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Data Analytics, Visualizations, Data Warehousing

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15 Dec 2021
7 Min read

How To Build & Structure A Highly Efficient Data Analytics Team?

Effective data analytics can give businesses a huge competitive advantage by allowing executives to gain new insights into trends and customer behaviour that would otherwise be impossible to obtain. Therefore, enterprises must have a strong analytics team in place to get the most out of their data resources. 

In today’s heavily data-driven business environment, how companies assemble and manage an analytics team could have a long-term impact on their bottom line. While formulating a team, it is therefore critical for companies to include people with analytical skills and those with business and relationship skills who can help frame the question in the first place and then effectively communicate the findings after the analysis.

How to Build And Structure Your Data Analytics Team?

The first thing to remember is that your data team is a part of the business. It needs to be embedded within the company during the project’s duration. Because advanced analytics is becoming increasingly important to business success, formulate a data science team made up of skilled data scientists and other employees.

Many companies now have a data science team or an entire department; larger companies may have multiple teams that work independently or collaboratively.

Finding an operational model once the data team is in place is the next step. Factors such as the maturity of a company’s data science program, its data analytics goals, overall organizational structure, and enterprise culture influence how teams are structured. 

However, some standard models for data science team structure have emerged, each with its own set of advantages and disadvantages. These models are as follows:

  • Decentralized Model

Members of the data science teamwork within the business units they support. This allows team members to work closely with business executives and workers on data science projects, but it can obstruct the strategic use of data across an organization and necessitates more resources than small businesses may have.

  • Centralized Model

At the enterprise level, the data science function is centralized under a single manager who assigns team members to individual projects and oversees their work.

This model makes it easier to have an enterprise-wide strategic view and implement analytics best practices uniformly, but it limits team members’ ability to become experts in a specific business area. To house a centralized team, some companies establish a formal data science centre of excellence.

  • Hybrid Model 

The data science team is centralized, but members are assigned to specific business units and are responsible for assisting those units in achieving their goals of making data-driven decisions. A centre of excellence in a hybrid structure may also promote data science best practices and standards. Resource constraints, as with the decentralized model, can be a problem.

Crucial Roles In Data Analytics Team

While the structure of a data team varies depending on the size of the company and how it uses data, most data teams have three primary roles: data scientists, data engineers, and data analysts. Other high-level positions, such as management, could be involved as well. 

Some of these crucial roles are as follows:

  • Data Scientist 

On the analytics team, data scientists play a critical role. To perform large-scale analytics, these professionals use advanced mathematics, programming, and tools. They identify problems that can be solved with a data project or data sources that can be collected for future use. Data scientists typically perform work that informs and shapes data projects, though their roles and responsibilities vary by organization. 

  • Data Analyst

Data analysts use data to perform direct analysis and reporting. Analysts work with data that has already been cleaned and transformed into more user-friendly formats, whereas data scientists and engineers work with raw or unrefined data.

Their analysis could be descriptive, diagnostic, predictive, or prescriptive, depending on the problem they’re trying to solve or address. 

  • Data Engineer 

Data engineers are in charge of creating, maintaining, and designing datasets that can be used in data projects. As a result, they collaborate closely with both data scientists and analysts. Data engineers tend to spend a lot of their time preparing the ecosystem and infrastructure upon which the data team and organization rely. 

Data teams frequently include a management or leadership role in addition to the job titles listed above, especially in larger organizations. Data director, data manager, and chief data officer are examples of these positions.

Key Factors To Consider While Building Your Data Analytics Team

The Size Of Your Data Science Team

Your data team will need to grow as your company grows and becomes more data-driven. When considering the size of your data team and the roles that should be included, consider the following:

  • What kind of data does the team have to manage and work with?
  • In a given time period, how many projects will the data teamwork on?
  • Who will be served by the data team?
  • Will they report to a single stakeholder or department, or will they help all employees?

How Centralized Should The Team Be?

Analytics initiatives are highly centralized in some organizations, with a single data team serving the entire organization. Other companies take a decentralized approach, with each department or business unit having its own set of resources, processes, and personnel. A hybrid approach is used by some people. While each method has advantages and disadvantages, none is inherently right or wrong. 

Data Strategy Of Your Company

Finally, your company’s data strategy has an impact on how you structure your data team. If, for example, a project is underway to back every business decision with data, this assumes that your company has access to the data and the processes, tools, and personnel needed to conduct significant analysis. 

Conclusion

Organizations must understand and manage their data if they want to be successful. Your results will be flawed, and any actions taken on them will be incorrect if the data process is unreliable or the data is incomplete. An effective data analytics team assists businesses in making data-driven decisions that are necessary for success.

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