Clean Up of Data in D365 CRM
Data Cleansing is always considered as a tedious task. When we have a look at our CRM systems we might find data full of duplicates, test records or blank records.
A well defined approach comes in handy to systematically clean up your data. Below is the approach to clean Account data in dynamics CRM.
We need to follow the below steps for cleansing the data.
Step 1: Segregate the data
Download the accounts data and segregate it in below sheets.
- Test Records
Step 2: Deactivate the test and blank records.
These records are not of any use and so the right thing to do is deactivate this records.
Step 3: Analyze the Duplicates
For the duplicates, we will use the OOB Merge Functionality of CRM.
Understanding Merge Functionality in CRM:
D365 CRM has OOB functionality to merge two records. We can select the records we want to merge and then click on Merge button on the ribbon.
Once we click on merge a pop up will ask us to choose the master record.
The master record is the one that will remain active in the system which the other record will automatically become inactive.
All the child records of this record will be moved to the Master record, also the inactive record will have a notification saying ” Merged with XYZ record”.
We need to analyze our data on the below points, so that we choose the right Master record:
A) Important Fields – For any records, fields like ID, Address, Name might hold significance and a record that has these fields has greater chances of being the Master record.
B) Related Records- If we take an example of Account entity, it has related opportunities, contacts, projects, cases; the account record which has more no of related records can be the master records. With an Advanced Find we can get the count of related records.
Analyzing the data on above points will help us decide the correct master record.
Step 4: Deactivate/ Merge the records
Once analysis is done, we have a better clarity for deciding the records to be merged or deactivated.
Clean and precise data makes working with systems fast and easy. The above systematic approach for data clean up proves a boon when we have large amount of data.