The Emerging Spectrum of Data & Analytics Roles: Gartner

17 min readJan 5, 2023

In recent years, the role of data and analytics in business has grown significantly, leading to the emergence of a spectrum of data and analytics roles. These roles range from those that focus on collecting and storing data, to those that analyze and interpret data, to those that use data to inform decision-making and drive business strategy.

In a recent study, Gartner delved deep into classifying various new age functions surrounding data and analytics.

Here’s a quick and easy to understand explanation of each of these functions:

Data Modeller

A data modeler is a person who works with data and helps to organize it in a way that makes it easy to use and understand. They might create a special diagram or picture that shows how all of the data is connected and what each piece of data means. For example, a data modeler might work with a school to create a model that shows how all of the students, teachers, and classes are related to each other. They might also help to create a model that shows how the school’s budget works, or how the school’s computers are connected. A data modeler’s job is important because it helps people to better understand and use data to make better decisions.

Data Broker

A data broker is a person or company that collects data from a variety of sources and sells it to other people or organizations. They might collect information about people’s names, addresses, phone numbers, and other personal details, and then sell this information to companies that want to use it to advertise their products or services. Data brokers might also collect information about people’s interests, hobbies, and other things they like, and then sell this information to companies that want to learn more about their customers. Data brokers play an important role in helping businesses to understand their customers better and to create targeted marketing campaigns.

AI Engineer

An AI engineer is a person who works with artificial intelligence (AI) and helps to build systems that can “think” and “learn” like humans. They might work on projects that involve creating robots that can work alongside people, developing computer programs that can understand and respond to human language, or creating systems that can analyze large amounts of data and make predictions or recommendations. AI engineers use their skills in computer science, mathematics, and engineering to build and test these systems, and to make sure that they are accurate and efficient. They also work closely with other professionals, such as data scientists and software engineers, to bring these AI projects to life. AI engineering is a very exciting and challenging field that is helping to shape the future of technology in many different ways.

Citizen Data Engineer

A citizen data engineer is a term used to describe someone who is not a professional data engineer, but who has the skills and knowledge to perform data engineering tasks as part of their job. This could be someone who works in a non-technical role, such as a salesperson or a marketing manager, but who has been trained to collect, organize, and prepare data for analysis. Citizen data engineers are often responsible for tasks such as importing data from different sources, cleaning and preparing data for analysis, and creating reports and visualizations to communicate findings to stakeholders. They may also be involved in building and maintaining data pipelines and other data infrastructure. Citizen data engineers play an important role in helping organizations to make better use of data, and they are often seen as a bridge between technical and non-technical teams.

Analytics Engineer

An analytics engineer is a professional who is responsible for designing, building, and maintaining systems for collecting, storing, and analyzing data. They may work with a variety of data sources, including structured and unstructured data, and may use a range of tools and technologies to extract insights from this data. Analytics engineers often work closely with data scientists and other data professionals to develop and implement data-driven solutions for businesses and organizations. They may be involved in tasks such as designing and implementing data pipelines, building and maintaining data warehouses and data lakes, and developing and deploying machine learning models. Analytics engineers play a key role in helping organizations to derive value from their data and use it to inform decision-making and drive business strategy.

Data Engineer

A data engineer is a professional who is responsible for collecting, storing, and organizing large amounts of data. They work with a variety of data sources, including structured and unstructured data, and use a range of tools and technologies to store and manage this data. Data engineers may be involved in tasks such as designing and implementing data pipelines, building and maintaining data warehouses and data lakes, and integrating data from different sources. They may also be responsible for ensuring that data is accurate, secure, and available to those who need it. Data engineers play a critical role in helping organizations to make use of their data, and they are often seen as the foundation upon which data-driven projects and initiatives are built.

D&A Architect

A data and analytics architect is a professional who is responsible for designing and building the technical infrastructure needed to support data and analytics projects within an organization. They work closely with data engineers, data scientists, and other data professionals to understand the needs of the business and to create a data and analytics architecture that meets those needs. This may involve tasks such as designing and implementing data pipelines, building and maintaining data warehouses and data lakes, and integrating data from different sources. Data and analytics architects also play a key role in ensuring that data is secure, available, and compliant with relevant regulations. They may also be responsible for developing and enforcing data governance policies and best practices within the organization. Data and analytics architects are strategic leaders who help to shape the direction of an organization’s data and analytics efforts and ensure that they are aligned with the overall business strategy.

