Building A High-Impact Data Analytics Team (Plus Advice for Heads of Data)

If you’ve been tasked with building or optimizing a data analytics team, you have a lot to consider: the roles you need, how to structure the team, the best time to scale, who should lead, and how to align work with business priorities.

We have worked with many clients to build teams from scratch, optimize existing team structures, and identify gaps. Through our experience, we can tell you that there is no single optimal way to build your team. Your data team needs depend on the analytical maturity across the organization, size, company culture, and short- and long-term goals of your organization.

This blog will guide you through what to consider for your data team, providing a structure that will help you hone in on what’s necessary to build and enable a team that supports your goals.

Specifically:

• Determining Your Operating Model ↵
• Optimizing Your Data Analytics Team for Each Stage of the Data Lifecycle ↵
• Common Roles & Functions of Data Analytics Teams ↵
• Beyond the Org Chart: How to Increase Effectiveness of Data Teams ↵
• Tips for Data Leaders: How to Be an Effective Data Leader ↵

Determining Your Operating Model

Your data and analytics operating model will largely determine the staffing needs and organizational structure required to achieve your goals and utilize the value of your people. The right model is usually determined by the size of your organization, how that organization is structured today, where you are on the analytics maturity model, and your data analytics needs.

Are you building a team from scratch? Do you already have data leadership in place? Who do you want reporting to whom? Answers to these types of questions will help you determine the best operating model for your organization.

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

There are many kinds of operating models for data teams, but the three most popular are: decentralized, centralized, or hybrid:

• Decentralized operating model distributes data and analytics responsibilities across different lines of business, as well as IT; there isn’t one centralized data team or authority. 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 for the domains the distributed teams are working within because business units have the autonomy to do their own analysis. However, it can also lead to lack of consistency, data silos, and higher costs due to overlapping technology and processes. Additionally, it can result in a loss of ownership and strategic misalignment across the organization.This model is typical for smaller organizations with limited data analytics resources, multinational organizations that naturally segment operations across regions/subsidiaries, or franchise-based businesses looking to provide autonomy to individual franchisees.
• 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 clear-cut decision-making and less redundancy, but it can also lead to rigidity and delays with data and analytics initiatives due to having to funnel all projects, requests, and initiatives through a singular function.This model is typical for a more analytically mature organization looking to control its unified strategy and requires more data governance, processes, and committee-based decision-making in order to execute effectively.
• 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 aims to achieve consistent data management and data governance while providing each line of business the freedom 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. While designed to be the best of both worlds, without proper collaboration and processes, a hybrid model can face challenges similar to those in decentralized functions.

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.

Optimizing 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. If you are dedicating resources only to data acquisition and analysis, for example, that means you are 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. 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 they should collaborate to ensure a seamless data workflow, enhancing the overall effectiveness of your data team.

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.

Common Roles and Functions of a Data Analytics Team

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.

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

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.

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

Beyond the Org Chart: How to Increase Effectiveness of Data Analytics Teams

While the org chart is important for supporting your goals, talent alone will not produce the results you want. The team must know their priorities, the values and standards they operate by, and how they support the company.

To build an effective data analytics team, consider the following key practices and principles:

• Make Data Quality and Usability the Driving Force Behind the Data Team: If you want stakeholders to adopt what your data team creates, you must build trust in the data. Assess data governance policies, data quality, and integrity. Make data accuracy, completeness, and consistency an ongoing pursuit. Involve stakeholders in the development process so they understand what is being created and how.
• Establish Clear Channels of Communication Between Data Teams and Stakeholders: Positive relationships between the business and data teams result in high adoption rates of data products. Define methods for stakeholders and data consumers to request, track, and understand ongoing work. Foster open communication between your team and decision-makers to ensure continuous alignment and investment in data projects.
• Ensure Team Members are Clear on Their Responsibilities AND the Bigger Picture: Ensure every member understands their role and how their work supports organizational goals. To encourage bigger-picture thinking, familiarize the team with how their work is making a real impact in the company. Motivate them to look beyond immediate tasks and see how their contributions create significant changes in the business’ decision-making. When team members see their impact, they will better appreciate their contributions — driving success and meeting stakeholders’ data needs and challenges.
• Invest in Ongoing Skill Development: A learning culture keeps the team adaptable and current with optimal data practices, theories, and technologies. Promote knowledge sharing within the team to set a high standard of proficiency and drive innovation and adaptability. It’s best to have a diverse skill set on the team so you are prepared to handle a variety of challenges that arise across the data analytics lifecycle.
• Have a Roadmap Outlining Short-Term Initiatives and Long-Term Goals: This will help address the company’s immediate data needs while acknowledging and planning for future growth and improvements. By having a clear roadmap, the team knows what they are working on and why, ensuring sustained focus and direction.

Watch: Our VP of Consulting, Kevin Lobo interviewed Josh Johnston who served as Head of Data for two of our enterprise clients. Watch the video and learn from Josh’s perspective on building and leading D&A teams and championing their success!

How to be an Effective Data Leader

Being an effective data leader takes a unique skill set. It demands a blend of technical proficiency, strategic vision, and exceptional communication skills. A data leader, such as Head of Data, has the primary role of building and fostering a data-driven culture within the organization.

Here are the necessary pillars for success:

• Align your data projects with priority business objectives: A data leader understands the broader business context to effectively align data projects with organizational objectives. They recognize interdependencies, manage stakeholder expectations, and ensure data initiatives address real business needs.

“Always be prepared to explain the ‘why’ behind your initiatives, clarifying the purpose and benefits to make them easily understood by the organization. This transparency and dedication to quality will drive the adoption of your solutions and reinforce trust in your leadership.” – Kevin Lobo, VP of Consulting

• Translate complex data initiatives into business value: A data leader must have the technical proficiency to lead a team of data professionals, but they must also know how to provide the right amount of context to the business to gain their support. They should be able to engage in informed conversations and provide mentorship to the technical team —translating technical concepts into strategic opportunities. Their ability to bridge the gap between complex data and business value is crucial for delivering impactful results.

“You need to be technically capable, but you also need to be able to socialize the meaning and value of the work your data team is doing to people who aren’t as technical as you.” -Josh Johnston, A8 Consultant

• Unite the organization to build a data-driven culture: A data leader continuously engages stakeholders in data team initiatives to build confidence in the solutions and get everyone invested in the data team’s success. To increase adoption of data solutions, they provide ongoing education on data solutions and opportunities for feedback.

• Set the vision and give autonomy to top talent: In the early stages of team development, a more hands-on approach may be necessary due to limited resources. However, as the team matures, a data leader should provide clear strategic direction while granting autonomy to their team. The data analytics industry moves fast, and there are many ways to handle data challenges — tap into your talent and let them take initiative.

“As a data leader, it is your job to set direction, define strategy, and provide the support your people need to succeed, and then just let them do their job.” – Josh Johnston, A8 Consultant

Effective data leaders bridge the gap between technical execution and business strategy, driving significant value for their organizations and positioning their teams for long-term success. By focusing on these key areas, data leaders can lead their teams to excel and make a meaningful impact.

Need help optimizing your data 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 get more value out of your data, our experts can help you identify your gaps, map out the optimal team makeup, and fulfill staffing needs if necessary.