Data Platform Data Quality Metadata 
IBM Journey to AI blog
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.
IBM Journey to AI blog
APRIL 10, 2023
That is, it should support both sound data governance —such as allowing access only by authorized processes and stakeholders—and provide oversight into the use and trustworthiness of AI through transparency and explainability.
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Marktechpost
JUNE 5, 2024
In addition, organizations that rely on data must prioritize data quality review. Data profiling is a crucial tool. For evaluating data quality. Data profiling gives your company the tools to spot patterns, anticipate consumer actions, and create a solid data governance plan.
IBM Journey to AI blog
JULY 3, 2024
Open is creating a foundation for storing, managing, integrating and accessing data built on open and interoperable capabilities that span hybrid cloud deployments, data storage, data formats, query engines, governance and metadata. Effective data quality management is crucial to mitigating these risks.
Pickl AI
APRIL 14, 2023
Falling into the wrong hands can lead to the illicit use of this data. Hence, adopting a Data Platform that assures complete data security and governance for an organization becomes paramount. In this blog, we are going to discuss more on What are Data platforms & Data Governance.
IBM Journey to AI blog
OCTOBER 14, 2024
Unstructured enables companies to transform their unstructured data into a standardized format, regardless of file type, and enrich it with additional metadata. Text-to-SQL models are getting very good, which will dramatically reduce the barrier to working with data for a broad range of use cases beyond business intelligence.
TheSequence
JULY 5, 2023
Like any large tech company, data is the backbone of the Uber platform. Not surprisingly, data quality and drifting is incredibly important. Many data drift error translates into poor performance of ML models which are not detected until the models have ran.
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