ML Validator

A machine learning validator is a professional who is responsible for evaluating and validating the accuracy and effectiveness of machine learning models. They may work with data scientists and other professionals to develop and test machine learning models, and to ensure that they are performing as expected. This may involve tasks such as selecting and preparing data for model training, testing models on different datasets, and analyzing the results of model evaluations. Machine learning validators may also be responsible for identifying and addressing any issues or biases that may be present in the models, and for recommending improvements or changes as needed. Machine learning validators play a critical role in ensuring the quality and reliability of machine learning systems, and they are an important part of the process of building and deploying machine learning solutions in a variety of contexts.

Citizen Data Steward & D&A Steward

A Citizen Data Steward or a Data & Analytics Steward is responsible for managing and maintaining the integrity, accuracy, and quality of an organization’s data and analytics assets. This typically includes tasks such as defining and enforcing data governance policies and procedures, working with stakeholders to identify and prioritize data needs, and ensuring that data is collected, stored, and used in a way that is consistent with regulatory requirements and the organization’s goals.

The Data & Analytics Steward may also be responsible for helping to develop and implement data management and analytics strategies, as well as working with data scientists and other analytics professionals to extract insights and value from the organization’s data. They may also play a key role in helping to identify and address any data quality issues, and work with stakeholders to ensure that data is being used effectively to support decision-making and drive business outcomes.

Knowledge Engineer

A Knowledge Engineer is a professional who is responsible for designing, building, and maintaining knowledge-based systems. These systems are typically used to support a variety of tasks, including decision-making, problem-solving, and natural language processing.

In the context of data and analytics, a Knowledge Engineer might be responsible for designing and implementing systems that use machine learning algorithms and natural language processing techniques to extract insights and value from data. This might involve tasks such as developing and training machine learning models, designing and implementing data pipelines, and working with stakeholders to identify and prioritize knowledge needs.

The Knowledge Engineer may also be responsible for maintaining and updating the knowledge-based system over time, including tasks such as data cleansing, data integration, and data quality assurance. They may also work with data scientists and other analytics professionals to develop and implement analytics strategies and solutions that leverage the organization’s data assets.

Data Analyst

A Data Analyst is a professional who is responsible for collecting, organizing, and analyzing data in order to extract insights and inform decision-making. In the context of data and analytics, the Data Analyst might be responsible for tasks such as:

- Collecting and sourcing data from a variety of sources, including databases, APIs, and web scraping

- Cleaning and organizing data to prepare it for analysis

- Analyzing data using statistical and analytical techniques, such as regression analysis and machine learning algorithms

- Identifying trends, patterns, and relationships in the data

- Visualizing data using charts, graphs, and other graphical elements to help communicate insights

- Working with stakeholders to identify and prioritize data needs, and to develop and implement analytics solutions

- Providing recommendations based on data-driven insights to inform decision-making

Data Analysts typically use a variety of tools and technologies to perform their work, including programming languages such as Python and R, data visualization tools such as Tableau and Power BI, and databases and SQL. They may also work closely with data scientists and other analytics professionals to develop and implement analytics strategies and solutions.

Data Product Manager

A Data Product Manager is a professional who is responsible for defining, building, and managing data-driven products and services. In the context of data and analytics, the Data Product Manager might be responsible for tasks such as:

Identifying opportunities to use data to drive business value and competitive advantage
Defining the vision, strategy, and roadmap for data-driven products and services
Collaborating with stakeholders to understand and prioritize data needs and requirements
Working with data scientists and other analytics professionals to develop and implement analytics solutions
Managing the lifecycle of data-driven products and services, including tasks such as market research, product development, launch, and ongoing maintenance and optimization
Measuring and reporting on the performance of data-driven products and services
Managing budgets and resources to ensure that data-driven products and services are delivered efficiently and effectively

Data Product Managers typically have a strong understanding of both business and technical concepts, and are able to bridge the gap between data science and business strategy. They may also have experience with data visualization, product development, and project management.

