Automated Data Lineage: Why Do, How To, And Types Of Tools To Use

The importance of organizational access to data lineage continues to grow, as we’ve detailed elsewhere on the blog.

At the scale modern businesses utilize (and increasingly rely upon) data, it’s data lineage processes that foster data quality and trust, regulatory compliance, and operational efficiency. Simultaneously, it supports robust data governance (among other operational criticalities).

This is why it’s important for data teams to get their data lineage right. And that, friends, calls for partial, if not complete, automation of an organization’s data lineage processes.

And for good reason(s).

Why automated data lineage is becoming the new normal

Most organizations now strive to maximize the value of their data assets and ensure data-driven decision-making. This makes automated data lineage an increasingly critical component of modern data management strategies.

By leveraging tools to automate their data lineage processes, organizations gain access to a potent mix of benefits.

• Better scalability and efficiency: As data volumes and complexity continue to grow, manual data lineage tracking becomes increasingly time-consuming, error-prone, and impractical. Automated data lineage tools can handle large and complex data environments, providing real-time lineage updates with minimal human effort.
• Improved data quality: Automated data lineage enables organizations to identify and resolve data quality issues more efficiently. By tracing data flows and transformations, organizations can pinpoint the root cause of data quality problems and take corrective actions promptly.
• Enhanced data governance: Automated data lineage supports robust data governance practices by providing transparency into data flows, transformations, and dependencies. This visibility enables organizations to enforce data policies, ensure compliance with regulations, and maintain a clear audit trail.
• Access to impact analysis: With automated data lineage, organizations can perform comprehensive impact analysis, assessing the potential effects of changes to data sources or transformations on downstream systems and processes. This proactive approach minimizes disruptions and ensures data consistency.
• Improved operational efficiency: By automating the tracking of data lineage, organizations can reduce the time and effort required for manual data mapping, allowing IT teams to focus on strategic initiatives rather than routine data management tasks.
• Enhanced collaboration and knowledge sharing: Automated data lineage tools often provide visualization capabilities, enabling data teams to collaborate more effectively and share knowledge about data flows and dependencies across the organization.
• More robust regulatory compliance: Detailed data lineage documentation is essential for demonstrating compliance with various data protection and privacy regulations, such as GDPR, CCPA, and industry-specific standards. Automated data lineage tools streamline the process of generating audit trails and maintaining compliance.

How to automate data lineage in your organization

For most organizations, the following steps provide a general sense of how data teams will approach automating their data lineage processes.

1. Begin with planning and assessment

It's wise to begin any data engineering initiative by evaluating the organization's data landscape—identifying key data sources, processes, and systems that require lineage tracking.

The team should then clearly outline objectives and goals as part of this step. Automated data lineage is a means to an end—an end that often includes improving data quality, compliance, and operational efficiency.

2. Select the right tools

There are tools designed specifically for data lineage automation, such as erwin Data Intelligence, Atlan, and Alation. However, most organizations will have already invested in other tools that, while not specifically engineered for data lineage automation, can help in the automation process (we'll touch on a few of those shortly).

This is why starting this process with planning and assessment is so valuable, as it should be clear what the best way forward will be. That said, ensure any tools added to the environment will integrate seamlessly with your existing data infrastructure.

A given solution—be that a tool or combination of tools—should offer comprehensive data lineage tracking at the column level, not simply at the table level and/or in a downstream analytics database. Anything less will fall short of offering comprehensive data management.

3. Implement incrementally

Kicking things off with a small-scale pilot enables you to test how you've set up tools and refine your approach to automate the mapping and documentation of data as it flows across systems.

At this point, data contracts should (ideally) be integrated into the process to enforce data quality and governance standards. This will ensure that ongoing automated data lineage processes adhere to all relevant policies.

4. Enable continuous monitoring and maintenance

At this point, implement monitoring systems to ensure the accuracy and completeness of lineage information as the system automatically tracks data lineage.

Refine and update both processes and documentation as changes in the data environment occur. Additionally, regularly review and update data contracts and governance policies, maintaining compliance with regulations and standards.

5. Foster stakeholder collaboration

Work with business and technical teams to foster alignment and buy-in with your objectives and goals.

Training can serve a dual purpose here—furthering alignment for users learning to interface with the automated lineage tools, and the role data contracts and governance policies will play moving forward.

6. Review and optimize regularly

Remember: Automation in data engineering is never a “set it and forget it” proposition. Your newly automated data lineage processes will still need periodic reviews and tuning to ensure maximum effectiveness.

These periodic reviews also create opportunities to scale your data lineage solutions as the organization's data environment grows and changes.

Best practices for automated data lineage processes

Similar to the wisdom inherent in “measure twice, cut once,” the right steps for automating data lineage implemented in the right order serve as best practices, of a sort.

That said, incorporating automated data discovery, pattern-based lineage techniques, metadata management integration, and behavioral science considerations, while optional to some organizations, can further enhance your data lineage automation efforts.

Automated data discovery: Incorporating automated data discovery mechanisms into your data lineage automation process can be a significant time-saver compared to manual tracing. Additionally, tools that offer automated data discovery features frequently identify patterns, anomalies, and connections that are easy to overlook manually. 

For those who have automated their data lineage already, automated data discovery can also help uncover hidden relationships and transformations in your existing data flows that were missed during the initial planning and assessment phase. 

Pattern-based lineage techniques: Adopting pattern-based lineage techniques can minimize the need for manual code inspection. Pattern-based lineage uses metadata patterns to infer data transformations, reducing a data team’s reliance on parsing code directly. 

On the whole, this can simplify the data lineage automation process, making it much more scalable. 

