What the Modern Data Team Looks Like and Where It’s Headed
To tackle the challenges and advanced use cases ahead, companies need to rethink the structure of their data teams.
The cloud has become the cornerstone of data innovation as enterprises move their data and analytics effort to cloud platforms to expedite the time to insights for business decisions. Most companies are opting for a cloud-first approach to cloud data warehouses for their flexible and scalable architecture. Companies are also pursuing hybrid and multicloud strategies. Growing data volumes — and increasing data complexity — make scaling a data management strategy nearly impossible unless teams can implement a hybrid or cloud-first approach and enable self-service data inside the organization.
These trends are driving changes in today’s data teams. The highly technical coding skills and other hands-on tasks that were in high demand even a few years ago just to keep workflows going are giving way to low-code and no-code tools. Data engineers will always be a critical part of any modern data team, but the kind of hand-coding that was once routine is nearly impossible with today’s data volumes.
To tackle the challenges ahead, companies need to rethink the structure of their data teams.
What roles and responsibilities make up the modern data team? The job titles will vary depending on the business and industry, but each team member’s responsibilities fall into one of the following five categories.
1. A technical role — a data engineer or an ETL developer — builds workflows. This person will be responsible for making sure data pipelines and ETL jobs are running and colleagues have the data access they need for their projects. This team member oversees data migration, orchestration, and transformation and helps develop applications and systems that drive advanced analytics use cases, including AI and machine learning.
2. Data scientists are necessary to derive value and insight from data. Although data scientists are normally situated inside IT, nowadays it’s normal to find them in other parts of the business. Data scientists find innovative ways to work with data and help teams achieve a rapid ROI on analytics efforts using methods including data curation or advanced search, matching, and recommendation algorithms. Data scientists need access to the highest quality of data and large amounts of computing resources to extract deeper insights. Using the data scientists’ time for sourcing, preparing, and checking data in the warehouse is wasteful.
3. A data analyst queries and reports on data in the data lake or cloud data warehouse. Analysts can use these findings to build interactive charts and dashboards for business user reporting, diagnostics, and decision making.
