Powering a truly data-driven organization requires more than just advanced technology and sophisticated processes — it demands an effective data analytics team to support your mission and execute the plans. The real challenge, however, lies in assembling a cohesive data analytics team with a diverse set of skills and expertise that can collaborate seamlessly to turn raw data into strategic insights.

Building a successful data analytics team requires more than just talented individuals. It necessitates effective collaboration to support the organization’s goals through the proficient use of data.

The first step is to understand how data and analytics fit into your overall business operations. This will help you identify the talent gaps and build a data analytics team tailored to your unique needs.

In this blog, we’ll discuss how your operating model shapes your staffing needs and explore the different roles and functions your data analytics team needs in order to reach your business goals.

Specifically:

Determine Your Operating Model

How your business chooses to work with data and analytics — your data and analytics operating model — will largely determine the staffing needs and roles required to achieve your goals and how to best tap into the value of your people.

What Are the Different Operating Models for a Data-Driven Organization?

The three types of operating models: decentralized, centralized, or hybrid. The right model is usually determined by the size of your organization, where you are on the analytics maturity model, and your data analytics needs:

  • Decentralized operating model distributes data and analytics responsibilities across different lines of business, as well as IT. There isn’t one centralized authority, and in an ideal scenario, business units across the company are collaborating and utilizing shared processes and metrics to make decisions. A decentralized operating model can lead to faster time to value because business units have the autonomy to do their own analysis, but it can also lead to lack of consistency, data silos, and higher costs due to overlapping technology and processes. This model is typical for a smaller organization with limited data analytics resources.
  • Centralized operating model is more structured with everything data and analytics related falling under the responsibility of a specific executive function. A centralized operating model allows for easier decision-making and less redundancy, but it can also lead to rigidness and delays with data and analytics initiatives. This model is typical for a more analytically mature organization that requires more data governance.
  • Hybrid operating model occurs when there is one central authority for data management but individual business units manage their own data and analytics. A hybrid operating model allows for consistent data management and data governance and freedom for each line of business to take charge of their data and analytics initiatives. This model is ideal for organizations that want advanced data operations without a dedicated data and analytics business unit for the organization.

It’s important to note — especially in a decentralized or hybrid model where you don’t have a dedicated data analytics business unit — that one person can have multiple roles. For example, a senior sales rep could also be assigned the role of business analyst because of their experience and understanding of the business unit. This offers flexibility as companies grow their team and/or change operating models.

It’s also important to note that there will be some functions that often require a specialized skill and should be the sole duty of a team member.

How to Optimize Your Data Analytics Team for Each Stage of the Data Lifecycle

For the data analytics team to be effective, you need roles and functions that account for activities that occur across the entire data lifecycle. Dedicating resources only to the beginning and ending stages (data acquisition and analysis) means neglecting important activities like data integration, transformation, and enrichment, which are equally crucial for making data-driven decisions.

To maximize the value of your data, assign responsibilities at each stage of the data lifecycle and recognize the synergy among roles. Keep in mind that modern data roles have become increasingly complex and interconnected, with overlapping responsibilities that drive collaboration. Business analysts, data engineers, and data scientists — for instance — often share tasks and collaborate to ensure a seamless data workflow, enhancing the overall effectiveness of your data team.

 

This graphic illustrates the various roles within a data team across the data lifecycle, highlighting key stages and responsibilities. It begins with the Data Source stage, involving ERP, CRM, operational apps, and file systems. Data Transport follows, focusing on data movement and reverse ETL processes. Data Storage includes data lakehouses, data warehouses, and data lakes. Data Processing involves query engines, while Data Transformation emphasizes data modeling and semantic layers. The final stage, Data Analysis, encompasses visualizations, AI/machine learning, bots, and custom apps. The roles are divided into those focused on data preparation, integration, storage, processes, and transformation, and those centered on analysis, interpretation, communication, enrichment, and refinement.

When building a data analytics team, make sure you have assigned roles and functions that will address each stage of the data lifecycle.

Below is a list of key roles and functions — and their definitions (according to the DAMA International) — to consider when building an effective data and analytics team for your organization.

