- A large IT distribution organization using Salesforce CRM acquires a smaller company using Microsoft Dynamics. The 2 CRMs are consolidated in within days (see explanation below)

- A telecom in North India finds common customers based on Full Name and Date of Birth between 2 datasets of volume 9 Million and 3 Million records respectively. 

A US based internet service provider deduplicates and consolidates 6 datasets of average 3 Million subscribers into a single database. 

Do-It-Yourself Manipulation of Large Datasets

The Dataset Operations feature of DataStitch has numerous applications in preparing and processing datasets. There is no need to define any data structure and the operations can be performed easily with few keystrokes. The type of index on fields is decided by the system based on the field names in the header record of the CSV file.  Once a dataset is created within DataStitch, multiple operations can be performed on that dataset before exporting it out. 

An Example: CRM Data Consolidation

Described here is a case where a company (called FIRST) using Salesforce CRM has acquired another company (called SECOND), who has been a Microsoft Dynamics D365 user. The business problem solved here is to match the two CRM's records into an integrated one. 

Before the details, we will recap the basic functionality of our deduplication feature, built in the system. It will recognize patterns within individual's names, company names, dates, addresses etc and cluster them. In example below, the system knows that S12 and S24 are pointing to the same person (and assigns a unique key to both of them).

The two CRM databases are imported into DataStitch as datasets. Automatically it knows the similar values within the fields. 

Now, using the dataset operations, the two sets of FIRST and SECOND are selected, to arrive at their unified, intersected or subtracted sets. In the results below, the operation was done without deduplication & merge. 

As another illustration, we start by merging the datasets of FIRST and SECOND based on their unique values. Now the cleaned deduped dataset are unified, intersected and subtracted. The results are as follows: 

These examples show the power of dataset operations. The operations can take on single or compound fields within datasets, the size of which could be in millions of records.