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How data lineage became a boardroom metric
Data lineage has moved beyond a technical function, becoming a board-level signal of how well organizations govern, audit and explain their data across complex environments.
It's common to hear executives say they want decisions at all levels of their organization to be data-driven. That goal is both cultural and technical, and as more companies use more data in this way, it raises a vital question: Do you know where your data comes from?
This is the problem of data lineage. It begins as a question of trust, since claims about data-driven capabilities are meaningless without evidence of how data is governed, traced and controlled. Increasingly, that question is being raised at the board level, as regulations around the world converge on a single expectation: Organizations must be able to demonstrate, with technical evidence, where their data originates, how it has been transformed and who was responsible for it at each stage.
Faced with rising regulatory and trust expectations, executives now operate under a dual mandate. They oversee the infrastructure that establishes trust in data, but they also communicate those capabilities clearly. In practice, this means explaining technical systems and data practices in a language that regulators and investors can easily understand and audit.
The pressure of regulation
The EU AI Act, adopted in August 2024, treats data lineage as a form of legal proof of compliance for high-risk AI systems. Auditors expect organizations to trace training data for AI systems from its origin through every transformation. Well-documented policies alone are insufficient; lineage must be recorded as data moves through systems to show that governance controls have been applied consistently.
In the U.S., the SEC identified AI disclosures as a priority in 2024, signaling increased scrutiny over whether companies can substantiate claims about their AI capabilities. A likely precedent is the Commission's cybersecurity disclosure rules, which require rapid, evidence-based reporting of all incidents.
For many data teams, these expansions represent a considerable burden. Enterprise environments often include dozens of systems that were built by different teams, at different times and for different purposes. Few were designed with the thought of how an auditor in 2025 might want to trace a business decision back to its source.
Lineage and modernization
As organizations migrate from fragmented legacy databases to cloud-based platforms, modernization creates an opportunity to embed lineage directly into new architectures. Indeed, it might be critical to do so. Without it, modernization risks producing systems that are faster and more scalable, but are still opaque.
One approach is to create a digital twin of the data infrastructure -- a live model that maps how data flows across systems. Unlike static documentation, a digital twin is updated continuously, in near real time. While effective, this approach can be complex to implement. Specialized enterprise data platforms address this challenge in several different ways, depending on organizational maturity and technical needs.
For example, Alation emphasizes lineage with governance context baked in, tracing data from source systems to dashboards, with a focus on data flows. The goal is to make lineage accessible for business users, not just engineers. The hope is that broad adoption across technical and non-technical teams drives more value than depth alone.
Collibra takes a governance-first approach, integrating its data catalog with data quality, privacy and policy management into a unified platform. Graph-based metadata structures connect business terms to technical assets, which can be useful for large enterprises seeking to standardize definitions and workflows across fragmented ecosystems.
Informatica, one of the most established data management vendors, is commonly used by organizations with dedicated governance teams and complex hybrid environments. Its depth makes it a popular choice in heavily regulated industries, though that depth demands a proportional investment in expertise and often complex implementation.
Telling the story of lineage
Whatever the technical approach, effective data governance programs serve not only a defensive role but also an enabling one, increasing confidence in data and supporting longer-term advantages in innovation and insight.
These broad advantages introduce another challenge for the Chief Data Officer: different stakeholders define success in different ways. Data stewards focus on the adoption of certified datasets, compliance officers want fewer exceptions in their audits, and executives want faster, more reliable decisions. It's tricky to communicate across these perspectives simultaneously.
That translation begins with metrics that extend beyond technical details to reflect business relevance, including measures suitable for board-level reporting. The following are examples of lineage metrics that speak effectively to different audiences.
Time might be the most persuasive metric. In many organizations, staff spend many hours cleaning and preparing data for analytics and AI use cases. Framing modernization as a way to reduce that manual work speaks directly to operational leaders.
Metrics such as these increasingly appear in formal disclosures. For example, Environmental, Social and Governance (ESG) reports often document a company's frameworks for data and AI governance. In some cases, this reporting is mandatory; the 2024 SEC climate reporting rules require auditable data trails for emissions figures to show they have been calculated with sufficient evidence and reliability. Data lineage provides a means to substantiate claims that might otherwise be seen as mere greenwashing.
Metrics on their own are insufficient. Organizations that communicate data governance successes internally tend to have higher and more confident adoption of analytics and AI tools. Gaps often persist between executives' perceptions of their data culture and the day-to-day reality.
In their Data Culture Survey 2023, the analyst firm BARC reported that "Overall, the CxO’s view of initiatives that have already been implemented is significantly more positive than in general. Employees in operational functions and data and analytics leaders and experts report less widespread activity, so there is definitely work to be done here to convince top management that competence and communication are still nowhere near as far advanced as they think."
AI governance and lineage
Since 2023, the governance of business decisions has become more complex with the widespread adoption of generative AI, since the operations of AI systems are not always visible or explainable.
Regulators are adapting to this, albeit somewhat slowly. The US Government Accountability Office's AI Accountability Framework is organized around four principles: governance, data, performance and monitoring. The Institute of Internal Auditors updated its AI Auditing Framework in 2024 to address both data controls and alignment with ethical safeguards.
Neural networks and large language models (LLMs), however, are difficult to interpret by design. The weights do not come with annotations explaining why a particular output was produced. At the same time, organizations are increasingly expected to provide audit trails for systems whose internal behavior is genuinely opaque.
A helpful distinction can be made here. While model internals might not be interpretable, the data pipeline that fed training examples into that network can be documented. User prompts, system outputs and applied filters and guardrails can all be logged and traced.
For regulatory purposes, that level of traceability can be sufficient.
As a result, internal audit is finding a new role. Deloitte, a global consulting and audit firm, frames it as "the seatbelt for a company that already has the accelerator to the floor with its AI pilot programs." Practical steps include inventorying AI use cases and data flows, comparing governance frameworks against standards such as the NIST AI RMF, and identifying gaps in controls for ethics, bias and security. For agentic AI, where the AI can autonomously take decisions and act on them, controls should include goal alignment and audit trails, along with human-in-the-loop or human-over-the-loop monitoring and overrides.
Another challenge is the growth of what is often described as "shadow AI." Many generative AI tools, such as Gemini, Claude or ChatGPT, operate through browser extensions or personal accounts, influencing workflows, but existing outside the visibility of governance. As a result, data literacy increasingly encompasses not only analytical skills but also a sound understanding of governance and compliance.
This shapes how executives frame modernization: Technical upgrades can be thought of and communicated as gains in control, efficiency and resilience. Reducing data silos, automating workflows and improving decision processes are not IT achievements, but rather reductions in risk and operational improvements that benefit the entire company. Architectures designed with adaptability in mind are better positioned to accommodate evolving policies. That forward-looking framing resonates in both the boardrooms and the regulatory reports.
Conclusion
Trust in data is as much a matter of communication as it is a technical concern. Sound infrastructure is essential, but organizations still need to be able to tell the story of the data in terms that resonate with regulators and stakeholders. Faced with the question, "Where does your data come from?" the executive who can respond with clear, evidence-based answers and technical confidence is in a far better position than one who offers reassurances without evidence.
CIOs and CDOs who manage both dimensions effectively can explain what their investments deliver in concrete business terms. Organizations that do this well are more likely to view transparency not a regulatory burden but as a source of confidence, innovation and differentiation.
Donald Farmer is a data strategist with 30+ years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.
