Migrating Data involves in and out to a new platform which requires migration of all the functional data from the legacy system. Most businesses have shifted from traditional data centers to cloud with the promptly changing business needs and the advent of cloud technology. Like all other interactions data migration too involves few hurdles to overcome.So accurate planning is essential to overcome this hurdles.

In this piece, we would discuss:

  • Common Problems Of Data Migration
  • Data Migration Process
  • Best Practices for Efficient Data Migration

Most Common Problems of Data Migration

Sometimes it’s very uncertain and you don’t even realize when you run into data migration problems. However, being aware of the common hurdles that could potentially derail your project will increase the likelihood of achieving a smooth transition of data to your new system. The most common data migration problems that are often not addressed and the factors that far too often lead to failure are as follows:


Poor Knowledge of Source Data

Not being aware of the problems that exist in your data is the reason for the poor knowledge of source data. It can be all too easy to get complacent and assume that your data can easily be configured into the parameters of a new system however the reality could mean critical failures when it comes to user acceptance. So to ensure success, you need a good understanding of the source data.

Underestimating Data Analysis

Constraints in a computer system can be an issue as information can be hidden in obscure places because often there are not specific fields to hold all elements of the data and sometimes the users may also be not aware of the purpose of the available fields. This result is outdated data getting transferred during migration which sometimes is discovered after the project is completed. Due to this, there is not enough time available to identify and correct this data. Performing a thorough data analysis at the earliest stage usually while designing and planning can uncover these hidden errors.

Lack of Integrated Processes

Data migration is Disparate technologies used by a set of people. Using spreadsheets to document data specifications which are not easy to translate while performing data transformations and which are prone to human errors is a classic example. This usually leads to failure in transferring data and problems in the development, testing and implementation stages. Organisations must look to utilise a platform that successfully links the critical inputs and outputs from each of the stages to help reduce error and save time and money.

Inability to Validate a Specification

Critical misses of the early stage of data can have repercussions later in the chain of activities may well have an understanding of your source data, but is not necessary that it would result in a strong specification for migrating and modifying data into a target system. Validating your data transformation specifications early on with actual data, rather than just documented aspirations can increase the confidence in executing the rest of the steps.

Failure to Validate the Implementation

You can hit a brick wall because of lack of test cases even where your knowledge of source data is evident. To avoid the risk of developing problems you need to explore various scenarios before it is too late. Using full volume data from the real world helps to cover a wider range of possibilities while testing your migration.

 Late Evaluation of the Final Results.

The problem of late evaluation usually occurs in the testing stage, where the user only gets to see the actual data once it is loaded into the new system at the end of the development. Due to the incompatibility of the data in the new system takes place. Time, money and the embarrassment of a delayed project can be avoided by introducing early and agile testing phases and getting your users involved in evolving the test cases as they see actual prototypes of the data output.

 Lack of Collaboration

Data migrations along with disparate people also involve internal employees and external contractors in some cases. Some of these people may not even be in the same location. Working in silos can reduce efficiency, create more data silos and sometimes lead to misinterpretations. Collaborative tools enable all parties invested in a migration to see the same picture of data as it moves through the project stages, leaving little room for assumptions and misunderstandings.

 Inappropriate use of Expertise

It makes sense to source experts, and usually, this is applied to the management and technical aspects of a data migration. Introducing data experts into your migration projects right from the beginning will ensure they make sense of the disparate data sources, but also guide the data transformation to suit the audience who will use it on the target system.

A data migration project is challenging and high risk but if each of these hurdles is acknowledged during the planning stage and are overcome early on before data is transformed and transferred, you can be sure of success.

Steps Involved in the Data Migration Process

The process of data migration as we understand is transferring data from one system to another. It requires a lot of time to prepare as well as to finish the whole process with proper migration validation.

