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Published on November 1, 2021

By Lakshmi Mittra, VP and Head Clover Academy

Data is a major part of every organization. Organizations need professionals who can work with data to derive insights and help with business-critical decisions.

With the recent advancement in technology, data roles are changing along with increased adoption of AI & ML. Many of us are familiar with roles such as data scientist and data analyst but a true data science team consists of much more.

Why Should One Invest in Building a Data Science Team?

Data is an important asset of an organization and the management must plan and build a data science team to utilize the asset (data) and derive information and insights. The following are some reasons that organizations must invest in building a data science team:

To plan future actions based on insights

To identify growth opportunities

To help in better decision making

Organizations structure their teams based on cost, program objectives, and overall organizational structure. However, there are few common data science team structures that are followed globally:

Key Data Science Roles

Regardless of the industry and scale of an organization, data science teams must have the ability to understand business, embrace technology, and deliver analytics. Big players typically have a mix of data science roles and have a large team working in cohesion. Small enterprises may start with one professional who can deliver the required insights and may scale up from there if required.

Here are some key roles to consider when building a data dream team.

In Conclusion

Data science teams can supplement different business units and operate within their specific fields of analytical interest. Depending on the scale of an organization and their analytics objectives, data science teams can be reshaped to boost operational speed and foster business critical decision-making.

Original post:

Top Roles and Skills needed to build a Data Science Team - APN News