DataMatch Enterprise Documents: Features

DataMatch Enterprise Features

1. 1. Quick Profile: Highlight potential data quality issues like non-printable characters, missing data, mixed numbers, and letters, etc.​
   2. Instant Data Preview: See how your data changes with your data cleansing methods in real-time and create the best configuration.​
   3. Email Address Cleaning: Advanced email address cleaning feature finds errors and automatically suggests corrections.
   4. Data Survivorship: Define a single ‘master’ record by choosing which duplicate survives and the field you want to merge on while capturing additional data in a separate field.
   5. Data Deduplication: Merge the most complete information across duplicates, overwrite data from a master to other duplicates, and delete duplicates from your data sets.
   6. Data Integration: Integrate virtually any data source across the enterprise (databases, CRMs, social media, file formats, email, Big Data repositories, etc.).
   7. Workflow Orchestration: Schedule your projects to run once, on a recurring basis, on a defined schedule, or when an imported file is updated.
   8. Number Cleansing: Automatically cleans telephone numbers to improve matching capability. Useful when matching international numbers.
   9. In-Memory Processing: Process millions of records with our highly scalable, in-memory architecture and export only when you are satisfied.
   10. Cross-Column Matching: Match data across columns – useful when data entry errors put data in the wrong column.
   11. Reusable Workflows: Test and configure your data cleansing workflows and save them as reusable DataMatch projects.
   12. Advanced Filtering: Manipulate your data with advanced filtering functions like wildcards, and/or, or/not statements, etc.
   13. Name Standardization: Includes over 60,000 common names for standardization when matching. i.e. Danny becomes DANIEL, etc.
   14. Bulk Standardization: Identify and count unique words and values in your lists and so you can replace, delete, or extract values into new fields.
   15. Match Scoring: Set a match threshold (0-100%) and view your duplicates – match on multiple fields & see the match percentage on each field.
   16. Pattern Matching: Use Regex wizard to quickly identify patterns and extract into new fields. Example: Text “3 x 4 x 6” can be extracted into: Length = 3, Width = 4, and Height = 6.
   17. Standardization Library: Built-in libraries for nicknames (Jon=Jonathan), postal codes, street suffixes, state and city, and a best-in-class library creator for your custom needs.