Infographic showing 5 ways data teams are using AI to automate data lineage functions. Including quality control, tagging, anomaly detection, relationship discovery and monitoring and adjusting business performance

Chief Data Officers anticipate that Artificial Intelligence (AI) will be a formidable ally in untangling the complexities of data lineage. However, during a recent peer discussion, data leaders agreed that while third-party solutions promise serious AI-driven capabilities, they are yet to deliver. In the meantime, data teams are forging ahead to develop and deploy their own data AI solutions to enhance data lineage.

In this blog post, we look at five real-world industry applications of AI to enhance data lineage.

Data Leaders members can read the full post and connect with peers via the Data Leaders Hub.

Five Real-Life Applications of AI to Enhance Data Lineage

1. Automated Mapping and Tagging

A leading bank implemented AI to categorise and correlate millions of transactions to individual customer accounts. This automation not only elevated efficiency but also minimised errors, allowing for real-time transaction tracking and a robust framework for data lineage. The AI-driven system dynamically adapts to new transaction types, ensuring continuous improvement in data accuracy and lineage traceability.

2. Anomaly Detection

A healthcare provider deployed machine learning models to monitor patient data flows. The AI’s anomaly detection capabilities have become crucial for identifying data inconsistencies, potential breaches, and operational inefficiencies. By drawing on vast datasets and historical patterns, the AI system provides an additional layer of security and integrity to the patient data lineage, ensuring reliable data provenance and enhancing patient trust.

3. Relationship Discovery

A retailer uses AI to dissect complex datasets, revealing hidden correlations between product lines and purchasing habits. This not only informs inventory management but also shapes targeted marketing campaigns. The resulting insights into data lineage support a more nuanced approach to stock planning and customer relationship management, driving sales and improving customer satisfaction.

4. Quality Control

A manufacturer deployed AI tools to scrutinise each stage of the manufacturing process for compliance with quality benchmarks, immediately flagging anomalies for correction. The continuous audit trail not only supports regulatory compliance but also empowers manufacturers to uphold the highest quality standards.

5. Compliance and Regulation

An energy supplier has harnessed AI to trace customer data through all its operational processes, ensuring adherence to legal standards. The AI framework systematically verifies that each piece of customer data is managed in line with current data protection legislation, reducing the risk of non-compliance. This proactive approach to data lineage via AI mitigates the possibility of costly legal challenges and enhances the company’s reputation for data stewardship.


The role of AI in refining data cataloguing and data lineage is expanding —from providing unprecedented accuracy in automated data mapping to enforcing rigorous compliance in heavily regulated industries. These enhanced capabilities not only have the potential streamline existing processes but also open new avenues for strategic decision-making based on reliable data trails. While data leaders are currently chartering these territories independently, they expect that it only a matter of time before third party solutions deliver greater AI-driven capabilities.

This blog post is taken from an article originally published exclusively for the members of Data Leaders service. Our service gives unique access to trusted, timely, relevant insights from Chief Data and Analytics Officers and their leadership teams from across the world.

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