That's why complete data migration may take noticeably longer than the time needed to extract, transform, and load data into new databases. Every methodology instruction is given points out to consider the following steps in data migration:

1.      Analysis of business impact

It is better if the people are prepared for the lost of access to data on which they usually work. So it is important to make sure that migration won’t interrupt them much. It's not easy and requires completing a list of processes and operations that have something in common with data to be migrated, and let users know early enough so that they won't be surprised with system's downtime.

2.   Information gathering and identifying required fields

The first step is awareness about the impact migration can have on business users. At the same time gathering the information about software and hardware migration aspects is equally important. During this second stage, it is important to discover as many details as possible about future data migration complexity. There are two different methods for it - manual and automatic.

To avoid any kind of problems in the migration process it is important to list the fields involved in the migration process:

  • Required fields
  • Optional fields
  • System Generated fields
  • Legacy IDs
  • Business rules

3.    Plan and Determine the order of migration

The planning stage involves proper strategy building including all the information like data and its attributes, best practices for migration, tools to be used in the process and also operational constraints to migration. For small migrations, planning may seem like superfluous but is very important. It helps to monitor the process effectively.

In Salesforce, relationships that exist between objects and dependencies dictate the order of migration. For example, all accounts have owners, and opportunities are associated with an account. In this case, the order would be to

  • Load users
  • Load accounts
  • Load opportunities

Relationships are expressed through related lists and lookups in a Salesforce application while IDs create relationships in a database.

4.      Provisioning

According to the layout of the migration chosen the real provisioning may differ. In one-to-one migration layout, provisioning in more of copying the former structure of files, data volumes, and attributes so that the new environment could be ready for receiving the actual streams of data. On the other hand, in case of relay out migrations, it might be difficult to prepare a new environment requiring plenty of steps to accomplish before actually moving data.

You can also create and follow a data migration workbook throughout the scope of migration. This consolidated workbook holds the data mapping for each object involved in the process. The workbook can be personalized based on your own business requirements.

5.     Testing and Pre-data migration considerations:

The consequence of badly prepared migration process can be tragic to the company data, and systems. It is always advisable to run a test migration before the real migration takes place to avoid the risk of losses and wastage of money and time.Test migrations can be a small representative part of the data.

You can follow the below-mentioned pre-data migration steps:

  • Create and set up a user with a system administrator profile for data migration.
  • Complete system configuration.
  • Set up roles.
  • Be sure to store all possible legacy IDs for a record in Salesforce.
  • Confirm that record types and picklist values are defined.
  • Set up every single product/currency combination in the price book if it will be used in Salesforce.
  • Proper mapping needs to be defined.

6.      Validation

Once the migration process is done, it is mandatory to check if all went in an ordered way. Sometimes the migration finishes smoothly but after some time there are some hidden errors spotted deeper so it is essential to identify them in no time so that they don’t interrupt the future work of the database. Necessarily, there are a few points to check. These are the access to data, file permissions, structure of directories, and the work of applications.

Best Practices for an Efficient Migration

These best practices can help you prepare for and execute a successful large-volume Salesforce data migration.

  • Clearly, defining the scope of the project.
  • The process builder must be aware of the source format and target (Salesforce) required data format.
  • Your migration process must have the ability to identify failed and successful records. The common approach is to have an extra column in a source table that stores the target table’s unique ID. That way, if there are fewer failure records after the first iteration, you can re-execute the process, which will only pick failed records that are not yet migrated.
  • Actively refine the scope of the project through targeted profiling and auditing.
  • Minimize the amount of data to be migrated.
  • Profile and audit all source data in the scope before writing mapping specifications.
  • Define a realistic project budget and timeline based on knowledge of data issues.
  • Aim to volume-test all data in the scope as early as possible at the unit level.

To wrap up

Migration is said to be a one-time activity. The migration task gets completed once the data is loaded into the Salesforce org. So it is very important to follow the whole migration process carefully, keeping in mind it's best practises and avoid any kind of mistake that could cascade into having to do the whole process again.

Author is professional technical writer who is providing information about Salesforce Lightning Migration and Salesforce App Development.





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