How to Measure Data Quality Effectively: 4 Proven Strategies
Data fuels every decision your business makes. But if that data is incomplete, inconsistent, or unreliable, even the most sophisticated analytics or AI models can lead you in the wrong direction. Measuring data quality effectively is the first step toward ensuring that your data delivers real business value. Many organizations recognize the importance of data […]
Building a Foundation of Trust: How to Improve the Quality of Your Critical Data
Key Takeaways Inaccurate data undermines analytics, drives up costs, and damages customer trust. Implement core processes like validation, enrichment, entity resolution, and reconciliation to reduce risk and improve operational efficiency. For long-term success, build a data quality strategy that combines the right tools with clear goals, ownership, and metrics. Trusted data is the foundation of […]
Natural Language and AI: Driving Faster, More Accessible Data Quality
Operationalizing data quality at scale remains a challenge for many organizations. Rule creation is often manual and time-consuming, testing cycles are delayed by limited access to sample data, and pipeline flexibility is constrained by rigid tooling. These barriers slow down deployment and limit who can participate in data quality efforts. Precisely’s latest enhancements to the […]
Understanding Your Data: The Key to Trust and Business Success
Today’s best decisions—and boldest innovations—depend on trusted data, especially as organizations lean on AI and analytics to guide their next moves. But here’s a fundamental challenge that many organizations face – and one I’ve encountered in countless conversations with customers: they don’t fully understand what data they have, let alone whether they can trust it. […]
What is Data Integrity?
Key Takeaways: Data integrity is achieved when data has maximum accuracy, consistency, and context – giving you the power to trust your data and make better business decisions. The journey to trusting your data can be challenging, but you can more easily and effectively build data integrity when the core capabilities you need work together. […]
Must-Know Data Integrity Trends for 2025
2025 Planning Insights: Data Quality Remains the Top Data Integrity Challenge and Priority
Key Takeaways: Data quality is the top challenge impacting data integrity – cited as such by 64% of organizations. Data trust is impacted by data quality issues, with 67% of organizations saying they don’t completely trust their data used for decision-making. Data quality is the top data integrity priority in 2024, cited by 60% of […]
Data Democratization 101
Key Takeaways: Data democratization is about empowering employees to access and understand the data that informs better business decisions. The rapid advancement of analytical capabilities, capacity, and usability can make more information available to be analyzed. Data stewards must balance security and compliance with the expansion of data availability throughout the organization. The goal of […]
5 Data Quality Best Practices
Key Takeaways By deploying technologies that can learn and improve over time, companies that embrace AI and machine learning can achieve significantly better results from their data quality initiatives. Data enrichment adds context to existing information, enabling business leaders to draw valuable new insights that would otherwise not have been possible. Companies should implement a […]
How to Power Successful AI Projects with Trusted Data
Key Takeaways: Trusted AI requires data integrity. For AI-ready data, focus on comprehensive data integration, data quality and governance, and data enrichment. A structured, business-first approach to AI is essential. Start with clear business use cases and ensure collaboration between business and IT teams for the greatest impact. Building data literacy across your organization empowers […]
AI Success – Powered by Data Governance and Quality
Key Takeaways: Data integrity is essential for AI success and reliability – helping you prevent harmful biases and inaccuracies in AI models. Robust data governance for AI ensures data privacy, compliance, and ethical AI use. Proactive data quality measures are critical, especially in AI applications. Using AI systems to analyze and improve data quality both […]
5 Key Benefits of Data Democratization: How Self-Service Data Can Improve Your Business
Key Takeaways: Data democratization is a process that gives virtually anyone in your organization the ability to understand data for better decision-making. Data democratization has immense benefits, like a 360° customer view, enhanced innovation, and streamlined internal processes. To fully take advantage of these benefits, you also need to keep compliance, security, and potential data […]