Thought Leadership
How confident are you in the quality of your data? The accuracy and reliability of critical data directly impacts business decisions, operational efficiency, and customer satisfaction.
And yet, many companies still struggle with inaccurate data, leading to costly mistakes, inefficiencies, and missed opportunities. In fact, 67% of leading data and analytics experts say they do not have full confidence in the data their organization relies on for decision-making. And 64% of companies cite data quality as the biggest challenge affecting overall data integrity.
The question is no longer whether data quality matters, but how to achieve it.
Poor data quality undermines business outcomes
When critical data is inaccurate, incomplete, or inconsistent, organizations face a range of serious challenges. Operational inefficiencies arise when manual data corrections and repetitive processes waste time and resources, leaving teams to spend valuable hours fixing errors instead of focusing on strategic initiatives that drive innovation. Poor data quality also exposes organizations to financial risks and revenue losses, from customer churn to compliance penalties. And inaccurate data disrupts customer interactions: a single bad experience can be enough for a customer to lose trust and turn to a competitor, with the added risk of brand damage that cannot always be undone.
Key components for improving data quality
How can organizations avoid these common pitfalls and unlock the true potential of their data? The answer lies in establishing fundamental data quality assurance processes.
Data validation and enrichment encompasses multiple functions that become even more powerful in combination. Validation ensures that data is correct, current, and standardized, while enrichment adds valuable contextual information, revealing important relationships between people and places, assets and opportunities. Poste Maroc, a postal and parcel delivery service in Morocco, optimized its address validation across the country and reduced the rate of undeliverable shipments by approximately 90%.
Entity matching and resolution uses automated processes to reduce duplicate records and create a unified view of critical data, helping organizations lower costs, increase efficiency, improve compliance, and make better decisions. Schwan’s Company, a global frozen food distributor, reduced duplicate customer records among new customers by 25%.
Data reconciliation ensures the consistency, accuracy, and completeness of data across different systems by comparing two or more datasets to identify discrepancies. This reduces risk, supports regulatory compliance, and improves efficiency. Swedbank, a Nordic-Baltic banking group, successfully reduced regulatory risk in a compliance-driven environment.
The benefits of high-quality data
Once organizations have established the right data quality foundations, the benefits will quickly become apparent across the entire enterprise: improved data accuracy and consistency, reliable analytics and AI outcomes, streamlined processes with less manual intervention, and lower operational costs. Ultimately, high-quality data enables organizations to build the trust they need to make smarter decisions, deliver better customer experiences, and proactively mitigate risks.
Next steps: Developing a data quality strategy
Improving data quality goes beyond technology. A solution that provides tools for validation, matching, and reconciliation is essential, but long-term success requires a clear, purposeful strategy that guides organizations along the way.
By Ralf Krämer, Data Integrity Experte, Precisely
