A Step Toward Clean And Quality Data
How does bad data affect Salesforce? Duplicate Data. Even though Salesforce has Out of box functionality to address duplicate data, it’s not enough. For example, the Merge process for Accounts is manual. Users have to first identify the duplicate accounts, then search for the account, find the related duplicates and then manually merge them. This is a lengthy painful and manual process.

Salesforce Key Data Objects

  • 25% Account data
  • 30% Contact data
  • 45% Lead

Keeping this in mind, we developed our app focused on data cleanup for these key objects. Our program does this in 4 simple steps:

  1. Identify the object that you want to fix duplicates.
  2. Select the fields that you want to use to identify a unique record.
  3. Runs the process to identify duplicate records.
  4. Merge the duplicate records.

Our program uses several combinations of rules and algorithms to identify potential duplicates and flags the records. Our program also identifies the master record and merging records before merging them.

Our Solution

However, the “one size fits all” approach rarely works. Every business is different. Hence every business should have different rules to keep the data clean. Keeping this in mind, we work with you closely to understand the rules and the overall data strategy.

Key Features

  • Automated process

  • Supports Account, Contact, and Lead objects

  • It can be customized for custom objects

  • It provides flexibility to select the field criteria for duplicate data

  • Identifies potential and fix address issues

  • Seamlessly merges duplicate records

What does the app exactly do?

Let us consider the following example. You have three records of IBM with almost the same address and slight variation in the company name.

  1. IBM Corp 1001 5th Ave New York City NY 10001 US
  2. IBM Corporation 1001 5th Avenue New York City NY 10001-1234 USA
  3. IBM Inc 1001 Fifth Ave NYC NY 10001 United States

Even though technically, the addresses and the company names are entered differently, our program uses different algorithms to conclude that the three records are similar and would identify as a candidate for merge with one as master and the rest as duplicate.

How long does it take?

We are committed to complete the discovery,  data analysis, identifying duplicate records, and merging the data in 7 business days.