
Data quality degradation can have severe consequences. Decision-makers may rely on inaccurate information, leading to poor strategic choices. Customer experiences can be adversely affected, and regulatory compliance may be compromised. With the increasing importance of AI and machine learning in various industries, the need for high-quality data is more critical than ever.
Providing a clear data lineage is crucial. This feature helps users track data from its source to its destination, enabling them to identify exactly where and how data quality degradation occurs.
The visualization of any relationship in the data is sometimes branded as “data lineage.”
In the dynamic landscape of data-driven decision-making, maintaining high-quality data is the key to success or failure.
This flow path may have branches, and applications may augment or transform the data along the way.
Global IDs, was founded by a scientist, and now employs scientists and engineers with extensive industry as well as academic experience.
At Global IDs, we believe that the foundation for gainful analytics and compliance is suitable data quality standards.
Global IDs has learned that to infer semantics, it is necessary to analyze actual data content — the data values in columns.