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EA and BP modeling squeeze risk out of the digital transformation process by helping organizations really understand their businesses as they are today. Your organization won’t be able to take complete advantage of analytics tools to become data-driven unless you establish a foundation for agile and complete data management.
It allows both IT and business users to discover the data available to them and understand what it means in common, standardized terms, and automates common data curation processes, such as name matching, categorization and association, to optimize governance of the data pipeline including preparation processes.
This is where metadata, or the data about data, comes into play. Having a data catalog is the cornerstone of your data governance strategy, but what supports your data catalog? Your metadata management framework provides the underlying structure that makes your data accessible and manageable.
Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextualdata is what enables LLMs to change from general-purpose to domain-specific knowledge. In the generative AI or traditional AI development cycle, data ingestion serves as the entry point.
For this reason, people often struggle to make vital business decisions as they face a complex data landscape. Metadata is the information about data that gives it meaning and context. It helps to answer basic, yet important questions like: “What does this data mean?”, “Which data is used the most?”
An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.
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