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The expansion of big data applications has created opportunities across economic sectors. In healthcare, however, the potential of big data applications goes far beyond the financial. The contextualdata gleaned from big data can drive healthcare solutions and accessibility to new heights.
Emission factor mapping and other capabilities As part of Oracle Fusion Cloud Sustainability, enterprises would get access to features such as automated transaction records, contextualizeddata, pre-built dashboards, emission factor mapping, and audit capabilities.
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.
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. You need automated data mapping and cataloging through the integration lifecycle process, inclusive of data at rest and data in motion.
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.
Data consumers need detailed descriptions of the business context of a data asset and documentation about its recommended use cases to quickly identify the relevant data for their intended use case. Data consumers have more contextualizeddata at their fingertips to inform their analysis.
Knowledge graph technology can walk us out of the lack of context (which is basically absence of proper interlinking) and towards enriching digital representation of collection with semantic data and further interlinking it into a meaningful constellation of items.
Our vision for the data lake is that we want to be able to connect every group, from our genetic center through manufacturing through clinical safety and early research. That’s hard to do when you have 30 years of data.”
It also adds flexibility in accommodating new kinds of data, including metadata about existing data points that lets users infer new relationships and other facts about the data in the graph. Schemas are an example of how the right metadata can add value to the data it describes.
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?”
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.
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.
It’s a truism that data is the most important asset in the 21 st century economy. But, today too many enterprises exist in a data fog, with poorly contextualizeddata scattered across millions of tables. Dispelling this data fog is one of the key challenges for the next generation enterprise.
Our Knowledge Hub Fundamentals article What is a Knowledge Graph describes how knowledge graphs are more than just simple data graphs because they include a knowledge model that adds three things: formal semantics, descriptions that contribute to each other, and diverse data that is connected and described by semantic metadata.
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