Analytics COE Team: Roles & Responsibilities

March 4, 2023 by Ajitesh Kumar · Leave a comment
data analytics center of excellence roles and responsibilities

Data analytics Centers of Excellence (CoEs) are the key to unlocking a company’s full potential with data. As a business leader, you know how important it is to stay ahead of the curve and have access to timely, accurate analytics that can help inform decisions. But having access to this data isn’t enough—you need an experienced team in place who understand the nuances of data analytics, can develop models and uncover insights that drive business decisions. That’s where data analytics CoEs come in. In this blog post, we’ll explore the roles and responsibilities of staff members in data analytics CoEs, as well as their importance in enabling organizations by delivering them actionable insights that would help them make smarter decisions with greater confidence while measuring the impact. Lets dive in!

The following are some key roles which could prove to be useful for the Data Analytics COE:

  • Data visualization experts: Data visualization experts are responsible for creating visual representations of information, such as charts and graphs, to help people better understand complex data sets. They must know how to use various software programs such as Qliksense, Tableau or Power BI to create meaningful visualizations. They need to be able to apply methods such as color coding and labeling when designing visuals. They also need to have a strong knowledge of traditional design principles including composition, balance, texture, form, line weight, typography and illustration techniques. They also need to have story telling skills to be able to effectively communicate insights derived from the data sets. Data visualization expert can become part of analytics team.
  • Data engineers: Data Engineers are responsible for managing, processing and maintaining large-scale data sets used in machine learning and analytics. They build the infrastructure needed to store and process big data, including setting up data warehouse systems, data lakes, data lake house, cloud storage or database systems. They develop processes and pipelines for ingesting data from multiple sources into a usable format for data scientists and other users. Data engineers have expertise in the handling of big data by utilizing various technologies including Hadoop, Hive, Pig, Spark, Kafka, Cassandra and more. Data engineers also prepare data for features used in machine learning models in production. They prepare data by cleaning and combining data from different data sources for use with dashboards. They also set up automated pipelines to ease the flow of data between different systems. Data engineers can become part of data team.
  • Data quality analyst: The role and responsibility of a data quality analyst is to ensure that the quality attributes of data matches the desired standards of high quality data (accuracy, completeness, consistency, coherence, etc.). Read my data quality blog to learn more about data quality characteristics. Data quality analyst work through data quality framework which takes the data quality demands, prioritize them and implement data quality from time-to-time. The tasks of data quality analysts includes inspecting, cleansing, transforming, and monitoring the data to meet certain standards of each of the data quality characteristics. They will identify any errors or inconsistencies in the data sets, and then take steps to correct or improve them as needed. Data quality analyst can form part of the data governance team.
  • Data governance analyst: A data governance analyst is tasked with managing different aspects of data governance related to data collection, providing restricted access to data, data sharing and more. They play key role in all the important aspects of data governance including defining & classifying data etc. They are responsible for overseeing the organization’s approach to data governance, as well as developing and enforcing policies & rules related to data quality, security and privacy processes. Data governance analysts must possess a deep understanding of the technologies used to collect, store and share data within an organization in order to properly evaluate and manage risk associated with each system. They must also have a good grasp of industry-specific regulations that affect how organizations handle their data. Data governance analyst can form part of the data governance team.
  • Data analysts: Data analysts are highly skilled professionals who are responsible for extracting, manipulating and analyzing data from various sources to aid in the decision-making process. Their primary deliverables can be plots, charts, graphs, reports etc. They use their knowledge of mathematics, statistics, computer science, and other analytical techniques to help organizations make informed decisions based on insights derived from data. Data analysts are also responsible for maintaining databases and ensuring the accuracy of the data stored within them. In addition to providing expertise in data analysis and manipulation, data analysts also need to have strong communication skills as they will often be required to present their findings to stakeholders. One of the key difference between data analyst and data scientists could be around designing and performing experiments around hypothesis related to business problems. Other difference is around machine learning / AI expertise. Data analyst can be part of data team or analytics team.