Roles and Functions Focused on Analyzing, Interpreting, and Communicating Data:

  • Business Analyst: Responsible for being the liaison between IT and the business unit. This role identifies and articulates known problems that data analytics can solve. They assess processes, determine requirements, and deliver insights and recommendations to executives and stakeholders.
  • Business Intelligence Architect/Administrator: Responsible for supporting effective use of data by business professionals and for the design, maintenance, and performance of the business intelligence user environment. The individual in this function is a senior level engineer who uses business intelligence software to make data accessible to the business in meaningful and appropriate ways. This role is important to improving the self-service capacity of an organization by ensuring the structure supports each type of business user from dashboard consumers to hands-on power users.
  • Data Visualization Analyst/Analytics Report Developer: Responsible for creating reporting, dashboards, and analytical application solutions. The individual in this function works to create visual depictions of data that reveals the patterns, trends, or correlations between different points. This role enables business users to have data insights at their fingertips. Having reusable dashboards or analytics already prepared means business stakeholders can spend more time on their business function, interpreting the data and putting data insights into action in their day-to-day business decisions.
  • Data Scientist: Responsible for analyzing and interpreting complex data by combining domain expertise, programming skills, and knowledge of mathematics and statistics. The individual in this function requires analytical data expertise as well as technical skills to clean, transform, and explore data so that they can create value from it and work with stakeholders to make sure they are helping to solve real business problems. Additionally, they leverage Generative AI, applying prompt and flow engineering techniques to enhance data analysis, automate insight generation, and develop Generative AI-driven solutions that address specific business needs and use cases.
This graphic details key roles and functions focused on analyzing, interpreting, and communicating data within a data team. The roles include:Data Visualization Analyst/Analytics Report Developer: Responsible for creating reporting, dashboards, and analytical application solutions. Business Intelligence Architect: Responsible for supporting the effective use of data by business professionals. Data Scientist: Responsible for analyzing and interpreting complex data. Business Analyst: Responsible for being the liaison between IT and the business unit. Each role is essential for transforming data into actionable insights, ensuring effective communication, and supporting data-driven decision-making processes within the organization.

Roles and functions focused on analyzing, interpreting and communicating data.

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Roles and Functions Focused on Preparing, Integrating, Transforming, and Managing Data:

  • Data Architect: Responsible for data architecture and data modeling. The individual in this function is senior level and may work at the enterprise level. The person should be skilled in data modeling and have a good understanding of performing detailed data analysis. A strong data model designed according to best practices improves performance, flexibility, and accuracy when used for analytics and reporting. A skilled data architect can ensure a business is getting answers quickly, supports ad-hoc questions — and helps to promote self-service.
  • Data Engineer/Data Integration Specialist: Responsible for designing and developing data infrastructure to ensure broad availability of data throughout an organization, as well as for implementing systems to integrate (replicate, extract, transform, load) data assets in batch or near-real-time. This function is designed to build systems for collecting, storing, and analyzing data at scale. The data engineer (or ETL developer) works with the business analysts on the source to target mappings to populate a data warehouse and then write the code to transform the data and load it into the target data model. Centralizing data integration and preparation and having a dedicated role for it means data analysts and business users can focus on analyzing data and using insights rather than spending large chunks of time manually combining data sources repeatedly, redundantly, and sometimes inaccurately.
  • Data Governance Administrator: Responsible for defining processes and facilitating the identification and documentation of data definitions, business rules, data quality and security requirements, and data stewards. This role or function oversees an organization’s data management goals, standards, practices, and process, and ensures it is aligned with business strategy.
  • Database Administrator: Responsible for the design, implementation, and support of structured data assets and the performance of the technology that makes data accessible. This function within an organization manages, maintains, and secures data in more than one system so that business users can perform analysis.
  • Quality Assurance Analyst/ Data Quality Analyst: Responsible for determining the fitness of data for use and monitoring the ongoing condition of the data. This function within an organization contributes to root cause analysis of data issues and helps the organization identify business process and technical improvements that contribute to higher quality data.
This graphic outlines roles and functions within a data team that focus on preparing, integrating, transforming, and managing data. The roles include:Data Architect: Responsible for data architecture and data modeling. Data Engineer/Data Integration Specialist: Responsible for designing and developing data infrastructure. Data Governance Administrator: Responsible for overseeing an organization’s data management goals, standards, practices, and processes to ensure they align with their business strategy. Database Administrator: Responsible for the design, implementation, and support of the structured data assets and the performance of the technology that makes data accessible. Quality Assurance Analyst/Data Quality Analyst: Responsible for determining the fitness of data for use and monitoring the ongoing condition of the data. These roles ensure that data is properly managed, integrated, and maintained to support the organization's strategic objectives and operational needs.

Roles and functions focused on preparing, integrating, transforming, and managing data.

How to Fill in the Gaps for an Effective Data Analytics Team

There is no one-size-fits-all approach to building an effective data analytics team, just as there isn’t one operating model that’s better than another. It will always come back to your organization’s specific data and analytics needs, as well as your resources and capabilities at the time.

As you look to scale your analytics maturity and get more value out of your data, you can invest in an in-house data analytics team, or you can utilize consultants or contractors to help fill the gaps.

Watch: Advice for organizations that utilize contractors, consultants, or managed service providers for Data Team functions. This is not about cutting jobs — it’s a way to ensure you get the most from talent investments.

Christina Salmi Christina leads the Data Strategy Service Line, helping our customers to think and act strategically about data and analytics.
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