How to Build an Effective Data Analytics Team

According to Gartner, the shortage of skilled talent is the biggest barrier to emerging technologies adoption and business transformation. Businesses are dealing with all sorts of disruptions, but none is evermore present than that of the strained workforce. It’s difficult to find individuals who are trained and experienced in data and analytics—and the last thing you want to do is hire the wrong person. You need to build your data analytics team in a strategic way so that you are set up for long-term success.

But how do you build the right team? What skills do you need? Where do you start? First, you need to identify how data and analytics fit into your overall business operations and then learn the common roles and functions of a data and analytics team. This will help you understand where the talent gaps lie and build an effective and efficient team.

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

Specifically:

• What are the Different Operating Models?↵
• What are the Key Roles and Functions in A Data Analytics Team?↵
• What are the Roles and Functions Focused on Analyzing, Interpreting, and Communicating Data? ↵
• What are the Roles and Functions Focused on Preparing, Integrating, Transforming, and Managing Data? ↵

First, Determine Your Operating Model

How your business chooses to work with data and analytics—your data and analytics operating model—will determine a lot of the staffing and roles necessary to reach your goals and how to best tap into the value of your people.

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

There are three types of operating models—decentralized, centralized, and a hybrid of both. One isn’t better than the other—they’re usually determined by the size of your organization and its data analytics needs. As you scale—or if you’re looking to get more value out of your data—you can change operating models as necessary.

• Decentralized operating model distributes data and analytics responsibilities across different lines of business, as well as IT. There isn’t one centralized authority, but rather a more collaborative approach across the organization to things like data management, data strategy, and business intelligence. A decentralized operating model can lead to strong collaboration and faster time to value, but it can also lead to lack of consistency, data silos, and higher costs across the board. This model is typical for a smaller organization with limited 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 and can make data governance easier, but it can also lead to rigidness and delays with data and analytics initiatives. This model is typical for a more analytically mature organization.
• Hybrid operating model has the best of both worlds—decentralized and centralized—where there is one central authority for data management with decentralized business unit groups across the organization. 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 to have advanced data operations without having to create a dedicated data and analytics business unit for the organization.

What Are the Key Roles and Functions in A Data Analytics Team?

Within each of the operating models, you’ll have roles and functions assigned to data and analytics responsibilities. Some functions and roles can be interchangeable depending on your organization and its needs.

• A role is defined as a job assigned to a person in a particular situation. For example, a senior sales rep in your organization can also be assigned the role of business analyst because of their experience and understanding of the business unit. One person can have multiple roles within an organization. This may occur in all three operating models but is critical for the decentralized and hybrid models to work. Assigning people more than one role can also provide an organization flexibility as they grow their team and/or change operating models.
• A function is the sole duty of someone, and it often requires having a specialized skill.

Identify the gaps in your data analytics team.

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.

Address Each Stage of the Data Lifecycle

It’s important to ensure you have the proper roles and functions that address problems and opportunities across the entire data lifecycle. If, for example, you focus most of your investment only up front on data acquisition and at the end on analysis, you will miss out many opportunities for data enhancement, efficiency, integration, and more along the way. Ensure you are maximizing the value of your data across the entire lifecycle by assigning responsibilities at each stage.

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

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.

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.

Filling In the Gaps with Your 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.

What our Chief People Officer thinks it takes to retain good talent

As you look to scale your analytics maturity and get more value out of your data, you can either build an effective data analytics team in-house, or you can use consulting services to help fill the gaps in the meantime. You should also consider examining your data strategy to make sure it’s up to date, assess the capabilities of your data stack, and think about what it will take to get to your desired future state. These activities are essential to transforming your business with data and analytics.