  • Data scientists: Data scientists must possess a wide range of skills, including expertise in various forms of statistics, machine learning and programming languages such as Python and R. Mathematics is also a key component in the arsenal of any well-rounded data scientist. A decent understanding of the business domain they are working on can prove invaluable when it comes to making sense of complex datasets and building models. However, business analysts or product managers can help a great deal in understanding of business domain and business requirements. Beyond just technical proficiency, data scientists must also be able to communicate their insights effectively – both verbally and visually – in order to present their findings to stakeholders within an organization. Data scientists must work closely with business and operations team ranging from marketing to finance. Data scientists will form part of analytics team.
  • Cloud or Analytics engineers: Analytics engineers are responsible for developing and deploying data analytics systems. They must have expertise in building analytics systems, such as dashboard systems, analytics solution design and development, usage of analytics cloud services, and more. Analytics engineers also handle the maintenance of existing analytics systems, and investigations into new technologies including cloud (AWS, GCP, Azure, et.) and cloud-native technologies that can improve the performance of the current systems. Analytics engineers can also be involved in creation and maintenance of automated reporting system using tools such as automation anywhere (RPA), Alteryx, etc. Analytics engineers need to be highly skilled in areas such as cloud services design & development (especially AI / ML services), programming languages (Java, Python, etc.). Analytics engineers must also be knowledgeable in using big data processing frameworks such as Spark or Hadoop for deriving insights from structured or unstructured datasets. Analytics and data engineers can interchangeably play each other roles depending upon the organization needs. Analytics engineers can be part of analytics team.
  • Product managers / business analysts: Product Managers play a critical role in ensuring successful data analytics projects. Their core responsibility is to define the use cases for data analytics, create KPIs and value metrics to measure success, interface with end customers, and collaborate with the data analytics and IT team for end-to-end analytics solution development and deployment. To ensure success in these areas, product managers need to have strong expertise and understanding of the one or more products, business domain and the industry they are working with. They must be able to understand customer (internal or external) needs and requirements, as well as the capabilities of data analytics solutions that can address those needs. This requires an intimate knowledge of the current business objectives, business challenges, key personas, market trends, etc. Product managers also need to be able to effectively communicate these insights to stakeholders throughout the organization, including other members of the data analytics team. Product managers should have strong collaboration skills that allow them to coordinate with multiple stakeholders including data analytics teams, IT / technical teams, business teams, internal and external customers and leadership stakeholders. They should also be able to build relationships with subject matter experts who can provide valuable insights into specific domains or technologies when needed. Product managers could be part of data analytics team or could belong to product teams.
  • Project managers / Scrum masters: Project Managers in data analytics have a variety of roles and responsibilities. They must be able to effectively manage the entire project life-cycle from the initial Proof of Concept (POC) stage to scale up stages. This requires strong expertise in topics such as budget management, data gathering & analysis, and stakeholder communication. At the POV / POC stage, project managers are responsible for creating the project charter including outlining the objectives, scope and team for the project. During scale up stages, Project Managers are key in developing an effective roadmap for achieving project goals while managing resources efficiently. Ideally, they should have some knowledge of data analytics solutions and technologies including data management, data quality, data governance, reports & dashboards, AI / machine learning, etc. Project managers need to oversee quality assurance processes for data integrity during this stage. Projects managers can also be data analytics managers depending upon the organization needs.
  • Portfolio managers: Portfolio managers of data analytics are responsible for managing multiple data analytics projects, overseeing the successful delivery and execution of the projects, and engaging with different teams, vendors, and stakeholders. They must ensure that projects maintain their cost and time constraints while meeting customer satisfaction. Portfolio managers should possess decent expertise in data analytics and project management to develop strategies that leverage technology to meet objectives. They must also possess strong interpersonal skills like communication, negotiation as well as decision-making skills which are needed for working with various teams. They need to be adept at gathering requirements from stakeholders and converting them into achievable goals while being able to use project management principles to track progress of tasks and initiatives while staying within budget constraints.
Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
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