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Data Errors in Financial Services: Addressing the Real Cost of Poor Data Quality

The Data Administration Newsletter

Data quality issues continue to plague financial services organizations, resulting in costly fines, operational inefficiencies, and damage to reputations. Key Examples of Data Quality Failures — […]

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Why Is Data Quality Still So Hard to Achieve?

Dataversity

In fact, it’s been more than three decades of innovation in this market, resulting in the development of thousands of data tools and a global data preparation tools market size that’s set […] The post Why Is Data Quality Still So Hard to Achieve? appeared first on DATAVERSITY.

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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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How to Assess Data Quality Readiness for Modern Data Pipelines

Dataversity

The key to being truly data-driven is having access to accurate, complete, and reliable data. In fact, Gartner recently found that organizations believe […] The post How to Assess Data Quality Readiness for Modern Data Pipelines appeared first on DATAVERSITY.

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Alation + Soda: Dynamic Data Quality with the Data Catalog

Alation

Alation and Soda are excited to announce a new partnership, which will bring powerful data-quality capabilities into the data catalog. Soda’s data observability platform empowers data teams to discover and collaboratively resolve data issues quickly. Does the quality of this dataset meet user expectations?

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Enhancing Data Fabric with SQL Asset Type in IBM Knowledge Catalog

IBM Data Science in Practice

In this blog, we explore how the introduction of SQL Asset Type enhances the metadata enrichment process within the IBM Knowledge Catalog , enhancing data governance and consumption. This approach could be time-consuming and resource-intensive, with a significant portion of the analysis focusing on irrelevant data.

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Supercharge your data strategy: Integrate and innovate today leveraging data integration

IBM Journey to AI blog

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges, specifically as the growth of data spans multiple formats: structured, semistructured and unstructured.