Data Translator

A Data Translator is a professional who is responsible for facilitating communication and understanding between technical and non-technical stakeholders. In the context of data and analytics, the Data Translator might be responsible for tasks such as:

Translating technical concepts and data-driven insights into terms that are understandable to non-technical stakeholders
Working with data scientists and other analytics professionals to understand complex technical concepts and analyses
Facilitating communication between technical and non-technical stakeholders to ensure that data-driven insights are effectively communicated and understood
Developing and delivering presentations, reports, and other materials to communicate data-driven insights to stakeholders
Providing training and support to non-technical stakeholders on how to use and interpret data and analytics tools and resources

Data Translators typically have strong communication skills and are able to effectively convey technical concepts to non-technical audiences. They may also have a strong understanding of data and analytics concepts and tools, and be able to work with data scientists and other analytics professionals to extract insights and value from data.

ABI Developer

An ABI Developer, or Advanced Business Intelligence Developer, is a professional who is responsible for designing and developing advanced business intelligence (BI) solutions that enable organizations to extract insights and value from their data. In the context of data and analytics, the ABI Developer might be responsible for tasks such as:

Designing and implementing data pipelines to extract, transform, and load data from a variety of sources
Developing and maintaining BI dashboards and reports using tools such as Tableau and Power BI
Working with stakeholders to identify and prioritize BI needs and requirements
Building and maintaining data models and data warehouses to support BI and analytics efforts
Implementing and maintaining data security and governance policies and procedures
Providing technical support and training to users of BI tools and resources

ABI Developers typically have strong technical skills, including programming and database experience, as well as experience with BI tools and technologies. They may also have strong analytical and problem-solving skills, and be able to work with data scientists and other analytics professionals to extract insights and value from data.

Model Manager

A Model Manager is a professional who is responsible for the design, development, and management of statistical and machine learning models. In the context of data and analytics, the Model Manager might be responsible for tasks such as:

Identifying opportunities to use statistical and machine learning models to drive business value and competitive advantage
Defining the vision, strategy, and roadmap for model-driven analytics and solutions
Collaborating with stakeholders to understand and prioritize modeling needs and requirements
Working with data scientists and other analytics professionals to design, develop, and implement statistical and machine learning models
Managing the lifecycle of models, including tasks such as model development, validation, deployment, and ongoing maintenance and optimization
Measuring and reporting on the performance of models
Managing budgets and resources to ensure that modeling efforts are delivered efficiently and effectively

Model Managers typically have strong analytical and problem-solving skills, and are able to work with data scientists and other analytics professionals to extract insights and value from data. They may also have experience with statistical and machine learning techniques, as well as experience with project management and data visualization.

SME

An SME, or Subject Matter Expert, is a professional who is an expert in a particular area or domain. In the context of data and analytics, an SME might be a professional who has extensive knowledge and experience in a specific area of data or analytics, such as machine learning, data visualization, or data governance.

The role of the SME in data and analytics may vary depending on the specific needs of the organization. In some cases, the SME may be responsible for providing guidance and mentorship to other team members, helping them to understand and apply complex technical concepts and techniques. In other cases, the SME may be responsible for leading and managing data and analytics projects, or for developing and implementing data and analytics strategies.

Regardless of the specific role, the SME is typically considered to be a trusted advisor and resource within the organization, and is often called upon to provide expertise and guidance on a wide range of data and analytics topics.

Citizen Data Scientist or Data Scientist

A Citizen Data Scientist or a Data Scientist is a professional who is responsible for collecting, analyzing, and interpreting large and complex datasets in order to extract insights and inform decision-making. Data Scientists typically use a combination of programming, statistical, and machine learning techniques to analyze data, and may work with a variety of data sources, including structured and unstructured data, as well as real-time and historical data.

