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Understanding Data Governance

IBM Journey to AI blog

No one is going to trust you if they can’t trust your data. As you can see, data governance is no joke. Okay, maybe “fun” is a stretch, but there are definitely ways to make data governance less of a chore. Ensure data quality Regularly check your data for accuracy and completeness.

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Unlocking the power of data governance by understanding key challenges

IBM Journey to AI blog

While operational data runs day-to-day business operations, gaining insights and leveraging data across business processes and workflows presents a well-known set of data governance challenges that technology alone cannot solve. Silos exist naturally when data is managed by multiple operational systems.

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Five Reasons Automation Is Key to Data Governance

Precisely

Data governance teams must establish common data definitions and propose meaningful governance metrics. By visualizing how data flows through an organization’s systems and how it impacts processes, users also gain confidence that there is oversight and transparency with any format, function, and integrity level changes.

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In Uncertain Times, Data Integrity is More Important Than Ever

Precisely

Business needs and challenges 77% of respondents say data-driven decision-making is the top goal of their data programs – and they’re also looking to accelerate those processes. Those who have already made progress toward that end have used advanced analytics tools that work outside of their application-based data silos.

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Data Integrity Trends for 2023

Precisely

When organizations neglect data enrichment and location intelligence, for example, they miss out on the perspectives deep contextual information can provide. These factors have expanded the definition of data integrity to include data that is accurate, consistent, and has context.

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Why Your Data Governance Strategy is Failing

Alation

For example, it may be helpful to track specific daily activities or benchmarks for all data-related processes. Numerous committees spend hours deliberating over every word in a Glossary definition, then 6 months down the line leaders complain there hasn’t been enough value shown. Roadblock #2: Data problems and inconsistencies.

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Data Fabric & Data Mesh: Two Approaches, One Data-Driven Destiny

Heartbeat

Data should be designed to be easily accessed, discovered, and consumed by other teams or users without requiring significant support or intervention from the team that created it. Data should be created using standardized data models, definitions, and quality requirements. How does it?