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Insurance Organizations Depend on the Quality of Their Data

Precisely

Companies that lack well-defined processes and supporting technology are dependent on internal staff to manage data quality as best they can. Only 26% regard this tactic to be highly effective, whereas more than 40% indicate a strong preference for automated systems and scalable data validation tools.

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Accelerate Digital Transformation with Hyperautomation

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Data Quality and Integrity Improved data quality and integrity are foundational prerequisites for making sound data-driven decisions. Organizations should be careful not to automate business processes before considering which data sets those processes impact. Interested in learning more?

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Unraveling the Threads: Data Fabric vs Data Mesh for Modern Enterprises

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Large data- intensive organizations with multiple data sources, businesses that would benefit from near real-time analytics, industries with stringent compliance or security regulations, or those that can benefit from AI, machine learning, and advanced analytics will also see value. ” today.

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The Human-Centric CDO: 3 Key Takeaways from the Gartner Data & Analytics Summit 2023 in London

Alation

D&A leaders from around the world gathered to discuss and find ways to overcome the latest challenges through strategies and innovations backed by data, analytics, and data science. The observations comprised a mix of classic (the power of people, data quality ), recent (architectures such as fabric and mesh ), and emerging (AI).

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How to Build a Meshy Data Fabric (With a Data Catalog!)

Alation

This white paper makes this information actionable with a methodology, so you can learn how to implement a meshy fabric with your data catalog. For the full story, download the white paper here ! to an external, compliance-focused perspective (“How do we ensure analysts use private data legally?”).

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Why Your Master Data Management Needs Data Governance

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An MDM consolidates important domain data into unique or linked instances (e.g. a “golden” record) and then uses that unique record as a reference point for aggregating associated data, purging duplicates, standardizing data across various applications, and creating rules to continuously resolve, merge, or disassociate records.

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Leveraging Generative AI in Genomics with IBM’s watsonx Platform

IBM Data Science in Practice

Computational Costs : Analyzing vast and complex genomic data requires substantial computational resources, making it expensive and time-consuming. Data Quality : Ensuring the accuracy and reliability of sequencing data is crucial.

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