Four Enterprise Metadata Management Best Practices to Follow

In addition to providing valuable context to data users, Metadata is critical to enabling intelligence and automation in data management. Siloed approaches and manual processes will not scale to meet the demands of today's cloud-first digital enterprise.

Fremont, CA: Companies are accelerating digital transformation for a variety of business reasons, including driving innovation, developing new business and customer engagement models, and improving operational efficiencies through cloud modernization. The need for trusted data to enable agile, informed decision-making is a common thread running through all of these business priorities. And this trusted data must be easily accessible to data consumers throughout the organization. Simultaneously, they must ensure compliance with an increasing number of industry regulations while safeguarding private and sensitive customer data.

Customer loyalty and retention are dependent on the use of customer data that is transparent, responsible, and ethical. So, how do businesses navigate this difficult balancing act? This is where metadata management comes into play.

Here are some examples of how metadata management enables intelligent data management:

In addition to providing valuable context to data users, Metadata is critical to enabling intelligence and automation in data management. Siloed approaches and manual processes will not scale to meet the demands of today's cloud-first digital enterprise. One must take a comprehensive, integrated, and intelligent approach to manage cloud data effectively. The foundational building block one needs to address this challenge is unified metadata management integrated into all cloud data management processes. When combined with the ability to infer metadata beyond what is directly collected and overlaid with AI/ML intelligence, all cloud data management processes become intelligent and dynamic, facilitating agile, data-driven decision-making at scale. Here are some examples to illustrate this:

• Automatic Recognition of Relationships Between Data Sets: Data leaders must use metadata management to automatically detect data relationships across an organization's distributed data silos. This automation can significantly reduce tedious manual efforts to find the right data sets for an analyst looking to recognize data sets that should be joined together for analysis.

• Data Engineering Transformation Recommendations: Data leaders need to analyze metadata from the organization's data pipelines and make design recommendations to data engineers building data pipelines for AI and analytics. This speeds up development, automates repetitive tasks, and allows more types of users to connect and integrate data more quickly.

• Automated Sensitive Data Mapping and Movement Tracking: Data leaders can automate the process of tracking, reporting, and managing the risks associated with sensitive data movement throughout the enterprise. A violation may occur, for instance, if personal data is moved from a source to a target across geographic boundaries or if data onboarded for billing processes is now being proliferated to other departments or locations for marketing processes that may violate privacy. Such metadata-driven automation is critical for efficiently meeting data privacy and security compliance requirements.

• Automatic Data Quality Assessment: Data leaders need to identify relevant data quality rules for data sets across distributed data and execute those quality rules at scale automatically. This is accomplished by utilizing metadata management intelligence to instantaneously associate data sets with business terms, identify the relevant policies and data quality rules for each business term, and then apply the relevant data quality rules effectively across the entire data estate.