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How AI Can Make Metadata More Meaningful for Data Management
By Dick Weisinger
Metadata is data about data. It provides context and meaning to the data, such as its source, type, owner, and relationships. It’s essential for data management because it helps users find, understand, and use data effectively. However, creating and maintaining metadata can be a tedious and error-prone task, especially when dealing with large and complex data sets.
This is where artificial intelligence (AI) can help. AI can automate and improve the processes of metadata creation, extraction, classification, cataloging, quality, security, and integration. AI can analyze the data and generate insightful metadata that adds value and relevance to the data. AI can also help users to discover and access the data they need, by using natural language processing and machine learning to understand their queries and preferences.
Some examples of how AI can improve metadata are:
- Classification: AI can extract and structure metadata from various sources and formats, such as documents, images, audio, and video. AI can also identify and label the content and context of the data, such as topics, entities, sentiments, and emotions.
- Cataloging: AI can automate the search and discovery of data and metadata across different repositories and systems. AI can also capture and describe the lineage and provenance of the data, such as its origin, creation, modification, and location.
- Quality: AI can reduce errors and inconsistencies in the data and metadata, by detecting and correcting anomalies, duplicates, and outliers. AI can also monitor and measure the quality and accuracy of the data and metadata over time.
- Security: AI can protect the data and metadata from unauthorized access and misuse, by enforcing policies, rules, and regulations. AI can also identify and mitigate the risks and threats associated with the data and metadata, such as privacy, compliance, and ethics.
- Integration: AI can help to build and maintain a unified and consistent view of the data and metadata, by merging and reconciling data from different sources and systems. AI can also enrich and enhance the data and metadata, by adding new attributes and relationships.
AI can make metadata more meaningful for data management, by increasing its efficiency, effectiveness, and value. It can also enable new and innovative ways of using and leveraging the data and metadata, by providing insights, recommendations, and predictions, and AI can transform metadata from a passive and descriptive element to an active and prescriptive one.
AI is already being used by many organizations and platforms to improve their metadata management, such as Box, Google Cloud, and Qlik. However, AI is not a magic solution that can solve all the data and metadata challenges. AI still requires human input and oversight, to ensure the quality, validity, and reliability of the data and metadata, and it also needs to be transparent and explainable, to ensure the trust, accountability, and responsibility of the data and metadata.
AI is a powerful and promising tool that can make metadata more meaningful for data management. However, AI is not a substitute for good data management practices, such as defining and implementing clear goals, roles, and processes for data and metadata. Rather, AI is a complement and an enabler for data management, that can help users to achieve better outcomes and experiences with their data and metadata. AI is not the end, but the means to an end.