Data Integrity

What is Data Integrity?

Data integrity refers trusted data with maximum accuracy, consistency, and context that enables organizations to make fast, confident decisions.

Why Data Integrity is Important?

When organizations are ensured of data integrity through maximum accuracy, consistency, and context, they benefit from:

• Improved insights and analytics
• Reliable data for use in artificial intelligence (AI) and machine learning (ML) projects
• Faster and more confident decision-making
• Enhanced business agility

Organizations are hampered in their efforts to achieve data integrity when they are unable to bring the critical capabilities of data integration, data quality, location intelligence, and data enrichment together. One of the biggest challenges to achieving data integrity is integrating siloed data. Organizations need a solution that understands various data sources and can integrate legacy data into next-gen cloud data platforms.

Data quality issues present an even more significant challenge to data integrity. Inaccurate, non-standardized, and incomplete data diminishes business analytics' potential value, even in a best-case scenario. In the worst case, it renders results invalid. A sound data integrity strategy includes data quality solutions capable of standardizing and validating data, identifying gaps or discrepancies, and triggering workflows and processes to correct those errors at scale.

Data must also have context for it to truly have integrity. Location intelligence and data enrichment provide data with the context that is increasingly essential for success. Location intelligence enables organizations to better understand boundaries, movement, and the environment surrounding its customers, vendors, store locations, and other entities. Hyper-accurate location insights allow businesses to organize, geo-enrich, and visualize location data with unmatched performance and scale. 

Data enrichment adds trusted third-party data to internal data, increasing its completeness, context, and value. Organizations must look for 3rd party data related to streets, addresses, boundaries, points of interest, demographics, and more to provide context for decision-making, downstream analytics, and machine learning projects.

Achieving data integrity is a journey. Organizations may choose to adopt data integration, data quality, location intelligence, or data enrichment capabilities for a line-of-business use case. Then they build out their strategy by expanding to tackle new use cases and challenges.

How Precisely can help?

Data integrity is built on four key components: enterprise-wide data integration, data quality, location intelligence, and data enrichment. The Precisely Data Integrity Suite brings these four components together to deliver unmatched value. Built on proven technology that 12,000 global organizations rely on for their data-driven initiatives, the Precisely Data Integrity Suite delivers trusted data that is accurate, consistent, and in context to empower your business to build bigger possibilities with your data.