Data warehousing refers to the process of collecting, organizing, and storing large volumes of data from multiple sources in a centralized repository called a data warehouse. It is designed to support efficient querying, analysis, and reporting of data for decision-making purposes. Data Science Central brought this interesting topic to us in their article, “Enhancing metadata-driven data warehousing through AI.”
Organizations are constantly seeking ways to unlock the full potential of their data. Data warehousing has emerged as a crucial component in managing and analyzing vast amounts of information.
Data warehousing enables organizations to consolidate data from disparate sources, providing a unified view of information for decision-making, reporting, and analysis purposes. It supports complex queries, trend analysis, data mining, and business intelligence initiatives. By centralizing and structuring data, data warehousing helps improve data quality, accessibility, and consistency, empowering organizations to derive valuable insights and make informed decisions based on a comprehensive view of their data.
Metadata makes digital content findable. Findability, however, works only when a proper taxonomy is in place. Proper indexing against a strong standards-based taxonomy increases the findability of data. Access Innovations is one of a very small number of companies able to help its clients generate ANSI/ISO/W3C-compliant taxonomies.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, changing search to found.
