
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.
This flow path may have branches, and applications may augment or transform the data along the way.
Businesses need to record data movements through metadata discovery on a regular basis.
Indeed, business people may think it is some concept that is used by IT for their purposes, but they do not know why.
Metadata management is the process of organizing and centralizing metadata from different data sources.
Enterprise metadata management is the term given to the practices and methods of using data to its fullest potential.
The data lineage can be traced, and one can carry out a set of procedures to be able to trust the data at hand.