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Make Sure Your Big Data Team Includes the Right Members   Featured

Make Sure Your Big Data Team Includes the Right Members    Javier Allegue Barros

Some of the world's biggest and most successful companies have one thing in common; they have embraced data-driven decision-making. This is the step that has made them successful than others and is perhaps one of the reasons they have better customer satisfaction than the others. While big data is important as part of the decision-making process, it is not the only thing. There is a need for a comprehensive combination of the big data processes. As a leader in an organization, make sure you have a big data team that includes the right members. Although every organization may have a different big data team structure, here are some of the most important team members and what they do.

  1. Data scientist

Data scientists play an important role in the big data and analytics team and are one of the most in-demand professions. They use advanced mathematics and programming knowledge and tools such as artificial intelligence (AI), statistical modeling, and machine learning to conduct large-scale analyses. The roles of data scientists differ from one organization to the other. However, they typically perform work that helps in shaping up projects. They use their data analysis skills to identify the challenges and patterns that an organization for future use. Data scientists spend most of their time designing algorithms and models that can help mine and organize data.

  1. Data engineer

Datasets are a significant part of data analysis. Designing, building, and maintaining datasets is the work of data engineers. These professionals work closely with other team members such as data scientists and analysts to ensure suitable datasets are developed. Their work is primarily related to the preparation of the ecosystem and infrastructure used by the data team.

  1. Data analyst

These professionals use data to carry out reporting and direct data analysis. Unlike data scientists and engineers who typically interact with data in its raw and unrefined formats, analysts work directly with data that has been cleaned and transformed into user-friendly formats. Their analysis can be predictive, descriptive, prescriptive, or diagnostic, depending on the challenge they are trying to solve. They maintain dashboards, generate reports, prepare data visualizations, and forecast or guide business activity using data patterns. 

In addition to the above professionals, other people who play a crucial role in the big data team and its activities include the data director, chief data officer, and data manager. There are also various factors to consider when building your big data team. These are:

  1. The size of the desired team

Various factors determine the size of the desired team. Generally, the size of the organization mostly determines whether it is data-driven or not determines the size of the big data team needed. While determining the size and the roles of the team, ask yourselves questions, including the roles that should be included, the projects that the team will work on in a specified period, and the people that the team will serve.

  1. How centralized the team should be

Big data analytics projects are highly centralized. It involves one team serving the entire organization. However, some organizations take a more decentralized approach where every department has its resources, processes, and employees. Decide whether you want your organization to be centralized or decentralized.

  1. What is the data strategy for your organization

The data strategy that the organization wants to adopt significantly affects the structure of a data team. For instance, if you want each business action to be backed up by data, your organization must always have access to processes, tools, and professionals needed to conduct significant data analysis. On the other hand, you can have a smaller team if only a few decisions need to be made based on data.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

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