What are the main responsibilities of a data platform team?

A data platform team is responsible for the development and operation of data infrastructure, which helps businesses extract value from data and become data-driven. Their responsibilities include creating, scaling, and maintaining infrastructure that supports data, translating data into insights, creating and maintaining data science models, adding additional data sources to the ETL process, ensuring established SLAs are met, and integrating new data sources for analysis.

Core technical capabilities of a data platform team include Data Engineering, Data Analytics, and Data Science. Other roles may involve Analytics Engineer, ML Engineer, MLOps, and BI Developer.

What is the ideal team size for a data platform team?

An ideal data platform team should have three to eight people. If a data platform build requires more than eight people, it is recommended to split up the teams to focus on different aspects of the platform. This ensures efficient collaboration and manageable workloads.

When scaling a data platform team, consider analyzing the team structure, defining capabilities, recommending a new structure, and implementing and iterating on the changes.

What are some challenges faced by data platform teams?

Data platform teams may face challenges such as poor project planning, supporting a large organization with a small team, having one or two people hold all the institutional knowledge, data integration difficulties, data security concerns, and data quality issues.

Other challenges may include cybersecurity and privacy, lack of data literacy, poor data management tools and processes, lack of clear ownership and accountability, and conflicting priorities and resistance to change.

What are some additional roles within a data platform team?

Beyond the core roles of Data Engineer, Data Analyst, Data Scientist, Analytics Engineer, ML Engineer, MLOps, and BI Developer, a data platform team may also include the following roles:

 
• Data Architect: Responsible for designing and managing the data infrastructure, ensuring it is scalable, reliable, and efficient.
 
• Data Steward: Ensures data quality, consistency, and compliance with relevant regulations and policies.
 
• Data Operations Engineer: Focuses on the deployment, monitoring, and maintenance of data pipelines and workflows.
 
• Data Product Manager: Oversees the development and delivery of data products, ensuring they meet business requirements and user needs.
 
• Data Visualization Specialist: Creates visual representations of data to help stakeholders understand and interpret complex information.
 
• Database Administrator: Manages and maintains database systems, ensuring data integrity, security, and performance.
 
• Big Data Engineer: Specializes in working with large-scale data processing frameworks and technologies to handle massive amounts of data.

These roles can help address various challenges and requirements within a data platform team, ensuring a comprehensive approach to data management and analysis.

How can a data platform team scale effectively?

To scale a data platform team effectively, consider the following steps:

 
• Analyze the team structure using the Team Topologies lens
 
• Define capabilities and group them by affinity
 
• Recommend a new structure and its tradeoffs
 
• Implement and iterate on the changes

Additionally, consider bringing in experts, hiring data engineers, investing in technology, establishing clear processes, maintaining alignment and standards, finding the upper limit of each data role, and setting realistic boundaries for the team's work.

What is the role of a data platform team in supporting Stream Aligned Teams?

A data platform team is responsible for providing the tools, utilities, and technical services that make it easier for Stream Aligned Teams to do their job. By maintaining and improving the data infrastructure, data platform teams enable Stream Aligned Teams to access and analyze data more efficiently, leading to better decision-making and insights.

How can Secoda help a data platform team?

Secoda is a data management platform that assists data platform teams in finding, cataloging, monitoring, and documenting data. By leveraging Secoda's features, data platform teams can streamline their processes and improve efficiency. Some of the ways Secoda can help a data platform team include:

 
• Data discovery: Secoda's universal data discovery tool helps users find metadata, charts, queries, and documentation, making it easier for data platform teams to locate and utilize relevant data sources.
 
• Centralization: Secoda serves as a single place for all incoming data and metadata, simplifying data management and organization for data platform teams.
 
• Automation: Secoda automates data discovery and documentation, reducing manual effort and allowing data platform teams to focus on more strategic tasks.
 
• AI-powered efficiency: Secoda's AI capabilities help data platform teams double their efficiency by streamlining data management processes and providing intelligent insights.
 
• No-code integrations: Secoda offers no-code integrations, enabling data platform teams to easily connect various data sources and tools without extensive coding knowledge.
 
• Slack integration: Secoda's Slack integration allows data platform teams to retrieve information for searches, analysis, or definitions directly within their communication platform, promoting collaboration and quick access to data.

By utilizing Secoda, data platform teams at companies like Panasonic, Mode, and Vanta have been able to improve their data management processes and drive better decision-making across their organizations.

How can Secoda improve data security and compliance for data platform teams?

Secoda can help data platform teams address data security and compliance challenges by providing a centralized platform for managing and monitoring data. Some ways Secoda can improve data security and compliance include:

 
• Access control: Secoda allows data platform teams to set up granular access controls, ensuring that only authorized users can access sensitive data and reducing the risk of data breaches.
 
• Data cataloging: By cataloging data and metadata, Secoda helps data platform teams maintain an organized and up-to-date inventory of their data assets, making it easier to identify and address potential compliance issues.
 
• Automated documentation: Secoda's automated documentation capabilities enable data platform teams to maintain accurate and comprehensive records of their data processing activities, which can be crucial for demonstrating compliance with data protection regulations.
 
• Integration with data governance tools: Secoda's no-code integrations allow data platform teams to easily connect with data governance tools, facilitating the implementation of data security and compliance best practices across the organization.

By leveraging Secoda's features, data platform teams can more effectively manage data security and compliance, ensuring that their organizations meet regulatory requirements and protect sensitive information.

How can Secoda support data platform teams in addressing data quality challenges?

Secoda can assist data platform teams in addressing data quality challenges by providing tools and features that help identify and resolve data inconsistencies, errors, or missing values. Some ways Secoda can support data platform teams in improving data quality include:

 
• Data discovery: Secoda's data discovery tool enables data platform teams to quickly locate and assess data sources, making it easier to identify potential data quality issues.
 
• Centralized data catalog: By maintaining a centralized data catalog, Secoda helps data platform teams keep track of their data assets and monitor data quality across the organization.
 
• Automated documentation: Secoda's automated documentation capabilities provide data platform teams with up-to-date information on data sources and processing activities, facilitating the identification and resolution of data quality issues.
 
• Integration with data quality tools: Secoda's no-code integrations allow data platform teams to connect with data quality tools, streamlining the process of implementing data cleansing and validation processes to ensure accurate data integration.

By using Secoda, data platform teams can more effectively address data quality challenges, ensuring that their organizations have access to accurate and reliable data for analysis and decision-making.