Organizations that effectively implement data lineage as a strategic capability can outperform competitors by reducing model drift, lowering compliance costs, and fostering AI innovation.

AI adoption has surged, with 78% of organizations utilizing AI in 2024, emphasizing the growing importance of trustworthy data for successful AI deployment.

Concerns about AI hallucinations, which often stem from untracked data inputs, highlight the need for robust data lineage to ensure data integrity.

Data lineage is increasingly important for regulatory compliance, bias detection, and building consumer trust by providing transparency in data usage.

To manage data complexity, some organizations treat datasets as products with clear ownership and quality standards, adopting a data product approach.

Data lineage is often compared to the 'nervous system' of an AI enterprise, ensuring reliable data flow and supporting sound decision-making.

Successful supply chains focus on cleaning, structuring, and enriching data before deploying AI, creating a unified, accurate data foundation that is maintained continuously.

The case of Zillow’s nearly $500 million loss due to flawed data inputs illustrates the critical importance of effective data lineage.

AI implementation in supply chains often fails not because of the technology itself, but due to poor data quality, which underscores the importance of establishing a reliable data foundation.

Building a trustworthy data foundation is more crucial than having advanced AI models, as accurate data enables smarter decisions and future-proofing of supply chains.

Data lineage plays a vital role in AI success, helping to ensure data quality, transparency, and effective decision-making.

Automated and scalable data lineage, integrated with broader data management systems, is essential for improving data visibility and accessibility.

Data lineage provides critical operational intelligence by tracking data origins and transformations, which is essential for trust, explainability, and ROI in AI.

Leading organizations prioritize trust and data integrity as prerequisites for AI readiness, recognizing that confidence in data is fundamental to deriving value from AI.

Supply chain data is often fragmented, inconsistent, and outdated, with issues like duplicated records and missing updates, which can mislead AI systems.

AI systems tend to follow inputs without questioning their accuracy, so flawed data can lead to inaccurate forecasts and poor decision-making, often exacerbating risks.