How to identify

inaccurate data?

Our vision

Your challenge

Our solution


Our vision


We believe that the realisation of correct master data need not be too complex and time consuming and that every organisation is entitled to improve data quality using intuitive tools that facilitate transparent and immediate insights.

Your challenge


Ensuring correct master data can be challenging and therefore the risk of poor information quality increases with growing complexity of IT landscapes, high amounts of data generated and lack of support options for data quality monitoring.

Our solution


Data Quality Insights monitor the data quality in your systems and provides input for improvements and cleansing activities. With a rigorous focus on exception reporting, data profiling and cleansing, data quality can be improved in an easy and efficient way.

Analyse data quality

Analyse data quality

Analyse the completeness, validity, consistency and accuracy of master data
using the business rule set, selected for you based on KPMG experience.
Analyse the completeness, validity, consistency and accuracy of master data
using the business rule set, selected for you based on KPMG experience.

Manage exceptions

Manage exceptions

Manage data quality through automatically created exception reports,
address these exceptions and follow up on them through data cleansing.
Manage data quality through automatically created exception reports,
address these exceptions and follow up on them through data cleansing.

Monitor data migration

Monitor data migration

If used during data migration, monitor whether data migration
is done correctly and follow up on data migration issues, if any.
If used during data migration, monitor whether data migration is
done correctly and follow up on data migration issues, if any.

Profile data

Profile data

Use data profiler as a supporting tool for business rule creation.
Use data profiler as a supporting tool for business rule creation.

Data Quality Insights into insurance


From 1 January 2016, one of our clients, a Dutch insurer, is required to report based on the Solvency II framework. However, the client was struggling with incomplete contracts and claims and was therefore not compliant with Solvency II regulations. This is where the client used SOFY Data Quality Insights to detect exceptions within its data by leveraging a real-time automated monitoring solution. This enabled the client to quickly identify incomplete, incorrect, duplicate and inaccurate data concerning contracts and claims and improve data quality. Using SOFY, the insurer is now compliant with Solvency II and able to stay compliant.