7 critical steps for a successful data migration project

7 essential steps for a data migration project

There isn’t a cookie-cutter solution to a data migration project. While each approach will have similar steps, it will also require customisations because every data set and business context differs. The objective is to create an efficient plan that prevents common challenges from occurring.

Before you do anything, you need to identify the target system and how well it can meet your business needs from a functional perspective. It helps to ask questions, like does this new platform meet the needs of our growing business?  Does it marry up with existing processes? What’s the degree of customisability to fit existing processes, vs process re-engineering? And then there’s the cost. You must weigh up the system features with your organisation’s finances to make sure that your investment is going to be economically viable. 

1. Define a data migration strategy

Now that you’ve picked your target system, you must plan your data migration strategy. This step is absolutely critical in getting business stakeholder buy-in so that the strategy is something that all teams can confidently reinforce. The data strategy should clearly document everything from outlining the scope of work, the processes involved, timelines and budgets to adhere to, as well as who is responsible for what tasks. As migrations can often span multiple months, even years, it’s best practice to have a  staged approach across multiple systems.  This document will become the baseline reference to revisit time and again during the migration project.  You will find yourself inevitably in this position as the complexity unfolds amid changing requirements and numerous stakeholders with different agendas.

2. Conduct a data assessment

As with any data project, a data migration can’t really begin without assessing your current systems and data landscape. That means identifying and locating all the data that needs to be migrated, and noting its current format, as well as the sensitivity of the particular dataset.

Usually, some data will be managed differently after the migration so it’s critical to holistically understand where the data is coming from, where it is going, who is responsible for it as stipulated by your data strategy and what must be transformed for the migration to happen effectively. This is because it can be quite difficult to make changes to the data once the migration is already in action. As some data might have greater security restrictions, the initial assessment of the data pre-migration must take this into account to ensure that the transfer process is appropriately secured throughout the migration.

3. Data mapping

This is the process of establishing a relationship or correspondence between data attributes in the source system and the target system. It involves defining how data from the source system will be transformed, restructured, or mapped to fit the data format, structure, and requirements of the target system. Data mapping ensures that the data is accurately and correctly transferred from one system to another during the migration process. This provides a key piece of documentation to make the process auditable and transparent. 

4. Data cleansing

Using the findings from the data mapping step, you then build out the rules for data cleansing and transformation. This is an important part of the process whereby you identify and correct or remove errors, duplicates, and inaccuracies in the data. Then you transform the data to conform to the desired format, structure, and quality standards. 

This step is usually performed in a codified environment, for example, through a scripting or programming language, where the rules for cleansing and transforming the data are defined and executed as part of the data migration process. Data cleansing ensures that the data is reliable, accurate, and suitable for the target system, minimising potential issues and maximizing the effectiveness of the migration. However, choosing an ill-fitting cleansing can be problematic so it’s important to choose the right, business-friendly tool for your organisation.

5. Execute the data migration

Now to put your grand plan into action. The strategy for your data migration project should already outline the controls to apply when extracting the data from the source and migrating it to the new system, taking extra care to ensure that the transformation of the data will apply to the new system’s format. A big contributor to the projects involving complex data orchestration activities is the use of ETL platforms such as Data360Analyse or Alteryx. Firstly, because the agile nature of these platforms allows for quick adaptation and flexibility in handling changing requirements or complexities during the migration process. 

Secondly, they also are transparent platforms providing clear visibility into the data transformation. This allows you to better understand data flow and facilitate troubleshooting as needed. And moreover, they provided a repeatable framework that’s also completely auditable, meaning you’ll have access to a comprehensive record of the data migration activities. This is very constructive for traceability, compliance, and post-migration analysis or validation.

6. Testing and validation

After execution of your data migration, it’s time to test the results. The first thing to do here is to validate the quality of your data in the target system using the key metrics of data quality. If you have been regularly monitoring the migration process, it’s likely that there will only be a few discrepancies or errors in the transferred data. However, it’s crucial to take a close look in case anything was missed, migrated inaccurately and so forth. Thankfully, there are automated tools that can do this quite easily and effectively, especially with great volumes of data. A good rule of thumb is for the testing and validation to be done not by the data migration team but with the business SMEs. 

In this step, you’ll also ensure that all issues are resolved to avoid barriers and bottlenecks in the future. It pays to do a thorough retrospective to reflect on the data migration process, noting the learnings and challenges to help inform and improve future processes. After a successful post-migration, the old system can be retired.

7. Go-live

Now it’s time to have the new system up and running and ready for use. This phase should include activities such as user training, change management, and post-migration support to facilitate a smooth transition and ensure that the migrated data is effectively utilised in the target environment.

It can be beneficial for organisations to engage skilled experts for the project to ensure seamless change management and help equip teams with the capability to use the data within the new system, rapidly respond to changes or manage any issues or concerns. 

A data migration project is a sizable job and it’s critical to get it right to maintain data integrity and business continuity. Having the right teams, tools and talent through every phase of the migration process is critical to a successful execution. To learn more about the organisational benefits of a robust data migration framework and how to implement it, contact the Optivia team today.