How to Structure Your Data Analytics Team

Some of the most successful companies are those that have embraced data-driven decision-making. Basing business decisions on real, tangible data brings many benefits, including the ability to spot trends, challenges, and opportunities before your competition. Perhaps most importantly, it allows you to measure progress toward goals so you can understand whether your strategy is working and, if it isn’t, how you might pivot.

If your organization consists of just yourself or a small group of employees, it’s likely everyone is versed in gathering and interpreting data to some extent. As your organization grows, however, it becomes increasingly important to have employees whose job is specifically anchored around data. Depending on your organization, this team may be called the data team or the analytics team.

Below is an overview of the job titles typically included on an analytics team, along with several considerations you should keep in mind as you build yours.

Key Players on a Data Analytics Team

While team structure depends on an organization’s size and how it leverages data, most data teams consist of three primary roles: data scientists, data engineers, and data analysts. Other advanced positions, such as management, may also be involved. Here’s a look at these important roles.

1. Data Scientist

Data scientists play an integral role on the analytics team. These professionals leverage advanced mathematics, programming, and tools (such as statistical modeling, machine learning, and artificial intelligence) to perform large-scale analysis.

While their role and responsibilities vary from organization to organization, data scientists typically perform work designed to inform and shape data projects. They may, for example, identify challenges that can be addressed with a data project or data sources to collect for future use. Much of their time is spent designing algorithms and models to mine and organize data.

2. Data Engineer

Data engineers are responsible for designing, building, and maintaining datasets that can be leveraged in data projects. As such, they closely work with both data scientists and data analysts.

Much of the work data engineers perform is related to preparing the infrastructure and ecosystem that the data team and organization rely on. For example, data engineers collect and integrate data from various sources, build data platforms for use by other data team members, and optimize and maintain the data warehouse.

3. Data Analyst

Data analysts use data to perform reporting and direct analysis. Whereas data scientists and engineers typically interact with data in its raw or unrefined states, analysts work with data that’s already been cleaned and transformed into more user-friendly formats.

Depending on the challenge they’re trying to solve or address, their analysis may be descriptive, diagnostic, predictive, or prescriptive. Data analysts are often responsible for maintaining dashboards, generating reports, preparing data visualizations, and using data to forecast or guide business activity.

4. Advanced Positions

In addition to the job titles above, data teams often include a management or leadership role, especially in larger organizations. These positions include data manager, data director, and chief data officer.

3 Factors to Consider When Building Your Data Team

1. How Large Does the Team Need to Be?

The answer to this question depends on several factors, and there’s no single answer that applies to all organizations. Generally speaking, the larger your organization is and the more data-driven it becomes, the larger your data team needs to be.

In thinking about your data team’s size and which roles it needs to include, ask yourself:

• How much data is the team responsible for managing and working with?
• How many projects will the data team work on in a given period?
• Who will the data team serve? Will they answer to a single stakeholder or department or assist employees organization-wide?

2. How Centralized Does the Team Need to Be?

In some organizations, analytics initiatives are highly centralized, with a single data team serving the entire organization. Other organizations take a more decentralized approach, where each department or business unit has access to its own resources, processes, and employees. Some apply a hybrid model.

While there are pros and cons to each approach, none is inherently right or wrong. The one you employ depends on your organization and its relationship to data. That being said, it can significantly impact your data team’s structure and the data governance processes, so it’s important to consider.

3. What Is the Overarching Data Strategy for the Organization?

Finally, your organization’s data strategy impacts how you structure your data team.

If, for example, there’s an initiative to back every business action in data, then this presumes your organization not only has access to that data, but the processes, tools, and professionals required to conduct significant analysis. On the other hand, if your organization intends to back its larger business strategy in data but is comfortable allowing smaller, daily decisions to be made without data, it may be possible to get by with a smaller team or fewer resources.

The Value of the Data Team

For organizations that pursue data-driven decision-making, a highly skilled data team is essential. Key players include data scientists, data engineers, data analysts, and managerial and leadership roles. If you’re in the process of building your organization’s data team—or expect to significantly interact with one—it’s crucial to understand the different professional roles and responsibilities that make it up.

Are you interested in improving your data literacy? Download our Beginner’s Guide to Data & Analytics to learn how you can leverage the power of data for professional and organizational success.