Improving data quality to facilitate
strategic decision-making
Ensuring reliable and usable data
Data quality is a strategic lever for any organisation wishing to make informed and effective decisions. Reliable, consistent and up-to-date data enables you to secure your analyses, optimise your processes and strengthen your competitive advantage. At Everience, we place data at the heart of your projects to maximise its impact on your performance.
Our 5 key strengths:
- Recognised expertise: mastery of best practices and quality standards
- Comprehensive approach: from initial audit to the implementation of robust governance
- Advanced technologies: integration of cleaning, enrichment and monitoring tools
- Regulatory compliance: adherence to standards and regulations such as the GDPR
- Continuous improvement: ongoing monitoring to maintain a high level of quality
Our key figures
Our data quality services:
We analyse your data sources and flows to measure their accuracy, consistency and timeliness. This audit identifies anomalies, duplicates and obsolete data in order to build an improvement plan tailored to your business and regulatory challenges.
We implement data cleaning processes and tools to correct errors, fill in missing data and harmonise formats. The aim is to make your data reliable, usable and ready to be integrated into your business applications or analysis tools.
We define quality KPIs (error rate, completeness, timeliness, inter-system consistency) and deploy monitoring solutions to track data quality in real time. This gives you clear management and up-to-date dashboards.
We integrate data quality into your workflows through robust data governance, defining roles, responsibilities and rules to be applied throughout the information lifecycle.
We support your employees in adopting best practices, reducing data entry errors and understanding the impact of data quality on the overall performance of the company.
Using monitoring tools and automated controls, we ensure that your data remains reliable over time, avoiding the costs associated with errors, reprocessing and poor decisions.