Integrating with metadata management solutions: Metadata capture and management is essential for maintaining accurate and up-to-date data lineage information. 

Therefore, consider integrating specific metadata management solutions (e.g., data catalogs, business glossaries, master data management [MDM] tools) in your lineage automation processes to ensure all relevant details are recorded at each step of the data lifecycle—sources, changes, personnel involved in each step, etc.

Tap into the benefits of behavioral science: Finally, do not (i.e., never) sleep on the potential benefits behavioral science holds for data engineering teams. Apropos of our focus here, data lineage modeling can provide valuable insights into how data is used and interpreted within a given organization. 

How does this help you automate your data lineage processes? Simply put, it doesn’t. But we’d kindly remind you to reap what you sow. Better data lineage should be one means to an organizational end. 

As automation helps your processes improve, make sure you are cultivating the ensuing information gains to design more intuitive and user-friendly solutions, promote the holistic embrace of data as a product, and foster a more data-driven culture within your org.

Common tools you can use to automate your data lineage processes

As mentioned, some data engineering teams may opt to use tools designed specifically for data lineage automation to ensure their processes are pristine. Many others, though, will work to utilize the portfolio of tools and tech the organization has already invested in. In these cases, and as part of the planning and assessment process, it might prove beneficial to approach automated data lineage as a minimum viable product (MVP).

For example, a potential MVP here could focus on automating basic data lineage tracking and visualization capabilities for a specific set of data sources or systems. Teams could then plan out how to integrate additional features—advanced data quality checks, impact analysis, robust compliance support—in subsequent iterations based on user feedback and evolving requirements.

As such, the following four tools are widely used across industries, making the chances good one more of them will be part of a given data team’s solutioning:

dbt (data build tool)

As an open-source command line tool, dbt enables data teams to transform data in cloud data warehouses using analytics engineering best practices. 

It focuses on the "T" (transformation) part of the ELT (i.e., extract, load, transform) process, allowing users to write SQL models that define data transformations. It also integrates with modern data platforms, provides testing and documentation capabilities, and follows software engineering workflows like version control and CI/CD.

Pros:

• Widely adopted and popular tool for data transformation and analytics engineering
• Provides table-level data lineage visualization out-of-the-box
• Encourages good practices like documentation, version control, and collaboration
• Can be integrated with other tools to enhance lineage capabilities

Cons:

• Lacks native column-level lineage tracking, which is essential for comprehensive lineage
• Limited to tracking lineage within its own models and transformations
• May require additional tools or custom development to achieve end-to-end lineage

Pricing:

dbt provides users with the choice of a free open-source version and dbt Cloud, which starts at $50/user/month.

MANTA

MANTA is a data lineage tool that provides automated, end-to-end lineage tracing across various systems and technologies. 

It maps data flows, including direct and indirect dependencies, to help organizations understand, analyze impact, ensure data quality, and comply with regulations. MANTA offers features like detailed technical lineage, data flow history comparisons, filtering, and integration with data catalogs.

Pros:

• Specialized data lineage tool with automated lineage tracking capabilities
• Provides detailed, column-level lineage across various data sources and systems
• Offers impact analysis and root cause analysis features
• Supports compliance and governance requirements

Cons:

• May have a steeper learning curve and require more specialized expertise
• Pricing and implementation costs can be higher compared to other options
• Integration with existing tools and processes may require additional effort

Pricing:

MANTA offers custom pricing based on organizational needs.

Collibra

Collibra is a comprehensive data governance platform that offers robust data lineage capabilities along with other data governance features like data cataloging, stewardship, collaboration, and compliance management. 

It provides a business-friendly interface, facilitates organization-wide data understanding, and enables integration with various data management and analytics tools.

Pros:

• Comprehensive data governance and catalog solution
• Automates data lineage mapping and visualization
• Supports business and technical lineage views
• Integrates with a wide range of data sources and systems

Cons:

• Primary focus is on data governance, with lineage as a secondary feature
• May not provide the same level of granularity and depth as specialized lineage tools
• Pricing and implementation costs can be substantial for larger organizations

Pricing:

Collibra offers custom pricing based on organizational size and needs.

Informatica Enterprise Data Catalog

Informatica Enterprise Data Catalog is an AI-powered data catalog that automates the discovery, scanning, and cataloging of data assets across an enterprise's multi-cloud and on-premises environments. 

It provides features like semantic search, data lineage visualization, data profiling, quality scorecards, data similarity recommendations, and integration with Informatica's data governance and integration solutions.

Pros:

• Robust data catalog with automated lineage tracking capabilities
• Provides end-to-end lineage across various data sources and systems
• Offers advanced features like data quality scorecards and impact analysis
• Integrates with other Informatica products for a comprehensive solution

Cons:

• May require additional Informatica products or modules for advanced lineage features
• Primarily focused on data cataloging, with lineage as a secondary feature
• Pricing and implementation costs can be higher for larger organizations

Pricing:

Informatica offers custom pricing based on organizational requirements and scale.

From automated data lineage to comprehensive data contracts

As shown here, embracing automated data lineage processes is no longer optional for modern organizations aiming to maintain high data quality, regulatory compliance, and operational efficiency. Automation transforms the tedious and error-prone manual lineage tracking into a seamless and scalable solution, offering real-time updates and enhanced visibility into data flows and transformations.

As impactful as it is, however, automating an organization’s data lineage processes is the beginning, not an end, to more efficient, impactful data engineering practices. 

To stay ahead in this data-centric era, it’s crucial to adopt these automated solutions. If you're ready to elevate your data management strategy, sign up for our product waitlist today and learn more about how next-level data contracts can build on the foundation data lineage enables.

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