Businesses are modernizing their data infrastructures and data governance policies gain the most form of Artificial Intelligence implementation. The increasing amounts of big data that new generation enterprises own make the case of data management even more imperative than ever before. Proper data management collaborating with Data pipelines and data catalogs lets enterprises gain the optimum from the disruptive technologies, artificial intelligence in specific.
Data catalogs are increasingly gaining popularity for all the data stakeholders, especially analysts, developers, and data scientists who are looking for available data assets to gather optimum business intelligence.
- A successful data catalog is built on two basics that sum up automation and collaboration
- Automation ensures that the technical metadata is enhanced with business metadata for business users to gain the optimum from this valuable asset.
- Since everything cannot be improved with automation, the case for collaboration gains power. Arrangement of a user-friendly interface for data specialists to improve some metadata manually, discuss it, and share the information amongst the citizen data scientists.
Several companies have not attained a high level of sophistication with vital data-related factors, posing a threat to the present data practices. Cleansing crude and inaccurate data before feeding it into an AI model is a complicated process. To add to data management difficulty, Data Governance is rapidly gaining importance as a tough problem spot. As a result, it is easy to fall prey to pitfalls as inadvertently using or exposing sensitive information hidden among data without sources. For example, while the patient’s name might record that is used by an AI system; it could be present in the doctor’s notes section of the record. Such data governance and data responsibility conditions must be addressed since it is crucial for the C-suite to be informed as they work to stay in line with privacy rules, such as California Consumer Privacy Act or the European Union’s GDPR and otherwise manage reputation risk.
If this different data management and governance issues are not addressed early on, more problems would occur later to fracture AI initiatives. The organizations must develop a holistic data-based AI strategy that scales with and adapts to their changing needs and demands.
