Scaling self-serve analytics: The tools empowering 5,000 employees

Datadog scaled self-serve analytics from 200 to 5,000 employees by building an open-source-based platform around three pillars: trusted data, accessible tools, and organizational knowledge. The goal is to let employees answer routine questions and make informed decisions without relying on a centralized Data & Analytics team. This approach combines a single source of truth, self-service data pipelines and transformations, data discovery, quality monitoring, and training.

The Purpose of Self-Serve Analytics

• Datadog’s mission is to “empower everyone at Datadog to make data-informed decisions on their own.”
• Self-service allows Data & Analytics teams to focus on higher-value initiatives instead of handling every request.
• The organization identified three primary user profiles:
  • Analytics Explorers: Need discoverable data and ready-made reports.
  • Analytics Builders: Create reports and run advanced queries.
  • Analytics Experts: Expose new data, maintain business logic, and manage quality.

Data as a Single Source of Truth

• Datadog centralizes product, operational, and business data so consumers work from the same version of reality.
• Its “Bring Your Own Data” (BYOD) tool lets teams expose their own data for analytics.
• The shared data layer supports BI tools, notebooks, data discovery, programmatic access, and machine-learning models.
• Trust depends on:
  • Consistent naming and modeling conventions.
  • Comprehensive documentation.
  • Continuous data-quality monitoring.

Self-Serve Data Intake

• Teams can connect internal and third-party data sources through integrations and BYOD.
• The platform provides scheduling and a user interface for exposing or requesting datasets.
• Pipeline observability covers:
  • Pipeline execution.
  • Data quality.
  • Actionable alerts when failures occur.

Self-Serve Transformation

• Analysts manage their departments’ business logic using SQL and dbt.
• The development environment integrates with workflow management, metadata, and pipeline-run systems.
• Enforced conventions keep the shared modeling layer consistent and understandable as more analysts contribute.
• Analysts can inspect lineage, pipeline runs, quality checks, and alerts.

Data Discovery and Metadata

• Every employee can browse datasets and fields in the central data platform.
• Search capabilities help users identify which data can answer a particular question.
• Metadata explains:
  • The dataset’s origin and owner.
  • Definitions and intended meaning.
  • Where the data is used.
  • Sensitivity and reliability.
• This context helps employees determine whether data is both relevant and trustworthy.

Supporting Adoption

• Tools alone are insufficient; Datadog also provides data knowledge, support, and training.
• The Data & Analytics organization acknowledges that self-service has limits and works to mitigate risks such as misunderstanding data or applying incorrect business logic.
• Success is tracked through adoption and the effectiveness of the overall self-service strategy.

Datadog’s experience suggests that self-serve analytics scales best when data is treated as a product: centralized, documented, observable, and accessible through tools designed for users with different levels of expertise.