The best way to learn more about how our solutions can help is to let us know more about your data challenge.
DQ for Dynamics™ Consolidate is designed to help you tackle duplicate records when the out-of-the-box duplicate detection and merge tools just aren’t enough. Run match sessions to find duplicate records using fuzzy matching logic and a library of data transformations, then present groups of duplicates back to users for duplicate review. Quickly select your best record and seamlessly create a single customer view within your Dynamics CRM instance.
Discover more duplicate records by utilising our vast library of data transformations to standardise data. Further increase the number of results by using fuzzy matching logic, proven over the past 20+ years.
Work through an easy 5 step process to surface duplicate records within your Dynamics 365. Return groups of duplicates to a results screen which in turn gives you the ability to process your duplicate results and decrease your review time by up to 75%.
Gain a complete view of your duplicate records on our multi record review screen. Select your best record from a set of duplicates and then select the best field value to create your golden record and single version of the truth, saving your hours/days of review time.
Automate the process and humanise the exceptions. Build automation rules to automate the processing of duplicate CRM records. First, select your best record by defining master record rules, and then define best field rules prior to accepting and merging duplicate records within Dynamics 365. Then, schedule sessions to run and constantly monitor your data, so you don’t have to anymore.
Remove duplicates from your sales process by cross comparing leads to contacts or account. Decrease the change of reputation damage and ensure you have a single record view within your business applications.
Deploy DQ for Dynamics™ online, on-premise or on-premise internet facing. Wherever your data resides, be sure that you can have the necessary tools to overcome poor data quality.