This article examines the challenges and trends in data lineage. Now, we focus on those that arise from data management and governance.

In Part 1 of this article, we examined why data lineage has become essential, how external drivers such as regulation and AI adoption are influencing the conversation, and how organizations are addressing technological challenges. But technology is only part of the picture.

In this second part, we shift the focus to data management and governance, including human challenges that make or break data lineage programs—and examine the trends that are helping organizations overcome them. Finally, we’ll wrap up with an action plan you can use to assess and improve your own data lineage capability.

Data Management Challenges

Even the best tools and architectures can’t make data lineage succeed without foundational management structures. These include standards, metadata models, stewardship roles, and scalable delivery approaches. Unfortunately, many organizations are still working through the basics.

Challenge 6: Closing Standardization Gaps

Inconsistent standards are a common root cause of lineage failure. Different teams may document lineage in different formats, with varying levels of detail, terminology, and structure. Without alignment, lineage artifacts are difficult to integrate, maintain, or trust.

These issues stem from the absence of:

  • A unified lineage data model
  • Agreed definitions for what counts as a transformation or flow
  • Guidance on how lineage connects to glossaries, data quality, and business rules

Without this foundation, lineage remains fragmented and isolated within systems, teams, or initiatives.

Challenge 7: Managing Costs and Resource Constraints

Lineage initiatives require sustained investment—in tooling, training, governance coordination, and ongoing maintenance. But for many organizations, lineage is seen as a “nice to have” or a technical luxury rather than a business necessity.

This leads to:

  • Underfunded lineage programs that stall or disappear
  • A lack of dedicated roles or teams to manage lineage long-term
  • Short-term projects that fail to demonstrate value or align with broader goals

Cost is often the barrier that keeps lineage stuck in pilot mode.

Trends That Address Management Challenges

The following trends are helping organizations strengthen their lineage foundations, demonstrate value, and reduce delivery risk.

Trend 6: Metadata and Lineage Standardization Frameworks

To close governance gaps, leading organizations are now investing in enterprise-level standards for lineage.

This includes:

  • A shared lineage data model, defining the core entities (e.g., source, transformation, target) and relationships
  • A controlled vocabulary for types of data movement (e.g., copy, join, aggregate, derive)
  • Templates and checklists to document lineage for new pipelines
  • Guidelines for connecting lineage to data quality rules, glossary terms, and compliance controls

These standards don’t just improve documentation—they also make it easier to integrate tooling, automate checks, and build trust across teams. In short, standardization reduces friction and increases the reusability of components.

Trend 7: Embedded Management and Lineage Validation

Another powerful trend is automating governance into delivery workflows.

Rather than relying on separate, manual lineage efforts, organizations are embedding lineage into:

  • CI/CD pipelines, where automated tests check whether lineage has been captured and validated before deployment
  • DataOps workflows, where lineage updates are triggered by changes in metadata or pipeline logic
  • Quality and risk dashboards, where missing or incomplete lineage is flagged as a control failure

This approach turns lineage into a non-negotiable part of data delivery—just like access controls or test coverage. It improves completeness and enforces quality without relying on manual review.

Trend 8: Value-Based, Phased Lineage Implementation

To address cost concerns, organizations are shifting from “big bang” lineage implementations to phased, value-driven strategies.

The new playbook looks like this:

  1. Start small—identify one domain with clear value (e.g., regulatory reports, financial KPIs, AI model inputs)
  2. Demonstrate impact—show how lineage improves audit response, accelerates issue resolution, or supports governance
  3. Build reusable components—scripts, templates, and connectors that can be scaled
  4. Expand with momentum—use success stories to justify investment and involve more teams

This approach avoids boiling the ocean. It allows lineage to grow organically, aligned to real needs and real results.

Trend 9: Modular, Open, and Ecosystem-Friendly Tooling

Instead of locking into large, expensive platforms, many teams are now building lineage capabilities using modular, open-source, and composable components.

This includes:

  • Open-source lineage extractors (e.g., Marquez, OpenLineage)
  • Lightweight visualization layers
  • Integration with existing data catalogs, orchestration tools, or observability platforms
  • REST APIs for flexible metadata ingestion and export

This modular approach:

  • Reduces upfront cost
  • Increases flexibility to adapt to change
  • Enables gradual implementation across different teams or technologies

It also allows lineage efforts to scale at the pace of business maturity—not vendor roadmap.