Some specific tasks that a Data Scientist might be responsible for include:

Collecting and sourcing data from a variety of sources, including databases, APIs, and web scraping
Cleaning and organizing data to prepare it for analysis
Analyzing data using statistical and machine learning techniques to identify trends, patterns, and relationships
Visualizing data using charts, graphs, and other graphical elements to help communicate insights
Communicating findings and recommendations to stakeholders in a clear and concise manner
Developing and implementing machine learning models and other analytics solutions
Staying up-to-date with the latest data science technologies and techniques

Data Scientists typically have strong technical skills, including programming and statistical analysis, as well as excellent problem-solving and communication skills. They may work in a variety of industries, including finance, healthcare, retail, and technology.

Data Ethicist

A Data Ethicist is a professional who is responsible for ensuring that data is collected, used, and shared in an ethical and responsible manner. In the context of data and analytics, the Data Ethicist might be responsible for tasks such as:

Developing and enforcing data ethics policies and procedures
Advising on the ethical implications of data collection, use, and sharing
Identifying and addressing any ethical issues or concerns related to data and analytics projects
Educating stakeholders on the importance of data ethics and best practices
Working with data scientists and other analytics professionals to ensure that data is being used ethically and responsibly

Data Ethics is a growing field that is concerned with the ethical implications of the collection, use, and sharing of data. Data Ethicists may work in a variety of settings, including academia, government, and the private sector, and may have expertise in areas such as philosophy, law, and computer science.

AI/ML Developer

An AI/ML Developer is a professional who is responsible for designing and developing artificial intelligence (AI) and machine learning (ML) systems. AI/ML Developers typically use programming languages such as Python and Java, as well as frameworks and libraries such as TensorFlow and PyTorch, to build and implement machine learning models and other AI systems.

Some specific tasks that an AI/ML Developer might be responsible for include:

Designing and implementing machine learning models and algorithms
Developing and training machine learning models using large datasets
Testing and evaluating the performance of machine learning models
Implementing and integrating machine learning models into applications and systems
Optimizing machine learning models for performance and scalability
Staying up-to-date with the latest AI and ML technologies and techniques

AI/ML Developers typically have strong technical skills, including programming and data analysis, as well as experience with machine learning concepts and tools. They may work in a variety of industries, including finance, healthcare, retail, and technology.

Change & Transformation Manager

A Change and Transformation Manager in Data Analytics is responsible for leading and managing change initiatives within an organization that involve data analytics. This may include implementing new data analytics tools or processes, training employees on how to use these tools or processes, and helping to integrate data analytics into the organization’s overall business strategy. The Change and Transformation Manager may also be responsible for identifying areas where data analytics can be used to improve efficiency or effectiveness, as well as for developing and implementing a plan to achieve these goals. They may work closely with data analysts, data scientists, and other data professionals to ensure that the change initiatives are successful.

Data Coach

A Data Coach is a professional who helps individuals and organizations to improve their data literacy and data-related skills. They may work with people at all levels of an organization, from executives to analysts to front-line staff, to help them understand how to use data effectively and make data-driven decisions.

Some specific responsibilities of a Data Coach may include:

Providing training and guidance on data-related tools and technologies
Helping people to develop their data literacy skills, including understanding how to read, interpret, and analyze data
Assessing an organization’s data needs and helping to develop a data strategy
Working with teams to identify and prioritize data projects
Coaching individuals and teams on how to use data to inform decision-making and problem-solving

Overall, the goal of a Data Coach is to help organizations and individuals to become more proficient with data and to use data to drive business results.

MDM Manager

An MDM (Master Data Management) Manager in Data Analytics is responsible for overseeing the development and implementation of an organization’s master data management strategy. This may include designing and implementing processes and systems to ensure the integrity, accuracy, and consistency of the organization’s master data, as well as managing a team of data professionals who are responsible for implementing and maintaining the MDM system.

The MDM Manager may also be responsible for working with different teams and departments within the organization to identify and prioritize data projects, as well as for coordinating with IT and other stakeholders to ensure that the organization’s data systems are aligned with business goals and objectives. In addition, the MDM Manager may be responsible for developing and implementing data governance policies and procedures to ensure that the organization’s data is used in an ethical and compliant manner.

Decision Engineer

A Decision Engineer in Data Analytics is responsible for using data-driven approaches to support decision-making within an organization. They may work closely with data scientists, analysts, and other data professionals to develop and implement solutions that enable organizations to make more informed, data-driven decisions.

