Data Quality & Deduplication Software
What is Data Quality Software?
Data Quality Software is the technology which helps any business improve the quality of their data.
Data is the fuel all organisations rely on for information and decision making. All businesses want to improve their data quality, which leads to better informed decisions, avoids mistakes and adds more value to the business. The ultimate goal is to provide duplicate free data and a single customer view that can be trusted.
Data quality software helps businesses create a virtuous data quality cycle by:
- Understanding what its levels of data quality are
- Identifying what is right or wrong
- Correcting data and process defects
- Removing duplicates
- Monitoring the state of data over time for feedback and continuous improvement
Our Data Quality and Deduplication Software Solutions
Our Data Cleansing Solutions encapsulate over 20 years of experience in delivering data quality projects for organisations worldwide.
We are continually updating our Data Quality Software to keep up with modern technology. Our software integrates with most databases and CRM Systems such as MS Dynamics CRM, Infor CRM and Salesforce etc.
To find out more about our extensive range of data quality & deduplication software – click the logos on the right or Contact Us for more information.
Data Quality problems we have seen…
Inaccurate, incomplete, duplicate or inconsistent data leads to unnecessary waste. Users become frustrated when applications contain bad data and very poor decisions are made when not based on fact.
Bad data to a business is like miss-fuelling an engine, or toxic food to a human. When data is sub-standard the business suffers, here are some of the problems we have seen.
- Consistency suffers as data is not consistent within or across systems
- Uniqueness violations as duplicates are introduced, so no Single Customer View (SCV) or Single Version of the truth
- Timeliness failures because data quality is not delivered at the right time in the right place to support the business need
- Validity rules not adhered to as there is no data governance or automated checking
- Accuracy is not trusted leading to double checking and unnecessary scrap and re-work
- Completeness is lacking so important data required to support the business is missing