What is a DataOps framework?

A DataOps framework is a structured set of practices, processes, roles and technologies for operationalizing DataOps principles. When implemented effectively, a DataOps framework helps organizations improve the speed, accuracy, reliability and governance of data management and analytics operations. 

The broader concept of DataOps—short for data operations—is a set of collaborative data management practices inspired by DevOps and agile methodologies used in software development and IT operations (such as continuous integration and continuous delivery). It emphasizes collaboration, automation and quality to accelerate and streamline data delivery for trusted, analytics-ready data. DataOps treats data as a strategic asset (also known as a data product) that must be continuously managed, governed and monitored to maximize business value.

In short, DataOps is the overarching discipline, while DataOps frameworks provide the structured methods for putting that discipline into action. DataOps architecture and DataOps tools then bring the framework to life in real-world environments.

Big data volumes are growing rapidly. Artificial intelligence (AI), machine learning (ML) and data analytics demand high-quality, trusted datasets. And data silos are deepening.

These challenges can’t be solved with data lakes or data processing tools alone—the underlying data management and operations need a rewrite. DataOps offers a structured approach that emphasizes automation, collaboration, governance and continuous improvement.

However, turning the concept of DataOps into fully operational and enforceable ways of working is complicated, especially from the ground up. DataOps frameworks provide the practices, processes, roles and technologies integral to implementing DataOps efficiently and consistently across the data lifecycle.

Without a framework, DataOps implementations risk creating inconsistency across teams, misalignment with organizational goals and new quality issues and bottlenecks.

DataOps frameworks help organizations orchestrate several core components of data management and data analytics:

• Workflow orchestration and automation
• Data governance
• Continuous integration and continuous deployment (CI/CD)
• Data monitoring and observability
• Cross-team collaboration

DataOps enables the automation and orchestration of data workflows across every stage of the data pipeline, including data integration, data ingestion, data processing, data storage and data analysis.

By sequencing these activities, DataOps ensures data is efficiently moved and processed so it’s quickly available for analysis. Automation also reduces repetitive tasks—such as data extraction, transformation and loading—and the risk of human errors.

A key aspect of data orchestration is its ability to manage dependencies. This capability helps ensure data is processed in the correct order, and that any changes or updates in one stage do not negatively impact downstream processes.

DataOps frameworks help establish clear data governance structures. Data governance helps ensure data is accurate, consistent and secure. It establishes the policies, procedures and standards that govern how data is collected, stored, managed and used within an organization.

Data quality management is a fundamental part of data governance. It specifically focuses on the practices for enhancing and maintaining the quality of an organization’s data, which can include data validation, data cleansing and the enforcement of data standards.

Continuous integration and continuous deployment (CI/CD) facilitate rapid, iterative development and deployment of data projects. Adopted from software engineering, CI/CD practices involve the automation of the build, test and deployment processes, so that data teams can quickly identify and resolve issues and deliver new features and improvements.

Two key aspects of CI/CD are version control and automated testing:

• Version control allows data teams to track changes to their code and data assets, making it easier to roll back changes if issues arise and reducing the risk of data pipeline failures.
  
  
• Automated testing includes unit tests, integration tests and end-to-end tests that allow data teams to catch and fix issues early. It helps ensure code and data assets meet quality standards and function as expected.

Data monitoring and observability help data teams proactively identify and address issues within data pipelines. They facilitate the collection, analysis and visualization of data pipeline logs, events and metrics (such as data processing times, resource utilization and error rates).

This tracking helps data teams gain insights into the performance and health of data workflows to better identify bottlenecks and optimize pipeline performance. By monitoring data access and usage patterns, data observability can also help organizations maintain compliance with data privacy regulations (such as the GDPR) and identify potential data security risks.

Through shared tools, processes and practices, DataOps breaks down silos and encourages collaboration across teams—data engineering, data science and data analytics—so everyone has consistent access to trusted data when it’s needed.

Collaboration is supported by another byproduct of DataOps: self-service functionality. This capability provides business users with dashboards and other tools for real-time data-driven decision-making.

DataOps also encourages a culture of continuous improvement and innovation. By collaborating closely, teams can more easily identify and address bottlenecks and inefficiencies in their data pipelines and workflows.

Organizations that effectively implement a successful DataOps framework can experience the following benefits:

• Better data quality
• Faster time-to-insight
• Increased efficiency
• Greater agility

DataOps frameworks help accelerate and simplify DataOps implementations. While frameworks differ by organizations, typical elements and steps include:

1. Understanding business needs
2. Defining objectives and a roadmap
3. Forming DataOps teams
4. Establishing data management practices, platforms and tooling
5. Creating governance structures
6. Continuously monitoring and iterating

A common first step in a DataOps framework is to assess the organization’s current data capabilities and landscape. This assessment typically examines data sources, systems, applications, pipelines and data stores in addition to the people, processes and workflows that support them across the data lifecycle.

The goal is to identify any gaps or inefficiencies in existing data management and analytics practices, and to determine the areas where DataOps practices can deliver the greatest business impact.

Once the current state of the business and its data capabilities are assessed, the next step is to define a DataOps strategy and roadmap that outlines clear goals, prioritized use cases and measurable milestones. This step also includes identifying the people, processes, tools, and technologies necessary to operationalize DataOps components such as orchestration, governance, and observability.

A fitting DataOps framework should support iterative improvement, addressing both short-term needs (such as pipeline reliability and data quality) and long-term goals (such as AI initiatives and advanced analytics).

To effectively implement a DataOps framework, it’s essential to establish dedicated DataOps teams (or embed DataOps roles within existing teams).

Team members can come from various functions—such as data engineers, data scientists, data analysts and business end-users—to ensure a collaborative and cross-functional approach. These teams are responsible for implementing, managing and continuously optimizing data management and analytics operations in an iterative manner.

With business needs and team responsibilities defined, organizations should establish data management practices, hardware and software for supporting their DataOps goals.¹

This process includes decisions around how data will be acquired, data transformation performed and data modeled; which data platforms will be used (for instance, data warehouses, data lakes and data lakehouses); and which tools will be adopted for orchestration, observability and governance. It is critical that these practices and tools support scalability as data volumes and business needs evolve.

It is also important to establish clear data governance structures that define the roles, responsibilities and processes for managing and using sensitive data in a consistent and compliant manner. 

These governance structures should be embedded directly within DataOps processes, data strategy and data flows to help ensure data remains high quality, consistent, secure and compliant throughout its lifecycle. Guidelines for access controls, data formats, data lineage, master data management (MDM), metadata and naming conventions can be enforced through automation.

Operationalizing DataOps is an ongoing process that requires continuous monitoring and iteration to ensure optimal performance and outcomes. Data teams should put in place practices and tools to track the performance and health of data pipelines (including dimensions such as schema, lineage and volume), identify and address issues or bottlenecks as they arise, and continuously refine DataOps practices to improve decision-making and drive value creation.