Suffering from poor data quality? Looking to achieve more accurate, consistent, complete, unique, timely, and valid data? Implement a comprehensive data quality solution that responds to your specific business needs, and that’s robust, easy to use, flexible, powerful, scalable, fast, and fits your existing IT infrastructure. Improve your data quality to leverage the full potential of your data, avoid risky decisions, and save money.
Clean, correct, validate, transform, and standardize your data. Leverage an extensive set of predefined algorithms, or create your own. Harness available information from various data sources to make your data more complete and consistent. Best of all, do it all locally or in the cloud.
Identify duplicate records representing the same entity (customers, patients, products, locations, or others) with deterministic or fuzzy matching rules, and create groups of records that represent (with the highest possible probability) one real entity. Use phonetic and other sophisticated algorithms to uncover hidden relationships in your data.
Prevent bad data from entering your systems with a powerful data quality firewall. Extend current cleansing and matching processes with a component preventing users from creating new data quality issues in your systems. Be confident that any and all edited or newly entered data will always be accurate.
Keep information about your current data quality state readily available for faster, improved decision making. Set up the customizable dashboard using predefined and custom metrics. Receive customized alerts and instant notifications of critical situations. Track problems to their source by drilling down to original records, and quickly understand the cause of invalid data. See trends, view history, and get accurate and timely visual reports shared across your entire company.
Identify, monitor, and resolve data quality issues that cannot be resolved automatically. Detect and correct errors, perform manual cleansing, eliminate duplicates, and manually match and merge where automated processes are insufficient or prone to risk. Source systems are updated as errors are corrected, resolving the original faulty data.