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:

• Centralized – The team is consolidated under a single manager to oversee their work, and to assign individual projects. This model allows for a strategic view and an enterprise-wide implementation of analytics. Organizations also form a center of excellence to operate the centralized team.
• Decentralized – The data science teams are a part of individual departments and work in conjunction to the process they work for. This allows teams to work closely with the process they are assigned to but it fails to create a strategic view of data science as a whole.
• Hybrid – Teams are managed centrally but are assigned different processes within the organization. This helps in having a strategic view of the team as well as enables team members to work closely with the individual processes assigned.

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.

• Data scientist – use statistical methods, machine learning algorithms, and other tools to analyze data and create predictive models. They have a variety of skills in areas such as mathematics, statistics, data mining, and coding.
• Data Analyst – ensures that collected data is relevant and exhaustive while also interpreting the analytics results. They also have visualization skills to showcase graphical presentation of data and numbers.
• Data Engineers – collect and manage data. They also manage the storage of the data. Their core skill is to work with large amounts of unstructured and raw data and prepare it for others for further consumption.
• Data Architect – works with Big Data. Data architect oversees the implementation of the underlying data infrastructure on which the analytics run. Their primary job is to define database architecture, centralize data, and to ensure integrity across different sources.
• Machine Learning Engineer – designs and deploys the algorithms and models needed for machine learning and AI initiatives.
• Business Analyst – converts business objectives into data analysis. They act as a conduit between the data science team and the management.

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.