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Enterprises need data for making informed decisions, interacting with customers and vendors, 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.

Clean and duplicate free 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. This is 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.

It's not just customer data that needs higher data quality and data cleansing. Cleansing product catalogs with redundant listings enable companies to plan inventory, cut operational costs, provide a better customer experience, and sell more.

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 their own data quality issues. The ability to merge source systems easily provides the foundation steps for the merged entities to work in tandem.

With data matching, organizations can quickly fuzzy match and identify duplicates in their data. Approximate string matching identifies different mentions of a customer, and removes them from customer relationship management(CRM) systems like Salesforce, Microsoft Dynamics CRM, Magento, SAP Business Suite, Oracle Siebel, ExactTarget, PipelinerCRM, Pardot etc.

Powerful data matching 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.

All this needs a comprehensive data matching tool that can cater to your requirements and run on your data easily.

Some of the features you should look for in a data matching tool are

- ability to handle different types of entities like customer, organization, addresses etc

- fast deployment and runtime 

- scalability to large data sizes

- accuracy of results

- support for different languages like English, Chinese etc

One such tool is Zingg, open source data matching which has the above features. You can also build your custom data matching solution using fuzzywuzzy or other string matching libraries. 

Hope this post was helpful to you in understanding why you may need data matching 

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Tags: data, dataquality, deduplication, entity, entity matching, resolution

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