Some specific responsibilities of a Decision Engineer may include:

Identifying opportunities to use data to support decision-making within the organization
Developing and implementing data-driven models and algorithms to support decision-making
Collaborating with business stakeholders to understand their decision-making needs and to develop data-driven solutions to meet those needs
Working with data scientists and analysts to develop and maintain data pipelines and infrastructure that support decision-making
Assessing the effectiveness of decision-making processes and identifying opportunities for improvement

Overall, the goal of a Decision Engineer is to help organizations make better, more informed decisions by leveraging data and advanced analytics techniques.

D&A Manager

A Data & Analytics Manager is responsible for overseeing the data and analytics function within an organization. They may work with a team of data professionals, including data scientists, analysts, and engineers, to develop and implement data-driven solutions that support the organization’s business goals and objectives.

Some specific responsibilities of a Data & Analytics Manager may include:

Developing and implementing a data and analytics strategy that aligns with the organization’s business goals
Managing and leading a team of data professionals
Working with stakeholders across the organization to identify and prioritize data projects and initiatives
Overseeing the design, development, and maintenance of data pipelines and infrastructure
Collaborating with data scientists and analysts to develop and implement data-driven models and algorithms
Analyzing data to identify trends, patterns, and insights that can inform business decision-making

Overall, the goal of a Data & Analytics Manager is to help organizations make better, more informed decisions by leveraging data and advanced analytics techniques.

D&A Tester

A Data & Analytics Tester is responsible for testing data and analytics solutions to ensure that they are accurate, reliable, and meet the needs of the business. They may work with a team of data professionals, including data scientists, analysts, and engineers, to develop and implement testing plans and procedures for data-driven solutions.

Some specific responsibilities of a Data & Analytics Tester may include:

Collaborating with data professionals to understand the requirements and design of data and analytics solutions
Developing and implementing testing plans and procedures for data and analytics solutions
Executing tests and verifying that data and analytics solutions are working as intended
Identifying and reporting issues or defects in data and analytics solutions
Collaborating with data professionals to troubleshoot and resolve issues with data and analytics solutions
Providing regular updates and status reports on the progress of testing efforts

Overall, the goal of a Data & Analytics Tester is to ensure that data and analytics solutions are of high quality and meet the needs of the business.

XOps Coach

XOps (Experience Operations) is a term that refers to the processes and practices that organizations use to ensure that their data and analytics systems are running smoothly and effectively. An XOps Coach in Data and Analytics is responsible for helping organizations to improve their XOps practices and to optimize their data and analytics systems.

Some specific responsibilities of an XOps Coach may include:

Assessing an organization’s XOps practices and identifying areas for improvement
Developing and implementing strategies to optimize data and analytics systems
Providing training and guidance to data professionals on best practices for XOps
Collaborating with data scientists, analysts, and engineers to identify and troubleshoot issues with data and analytics systems
Working with stakeholders across the organization to ensure that data and analytics systems are aligned with business goals and objectives
Developing and implementing metrics and processes to measure the effectiveness of XOps practices

Overall, the goal of an XOps Coach is to help organizations improve the performance and reliability of their data and analytics systems by implementing best practices for XOps.

CDAO

A CDAO (Chief Data and Analytics Officer) is a senior executive who is responsible for overseeing an organization’s data and analytics function. They are responsible for developing and implementing a data and analytics strategy that aligns with the organization’s business goals and objectives, and for leading a team of data professionals to execute on that strategy.

Some specific responsibilities of a CDAO may include:

Setting the vision and strategy for the organization’s data and analytics function
Overseeing the design, development, and maintenance of data pipelines and infrastructure
Collaborating with data scientists, analysts, and engineers to develop and implement data-driven models and algorithms
Analyzing data to identify trends, patterns, and insights that can inform business decision-making
Working with stakeholders across the organization to identify and prioritize data projects and initiatives
Managing budgets and resources for the data and analytics function

Overall, the goal of a CDAO is to help organizations make better, more informed decisions by leveraging data and advanced analytics techniques.

Overall it is a dynamic and vast domain with several new functions and specialisations emerging each year.