Identify document data for migration and integration

To understand data migration and data integration, let us first understand the process of data migration and integration:

Data migration: It is a process to move data from one system to another. Usually, it is a one-time activity or not so often. The example can be a migration project from legacy Dynamics 365 (on-premise) to Dynamics 365 Cloud.

Data integration: It is the process of synchronising data between the two systems. Usually, it is more frequent or a continuous process; it can be one way or two-way integration. The example can be the integration of Dynamics 365 sales with Netsuite or Dynamics 365 finance.

Tools

1. Import Wizard: It can be used to transfer a small amount of data or one-time activity. This can be considered as the fastest way to migrate data to Dynamics 365. They support both Insert and Update operations but cannot be used for integration. The tool is asynchronous in functionality. The supported file formats are.XML,.CSV,.XLS. This tool is part of Dynamics 365.
2. Scribe: It is a third-party tool and has some pricing. It is handy to migrate complex data using Flows. This tool can be used for both data migration and integration between Dynamics 365 or third application. It uses Flows and Connectors with Block objects.
3. SSIS: SSIS stands for SQL Server Integration Services, which is a component of Microsoft SQL Server. It can be useful to migrate a large amount of data. SSIS requires C# skills with good knowledge of SQL.
4. Power Automate: Power Automate is used to build flows with no- code. It can be used to integrate Microsoft Dynamics 365 with other apps and services. Some example of Power Automate uses are approving requests, synchronise files, event-based actions and notifications.

Data management and phases must be documented

1. Data format: The format of the data source is very crucial. When data received is not in format supported by Dynamics 365, steps are to analyse, clean, transform data in the supported format. The clean data would reduce the complication of migration as well as integration.
2. Data mapping: It is a crucial activity for data migration and integration. The mapping between existing entities in source and destination should be handled carefully. It is possible to map fields with a different name but same data type using data mapping. If the data source has information stored in more than one entity, the customer wants to store the same information in one destination; mapping can help.
3. Trigger: The trigger to start transferring data from one system to another system should be documented well. The trigger example can be once in a day at a specific time or triggering manually by a team member.
4. Test and deploy: The data is migrated to test environment first and transferred to production after the testing. The common strategies to test data are:
   1. check whether all the schema changes in the new system are up to date
   2. test the data migrated with the new application
   3. data integrity
   4. data redundancy check to make sure no duplicate data is created
   5. check mismatch of data between two systems