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Insurance Organizations Depend on the Quality of Their Data

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

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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!)

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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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Following the Roadmap to Better Data Governance

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Users also need to be able to trust that data, from tracking lineage, use, and potential transformations, to confidence in the fundamental reliability and accuracy of the data itself. McKinsey research found that poor data quality and availability can cause employees to spend a significant amount of time on non-value-added tasks.

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Automate SAP® Processes for Agility, Resiliency, and Success

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Speed to keep up with an accelerating business environment and gain or maintain a competitive edge Improved data quality and integrity – particularly for SAP master data. When you set out to improve data quality and integrity, it’s critical to keep in mind the interdependence of process and data.