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.
How confident are you in the quality of your data?
The accuracy and reliability of your critical data directly impacts your business decisions, operational efficiency, and customer satisfaction.
And yet, many organizations still struggle with inaccurate data – leading to costly errors, inefficiencies, and missed opportunities.
In fact, 67% of leading data and analytics professionals say they don’t have complete trust in the data their organization relies on for decision-making.
And 64% of organizations cite data quality as the top challenge impacting overall data integrity.
The question is no longer whether data quality matters – it’s how to achieve it.
Poor Data Quality Hurts Business Outcomes
When critical data is inaccurate, incomplete, or inconsistent, it opens your business up to various challenges:
- Operational inefficiencies: Manual data corrections and repetitive processes waste time and resources. Your teams end up spending valuable time fixing errors rather than focusing on more strategic initiatives that drive innovation.
- Increased costs: Poor data quality exposes your business to financial risks and lost revenue – from customer churn to compliance penalties and more.
- Negative customer impact: Erroneous data affects customer interactions – and it only takes one bad experience for a customer to lose confidence and take their business elsewhere. There’s also the risk of brand damage that can’t always be reversed.
Key Components to Improving Data Quality
How do you avoid these common pitfalls and unlock the true potential of your data? You need essential data quality processes. Here’s a snapshot:
- Data validation and enrichment: This involves multiple capabilities that are more powerful together. Data validation ensures that your data is accurate, up to date, and standardized. Then, with data enrichment, you append that data with valuable context that reveals critical relationships among people and places, assets, and opportunities.
Success story: Poste Maroc, a mail and package delivery service in Morocco, streamlined its address validation throughout the country – reducing the rate of undeliverable mail by approximately 90%.
- Entity matching and resolution: These automated processes reduce duplicate records and create a single view of critical data. As a result, you reduce costs, boost efficiency, enhance compliance, and make better data-driven decisions.
Success story: Schwan’s Company, a global frozen food distributor, reduced new customer duplicate data by 25%.
- Data reconciliation: Ensures the consistency, accuracy, and completeness of your data across systems, by comparing two or more datasets to reveal discrepancies. This reduces risk, ensures compliance, and enhances efficiency.
Success story: Swedbank, a leading Nordic-Baltic banking group, reduced regulatory risk in a compliance-heavy environment.
The Benefits of High-Quality Data
Once you’ve established the right data quality foundations, the benefits can be felt across your business – fast. Think about the possibilities that could arise when you achieve results like these:
- Improved data accuracy and consistency
- Trusted analytics and AI results
- Streamlined processes with reduced manual interventions
- Lower operational costs
Ultimately, high-quality data improves your operations, and enables you to build the trust and confidence you need to drive smarter decisions, deliver better customer experiences, and stay ahead of risk.
Next Steps: Build Your Data Quality Strategy
It’s important to note that improving data quality goes beyond technology.
A data quality solution that delivers tools for validation, matching, and reconciliation, is essential, but long-term success requires a clear, targeted strategy to guide your journey.
Precisely Data Strategy Consulting can help you build and execute a data quality strategy that’s tailored to your unique needs and use cases. The team will work with you to define the metrics that matter most, and identify the people, processes, and technologies needed in order to quickly deliver the value you need.
Where are you on your data quality journey? Ready to take the next step? Use our Interactive Data Quality Assessment Guide to evaluate your current data quality, identify gaps, and plan for improvement.
Start your journey to better data quality today!
The post Building a Foundation of Trust: How to Improve the Quality of Your Critical Data appeared first on Precisely.
