Charles B. Dewitt 
Data is an important part of every organization. Organizations need professionals who can work with data to derive insights and help them make critical decisions. With recent technological advancements, the roles of data are changing with the increased adoption of AI and ML. Many of us are familiar with roles like data scientist and data analyst, but a true data science team includes so much more.
Why invest in building a data science team?
Data is an important asset of an organization and management needs to plan and build a data science team to use the asset (data) and derive insights and insights from it. Here are some reasons why organizations should invest in building a data science team:
- To plan future actions based on information
- To identify growth opportunities
- To help with better decision making
Organizations structure their teams based on cost, program goals, and overall organizational structure. However, there are few common data science team structures that are followed globally:
- Centralized – The team is under one manager to oversee their work and assign individual projects. This model enables strategic insight and implementation of enterprise-wide analytics. Organizations also form a center of excellence to operate the centralized team.
- Decentralized – Data science teams are part of individual departments and work in conjunction with the process they work for. This allows teams to work closely with the process they’re assigned to, but it doesn’t create a strategic view of data science as a whole.
- Hybrid – Teams are centrally managed but assigned different processes within the organization. This helps to have a strategic view of the team and allows team members to work closely with assigned individual processes.
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. Large players typically have a mix of data science roles and have a large team working in cohesion. Small businesses can start with a professional who can provide the required information and can scale from there as needed.
Here are some key roles to consider when building a dream data 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 math, statistics, data mining, and coding.
- Data Analyst – ensures data collected is relevant and comprehensive while interpreting analysis results. They also have visualization skills to present a graphical presentation of data and figures.
- Data Engineers – collect and manage data. They also manage data storage. Their core skill is working with large amounts of unstructured, raw data and preparing it for others to consume later.
- Data Architect – works with Big Data. The Data Architect oversees the implementation of the underlying data infrastructure on which analytics run. Their main task is to define the architecture of the database, to centralize the data and to guarantee the integrity of the various 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 an intermediary between the data science team and management.
In conclusion
Data science teams can complement different business units and operate in their specific areas of analytical interest. Depending on the size of an organization and its analytics goals, data science teams can be reshaped to accelerate operational speed and support business-critical decision making.
The article was written by Lakshmi Mittra, Vice President and Director – Clover Academy
