Enterprises need data for making informed decisions, interacting with customers and vendors, plan marketing campaigns and analyze results. Trusted data helps overcome fraud challenges and enables organizations to comply with regulations. High quality data about key business entities provides the growth funnel for a successful enterprise.
Using Reifier, organizations can quickly fuzzy match and identify duplicates in their data. Approximate string matching in Reifier identifies different mentions of an entity, and removes redundant customer records from customer relationship management(CRM) systems like Salesforce, Microsoft Dynamics CRM, Magento, SAP Business Suite, Oracle Siebel, ExactTarget, PipelinerCRM, Pardot etc. Clean customer records enable efficient sales and marketing and help the organization to grow. Imagine reaching out to the same customer multiple times only because of multiple entries in the system – expensive and time consuming for the sales and support staff, troublesome for the data analyst, cumbersome for the BI developer and frustrating for the customer. Poor data quality hits brand value and hurts customer experience.
Its not just customer data that needs higher data quality and data cleansing. Cleansing product catalogs with redundant listings enables companies to plan inventory, cut operational costs, provide better customer experience and sell more. As Reifier learns from the data, its powerful proprietary AI algorithms can easily clean product catalogs and create a clean inventory.
Fuzzy string matching for record linkage and deduplication matches and consolidates vendor lists so that companies can leverage their data more effectively and drive their bottom line.
Duplicate patient data prevents healthcare companies from improving the quality of patient care and increasing physician acceptance of the new EHR. With collaborative initiatives like ACO, HIE etc, patient data is being shared. Unless we can successfully match and identify patients uniquely, our ability to drive healthcare reform remains limited.
Mergers and Acquisitions come with own data quality issues. Ability to merge source systems easily provides the foundation steps for the merged entities to work in tandem. This needs a comprehensive data matching engine which can handle different entities, deploy and run fast, scale to large data as well as provide accurate results.
Contact us to assess Reifier on your data.Posted on July 21st, 2016