Data Quality Metadata Optimization Unstructured Data 
IBM Big Data Hub
JANUARY 5, 2023
Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.
Ontotext
JANUARY 29, 2024
As companies in almost every market segment attempt to continuously enhance and modernize data management practices to drive greater business outcomes, organizations will be watching numerous trends emerge this year. Sometimes, the challenge is that the data itself often raises more questions than it answers.
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IBM Big Data Hub
DECEMBER 16, 2022
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.
Ontotext
DECEMBER 1, 2023
Atanas Kiryakov presenting at KGF 2023 about Where Shall and Enterprise Start their Knowledge Graph Journey Only data integration through semantic metadata can drive business efficiency as “it’s the glue that turns knowledge graphs into hubs of metadata and content”.
Ontotext
JANUARY 6, 2021
That means removing errors, filling in missing information and harmonizing the various data sources so that there is consistency. Once that is done, data can be transformed and enriched with metadata to facilitate analysis. You can apply graph optimizations or operations such as traversals and transformations.
Alation
JUNE 2, 2022
A data catalog is a central hub for XAI and understanding data and related models. While “operational exhaust” arrived primarily as structured data, today’s corpus of data can include so-called unstructured data. Other Technologies. Recently, Judea Pearl said, “All ML is just curve fitting.” Conclusion.
CIO Business Intelligence
MARCH 9, 2022
Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio. They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics.
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