data, which is then reintegrated into the data lifecycle for ongoing use and governance.

Data Scientists​

Data Scientists specialise in predictive and prescriptive analytics, leveraging data to forecast future outcomes and guide decision-making.

  • Predictive analytics uses historical data, statistical techniques, and machine learning to answer the question: "What is likely to happen?"
  • Prescriptive analytics, the most advanced form of analytics, provides actionable recommendations by answering: "What should we do?"

This category includes Data Scientists and Machine Learning Specialists, who develop sophisticated models that can be productionised to support business operations.

The output of these models often generates new data, which is fed back into the data lifecycle for ongoing analysis, governance, and improvement.