APPROACH

                              We used the following approach to enable this:

                              • We imported all the required data elements and cleansed them to a standardized format by getting the right Lat-Long through web extraction, stop word removal, abbreviation replacements etc.
                              • Following this, we used statistical fuzzy matching techniques like Jaccard similarity, Jaro Winker, phonetical match, distance match, etc. to determine records which were similar.
                              • We then applied business rules specific to the client’s needs to ensure that certain records were not falsely flagged as duplicates.

                              KEY BENEFITS

                              • The solution enabled the client to integrate multiple data sources into SFDC after removing duplicates to avoid redundancy of information.
                              • The entire algorithm has also been automated to run without any manual intervention and generate output files every time there is an update on the input files.

                              RESULTS

                              • The client could merge projects and leads information from multiple 3rd party sources into their environment in a seamless manner without any duplication of information.
                              • All relevant information pertaining to an account was accessible in one location when a rep logs into SFDC.

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