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Precisely
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What is Data Integrity?
- Data integrity is essential for accurate, consistent, and contextualized data to empower businesses to make fast and informed decisions.
- Trust in data is vital for impactful decision-making, but many organizations still face challenges in fully trusting their data.
- Data integrity encompasses accuracy, consistency, and real-world context of data, enabling better customer management, cost reduction, and risk management.
- It requires data sources to be integrated to provide a comprehensive view for business users and mitigate data quality issues.
- A sound data integrity strategy includes data quality solutions, data observability, and data governance to ensure security, privacy, and regulatory compliance.
- For AI success, trusted data with integrity is essential to avoid biased outputs, untrustworthy results, and loss of trust in AI systems.
- Data integrity goes beyond data quality by focusing on completeness, accuracy, consistency, and contextual relevance of data.
- Initiatives to improve data integrity start with specific projects to address data quality issues, improve governance, and enrich internal data with external datasets.
- Organizations can enhance data integrity by leveraging tools like the Precisely Data Integrity Suite, which integrates core capabilities for accurate and contextualized data.
- The suite offers data integration, observability, governance, quality, geo addressing, spatial analytics, and enrichment to streamline the data integrity journey.
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