Data Governance (Human and Organizational) Challenges

Even with the right tools and governance in place, lineage can still fail if the human side isn’t addressed. Collaboration, ownership, and sustainability are essential.

Challenge 8: Breaking Through Collaboration Silos

Lineage spans functions: engineers build pipelines, analysts build dashboards, stewards define meaning, and compliance officers define rules. But these roles often work in isolation, using different systems, terminology, and assumptions.

When there’s no shared ownership:

  • Lineage efforts become disconnected and inconsistent
  • Teams disagree about what lineage means or what it’s for
  • Opportunities for reuse, integration, and governance are missed

True lineage requires cross-functional coordination, not just technical alignment.

Challenge 9: Sustaining Lineage Over Time

Lineage is not a “one and done” activity. Systems evolve, teams change, and business definitions shift. Without sustained effort, even well-designed lineage becomes outdated quickly.

Common signs of this challenge:

  • Lineage is documented once, then forgotten
  • Ownership is unclear, and updates are nobody’s responsibility
  • When issues arise, lineage is missing or wrong, leading to distrust

To be useful, lineage must be treated as a living capability, maintained like any other critical data asset.

Trends That Support Data Governance, Collaboration, and Sustainability

These final trends focus on the people behind lineage—how to align, engage, and support them over time.

Trend 10: Cross-Functional Stewardship Models

Leading organizations are adopting shared stewardship structures for lineage. Instead of relying on a single team (often IT) to document everything, responsibilities are distributed across various roles.

Examples:

  • Engineers tag pipeline steps and update technical lineage
  • Stewards review and approve business-level lineage mappings
  • Analysts flag unclear or missing lineage in reports
  • Governance teams review completeness and support tool access

These roles are formalized—often through RACI matrices—and supported by:

  • Training and onboarding
  • Expectations built into role descriptions
  • Incentives tied to lineage quality and completeness

By sharing the load, lineage becomes more comprehensive, accurate, and resilient to turnover.

Trend 11: Collaborative Lineage Platforms

Tools are evolving to support collaboration and shared ownership. Today’s lineage platforms include:

  • Review and approval workflows, where changes are tracked and reviewed
  • Inline commenting and discussions, similar to GitHub or Google Docs
  • Versioning and change logs, so updates are transparent
  • Access controls, enabling teams to contribute without compromising governance

These features help teams work together in real time—closing knowledge gaps and reinforcing the idea that lineage is everyone’s job.

Trend 12: Lineage as a Monitored, Living Asset

The final and perhaps most transformative trend is the operationalization of lineage. Mature organizations treat lineage like they treat data quality:

  • Health checks identify stale or broken lineage
  • Alerts trigger when pipelines change without corresponding lineage updates
  • Dashboards show lineage coverage, freshness, and impact
  • Reviews are scheduled monthly or quarterly, just like compliance checks

This shift ensures that lineage stays relevant—not just at go-live, but over time. It also helps build trust. When users know lineage is current and reviewed, they’re more likely to rely on it in their work.

Final Reflection: Turning Lineage from Burden to Value

It’s clear that data lineage is no longer a technical nice-to-have—it’s a foundational capability for governance, compliance, analytics, and operational reliability.

However, making lineage work requires more than just tools. It requires clarity of purpose, organizational commitment, and a realistic approach to execution.

What we’ve seen in this article is that the most successful lineage programs don’t try to do everything at once. They start where value is clear, involve the right people, build on standards, and adapt as they grow.

Your Action Plan: Five Steps to Move Forward

Here are five next steps to make progress in your own data lineage journey:

  1. Focus on a high-value use case first
    Identify one domain where lineage has a clear business, compliance, or operational impact—such as regulatory reporting or AI model inputs.
  2. Define clear ownership and expectations
    Assign roles for creating, reviewing, and maintaining lineage. Make it part of job descriptions and governance structures.
  3. Standardize your approach
    Create templates, naming conventions, and validation rules to align how lineage is documented and used across the organization.
  4. Automate what you can, supplement what you can’t
    Combine automated capture with manual enrichment. Use CI/CD pipelines to enforce minimum standards.
  5. Treat lineage as a living asset
    Review it regularly. Monitor it continuously. Update it as systems and business needs evolve.

Those interested in the comprehensive approach to establishing a data lineage business case can consult my book, Data Lineage from a Business Perspective.

Lineage is hard—but it’s getting easier. These trends show that there are practical, proven ways to overcome the barriers and build something sustainable.

And with trust in data more important than ever, that effort is well worth it.