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.
CIO Business Intelligence
JANUARY 24, 2024
“Similar to disaster recovery, business continuity, and information security, data strategy needs to be well thought out and defined to inform the rest, while providing a foundation from which to build a strong business.” Overlooking these data resources is a big mistake. What are the goals for leveraging unstructured data?”
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AWS Big Data
FEBRUARY 29, 2024
Working with large language models (LLMs) for enterprise use cases requires the implementation of quality and privacy considerations to drive responsible AI. However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications.
DataKitchen
FEBRUARY 27, 2024
However, the foundation of their success rests not just on sophisticated algorithms or computational power but on the quality and integrity of the data they are trained on and interact with. The Imperative of Data Quality Validation Testing Data quality validation testing is not just a best practice; it’s imperative.
Ontotext
MARCH 20, 2024
In today’s data-driven world, businesses are drowning in a sea of information. Traditional data integration methods struggle to bridge these gaps, hampered by high costs, data quality concerns, and inconsistencies. It’s a huge productivity loss.”
CIO Business Intelligence
FEBRUARY 16, 2023
The Basel, Switzerland-based company, which operates in more than 100 countries, has petabytes of data, including highly structured customer data, data about treatments and lab requests, operational data, and a massive, growing volume of unstructured data, particularly imaging data.
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.
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