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Data architecture strategy for data quality

IBM Journey to AI blog

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.

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Unfolding the difference between Data Observability and Data Quality

Pickl AI

In this blog, we are going to unfold the two key aspects of data management that is Data Observability and Data Quality. Data is the lifeblood of the digital age. Today, every organization tries to explore the significant aspects of data and its applications.

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Five benefits of a data catalog

IBM Journey to AI blog

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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Build trust in banking with data lineage

IBM Journey to AI blog

This trust depends on an understanding of the data that inform risk models: where does it come from, where is it being used, and what are the ripple effects of a change? Moreover, banks must stay in compliance with industry regulations like BCBS 239, which focus on improving banks’ risk data aggregation and risk reporting capabilities.

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Why data governance is essential for enterprise AI

IBM Journey to AI blog

If you add in IBM data governance solutions, the top left will look a bit more like this: The data governance solution powered by IBM Knowledge Catalog offers several capabilities to help facilitate advanced data discovery, automated data quality and data protection.

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AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

Align your data strategy to a go-forward architecture, with considerations for existing technology investments, governance and autonomous management built in. Look to AI to help automate tasks such as data onboarding, data classification, organization and tagging.

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12 AI Insight Talks to Help Improve Your Company’s AI Game at ODSC West

ODSC - Open Data Science

Focusing on multiple myeloma (MM) clinical trials, SEETrials showcases the potential of Generative AI to streamline data extraction, enabling timely, precise analysis essential for effective clinical decision-making. Delphina Demo: AI-powered Data Scientist Jeremy Hermann | Co-founder at Delphina | Delphina.Ai