To manage your data's quality, you need to monitor it. Monitoring is a central aspect with DataCleaner - to establish the starting point and goals, and to ensure a process of following up on data quality issues.
Data profiling and DQ Analysis
Find the patterns, missing values, character sets and other characteristics of your data values. The heart of DataCleaner is a strong data profiling engine for discovering and analyzing the quality of your data.
Data cleansing and enrichment
Use reference data, external and internal, in order to verify that the data values you have correspond to the real world. Check if addresses exist, if phone numbers are internationally filled and more.
Detect and merge duplicates
Duplicates is the most common driver for data quality efforts. Avoid operational issues and bad customer experiences by identifying if you have the same persons, companies and products registered multiple times.
Customer data quality
A poor level of information on your customers is damaging for your business. Customers will easily loose confidence in your treatment of their data, and your internal operations might suffer as well.
DataCleaner's processing engine was built for highly interactive, performing and flexible tasks. Apply it for analytical purposes or for adhoc Extract-Transform-Load (ETL) activities with a lightweight workflow.
Datalytics is a leading business consulting firm in the South American region. We're specialized in Data Integration, Data Cleansing, Business Intelligence and Data Mining solutions. We've been following your products for some time now, since my belief is that it's an excellent software and the perfect match for our offering.
- Andrés Eyherabide, Professional Services Manager, Datalytics.
At Platon we use DataCleaner in our Information Management projects as a handy and powerful "swiss army knife" for activities related to Data Quality Analysis, Data Profiling and exploring customer data in general. We have found that the tool provides us with a lot of important analysis features in this process.
- Asbjørn Leeth, Senior Consultant, Platon.
Using DataCleaner and the EasyDQ Duplicate detection we identified around 10% redundant contact entries. Furthermore addresses were validated and standardized, so the data quality was lifted making it possible for BestBrains to improve our communication and relations with our large network.
- Jesper Thaning, Partner, BestBrains.
- DataCleaner 3.5.10 released by kasper
- DataCleaner embraces GitHub as collaboration platform by kasper
- DataCleaner 3.5.7 released by kasper
- Cosmetic improvements available in DataCleaner 3.5.6 by kasper