Data lineage: Map the data journey

In an era where data powers every strategic decision and AI initiative, data lineage has evolved from a technical necessity to a critical business capability. A data lineage solution maps and illuminates the complex pathways through which data flows, transforms and creates value across your enterprise.

Within a unified governance framework, data lineage creates the intelligence fabric that connects your entire data ecosystem. Every data transformation becomes a signal that enriches your understanding of data health and utilization patterns. Real-time visibility into data relationships enables confident decision-making at scale, while automated impact analysis accelerates the pace of innovation.

As data flows through your organization, lineage transforms raw movement into actionable insights that drive strategic value.

Why is data lineage important?

Data doesn’t exist in isolation; it flows, transforms, gets consumed and influences decisions. Knowing its journey is akin to understanding its story.

Trust and accuracy: Just as you’d trust a story more if you knew its source, tracing data’s lineage boosts confidence in its accuracy
Data quality: If data seems incorrect or inconsistent, lineage helps pinpoint the stage where the discrepancy occurred
Compliance and audits: Regulations often require businesses to explain their data sources and transformations. Data lineage offers a clear map for compliance teams and auditors

Key features to look for

Automated lineage discovery: Manual tracking can be tedious and prone to errors.Automation ensures a thorough and efficient tracing of data flow
Visualization capabilities: A graphical representation of data flow is more intuitive and actionable than textual logs. Visualizations help stakeholders understand and navigate the data’s journey
Integration with a wide variety of data sources: Data can reside in databases, cloud storage, third-party applications, and more. A good lineage solution should seamlessly integrate with diverse data sources
Support for technical and business lineage: While technical lineage focuses on the nitty-gritty of data flow, transformations, and systems, business lineage provides a higher-level view, mapping data’s impact on business processes and decisions
Ability to handle large volumes of data: Modern enterprises deal with vast amounts of data. The solution should scale without compromising performance

Use cases by use case:

Regulatory compliance: Demonstrate compliance — for example, to GDPR — by mapping the journey of personal data through various systems and processes
Data migration: Assess the potential impact of planned data changes to business units across the organization, minimizing disruptions
Data lineage: Track and document the lineage of input, training and output data of AI models, ensuring transparency and accountability

Use cases by industry:

Insurance: Tracking underwriting data history, claim records, and customer interactions, ensuring regulatory compliance and assisting in decision-making
Telecom: Troubleshooting network issues, optimizing resources and managing vast amounts of data from call records, network traffic and customer information
Government: Operationalizing a transparent trail of accountability, crucial for policy analysis and maintaining public trust Data governance: Ensure collaboration and compliance