How much does it cost to run a data team?

Data team cost refers to the expenses associated with running and maintaining a data team within an organization. This includes hiring and retaining data professionals, purchasing and maintaining data infrastructure and tools, and consuming resources such as compute power and storage. A data team can cost around $520,000 per year to build and maintain data pipelines, according to Fivetran. An in-house data analytics reporting and analysis team with two representatives can cost about $168,926 per year, plus additional costs.

• Hourly rates: Data analytics services can cost between $80 and $300 per hour, with junior staff or people with limited experience charging less. Analytics consultants can cost between $120 and $1,000 per hour, depending on seniority and specific expertise.
• Custom data analytics solutions: A custom enterprise-grade data analytics solution can cost at least $150,000–$200,000 to build and implement.
• SaaS-based data analytics products: Companies that opt for SaaS-based data analytics products end up paying $10,000–$25,000 per year in vendor and maintenance fees.

What are the responsibilities of a data team?

Data teams are responsible for establishing the integrity of data across all sources and work to establish a source of truth that everyone in the company can trust. They facilitate cross-team collaboration, empower people to use data, promote data-driven decision making, optimize company services, provide competitive advantage through innovation, develop intellectual property, contribute solutions, and educate people across the organization.

• Research: Data teams conduct research and collect data.
• Data pipelines: They build data pipelines for efficient data processing.
• Data visualization: Data teams create visual representations of data.
• Dashboards: They build dashboards for easy data access and analysis.
• Reporting: Data teams generate reports to inform decision-making.

What Types of Data Teams Exist?

Data teams play a crucial role in organizations by helping them make data-driven decisions, optimize processes, and gain a competitive edge. There are various types of data teams, each with its unique focus and skill set. Understanding these types can help organizations build the right team to meet their specific needs.

1. Data Engineering Team

A data engineering team is responsible for building and maintaining the data infrastructure, including data pipelines, storage systems, and data processing tools. They ensure that data is collected, stored, and processed efficiently and securely.

• Example: A data engineering team might build a data pipeline to ingest data from various sources, clean and transform it, and store it in a data warehouse for analysis.

2. Data Analytics Team

Data analytics teams focus on analyzing data to extract insights and support decision-making. They use various statistical techniques, data visualization tools, and reporting methods to present their findings to stakeholders.

• Example: A data analytics team might analyze customer data to identify trends and patterns that can inform marketing strategies.

3. Data Science Team

Data science teams apply advanced analytical techniques, such as machine learning and artificial intelligence, to solve complex problems and make predictions. They often work closely with data engineering and analytics teams to leverage their data and insights.

• Example: A data science team might develop a predictive model to forecast customer churn and recommend targeted interventions to retain customers.

4. Business Intelligence Team

Business intelligence teams focus on delivering actionable insights to business users through reporting, dashboards, and self-service analytics tools. They help organizations monitor key performance indicators (KPIs) and make data-driven decisions.

• Example: A business intelligence team might create a dashboard to track sales performance and identify areas for improvement.

5. Data Governance Team

Data governance teams are responsible for ensuring data quality, security, and compliance with relevant regulations. They develop and enforce data policies, standards, and processes to maintain the integrity and trustworthiness of the organization's data.

• Example: A data governance team might implement data quality checks and monitoring to ensure the accuracy and consistency of data used for reporting and analysis.

6. Data Operations Team

Data operations teams focus on the day-to-day management of data infrastructure, ensuring its reliability, performance, and availability. They monitor and troubleshoot issues, perform maintenance tasks, and collaborate with other data teams to support their needs.

• Example: A data operations team might monitor the performance of a data warehouse and optimize queries to improve response times for analysts.

7. Data Strategy Team

Data strategy teams work with business leaders to define the organization's data goals, priorities, and roadmap. They identify opportunities to leverage data for competitive advantage and align data initiatives with business objectives.

• Example: A data strategy team might conduct a data maturity assessment and develop a plan to improve the organization's data capabilities over time.

How can Secoda help manage the costs of running a data team?

Secoda is a data management platform that helps data teams find, catalog, monitor, and document data, streamlining their processes and improving efficiency. By using Secoda, organizations can potentially reduce the costs associated with running a data team in several ways:

• Data discovery: Secoda's universal data discovery tool helps users find metadata, charts, queries, and documentation, reducing the time spent searching for data and increasing productivity.
• Centralization: As a single place for all incoming data and metadata, Secoda simplifies data management and reduces the need for multiple tools and platforms.
• Automation: Secoda automates data discovery and documentation, reducing manual work and freeing up data team members to focus on higher-value tasks.
• AI-powered: With its AI capabilities, Secoda can help data teams double their efficiency, potentially reducing the need for additional staff and associated costs.
• No-code integrations: Secoda offers no-code integrations, making it easier to connect with various data sources and reducing the need for custom development.
• Slack integration: By integrating with Slack, Secoda allows users to retrieve information for searches, analysis, or definitions directly within the communication platform, improving collaboration and reducing context-switching.