Best Strategies Regarding Data Migration From Legacy Systems
Although it may not seem like it at first, data migration from legacy systems can be bumpy and lengthy. However, the process is practically inevitable for every data-driven enterprise. Because older data systems have limitations that cannot be overcome, legacy systems will eventually need to be replaced. The benefits of data migration for business competitiveness do not have to be expensive or risky as long as the data transfer is executed using best practices frameworks and with adequate preparation.
In this article, we will review the main requirements and risks that data migration involves and provide a clear step-by-step plan for migrating legacy systems.
Main Requirements for Data Migration
Data migration is the process of transferring data from one system to another – usually from an outdated system to a modern one (e.g., moving from on-premises hardware to the cloud). The main goal of data migration is to enhance the safety, performance, and overall efficiency of data operations while meeting or exceeding the business process requirements.
Although there are many details to think about, below are the 4 main requirements that you should consider before beginning legacy system data migration:
Create a Dedicated Data Migration Project
Migrating data is not an easy task that can simply be added to the engineering team’s backlog of tasks. Though it may seem straightforward at first glance, in reality, data migration is a highly complex and challenging process. A mistake inside the code can be easily fixed. However, recovering lost data can take much longer (if it is possible at all), and if the data is leaked in the process, it can seriously damage a business’s reputation.
Therefore, before planning data migration, it is important to treat it as a separate project, appoint a team, make a plan, and allocate resources specifically for this task. Leaving the data migration task in the backlog until someone finds the time for it will almost guarantee that the project will be orphaned and ultimately fail.
Estimate Data Migration Costs
One of the biggest mistakes managers and business stakeholders can make at the start of a project is underestimating the scope of the likely expenses and miscalculating the budget.
Direct costs, such as employee salaries, overhead, licensing, and ongoing maintenance costs, are usually easy to account for. However, an educated approach will also take into account that many costs are indirect. These may include missed opportunities, frequent downtime, and security.
In addition, do not underestimate the person-hours spent on data migration either, as tasks can take longer than you expect, even with automation. For a high-quality outcome, every stage requires thorough analysis, planning, testing, and sufficient time investment.
Check Data Quality
According to Gartner, many organizations believe that poor data quality is responsible for an average of $15 million in losses per year. Data migration is the perfect opportunity to test the data’s accuracy and integrity, even if there are no problems with it at first glance. The data audit step will also help to ensure that the data structure complies with the new platform developers that it will migrate to.
For example, suppose you are migrating a database from an older version of a database management system (DBMS) to a newer one. The older DBMS version allowed storing dates in a specific format, such as “YYYY-MM-DD.” However, the new DBMS version requires dates to be stored in a different format, such as “MM/DD/YYYY.” If the data is not converted to the new format before migration, it could result in incorrect date representations, leading to data integrity issues and issues with subsequent operations or queries that rely on proper date handling.
Hire the Right People
Make sure you hire the right people for your data migration tasks. To do so, analyze the project’s scope, the skills required, and the infrastructure needed in advance. For example, if you need to migrate to the AWS cloud, you should ensure that you have experts who work specifically with this platform.
Then, see if you have the required experts in-house. If you don’t, consider hiring data migration experts from an outsourced software development service provider. Ask for their methodology and risk management plan apart from time estimates and rates. Don’t trust a low price, and don’t fall for over-delivery. Make sure you understand your role, responsibilities, and resources needed to support the data migration process.
Although it may be difficult, executive knowledge holders need to make time to engage in the process too. Understanding the big picture, they are often the ones who can figure out how to sort data and which to migrate or keep. It is essential to try to cooperate with them as much as possible.
How to Migrate Data from Legacy Systems
After completing the preparation before migrating, you will need to think about your legacy system migration strategy. This involves conducting a data audit, creating a migration plan, building a backup, creating and testing a replacement system, and establishing the processes that are required for running and maintaining the new system.
Make an Audit
A data audit helps to determine the current state of your data: existing bugs and errors, data completeness, relevancy, usefulness, related documentation, and infrastructure. Next, the IT team, stakeholders, and finance department need to cooperate to figure out the budget and goals for migration related to the existing systems.
Here is the checklist of questions you could also ask yourself before the migration:
- What data do you have? What are its format, structure, and dependencies?
- What is causing the need to migrate?
- What are the potential benefits and risks?
- What fields do you need to move?
- Will data migration impact users?
- Are there enough data security measures that protect the data?
- Which important events could have significantly affected the data in the past?
- Do you want to keep the data as is or perform any cleanup?
- Do you need migration only, or is it better to modernize legacy applications as well?
- How will you measure success after the migration?
Remember that when working with data, you also work with business logic, as databases are made according to business requirements.
Create a Migration Plan
Data migration from legacy systems to modern databases means more than just copying and pasting data. It is a complex, long, and demanding project. Because of this, the migration plan should have clearly defined goals and objectives, responsibilities, priorities, limitations, etc. The data migration plan will help you to reach the desired goals by following a healthy, productive timeline and eliminating the risk of failure.
Some points you could include in the plan are the legacy system migration strategy, approach, project scope, stacks to be used, timeline, milestones, and team roles. It is essential to have a migration strategy with a rollback plan in place in case of any failure.
Learn how to plan each step and achieve the best efficiency with the application modernization roadmap
Build a Backup
Failures happen. They are a normal part of any change management process. Therefore, it is important to have a data backup in case anything goes wrong. Create a duplicate copy of digital information or files, or think about storing the data both on the cloud and on-premises. This will ensure their preservation and availability in the event of data loss, system failures, or other unforeseen events that could severely damage the business or its customers.
Create the Intended System
The next data migration step is to set up the target platform where to data will be migrated. Prepare the new environment, check whether the database is compatible with it, and make sure that it suits your objectives.
Knowing how to choose the perfect platform is extremely important. You could go for an existing platform or custom-make it, depending on your organization’s needs. You could also choose between cloud and on-premises platforms. Compare and decide with the IT team and business stakeholders.
Test the New Database
Double-check the new system during the process as well as at the end. Make sure that the data maintains integrity, is securely encrypted, and properly formatted. Look for any signs of data corruption and faults in the system. Check if applications are functional and ensure that you haven’t missed anything. Test all the stages of interacting with data. Work on catching and eliminating bugs.
Run the Updated Solution
After thorough testing, it is safe to deploy the updated solution. Monitor the output or logs for any errors or messages during the execution. Once the solution completes its execution, review the results or generated output. If needed, analyze any error reports or logs generated during the execution for troubleshooting. Repeat the process as necessary or adjust based on the solution’s performance and desired outcomes.
Support the New System
Finally, after migrating, it is crucial to ensure the maintenance of the modern data environment. Continue regularly monitoring the state of data and analyzing the new system’s performance. This will help improve, optimize, and fix what needs to be fixed before it becomes a bigger issue. For example, AWS’s CloudWatch can be used to support the process.
A good practice would also be to schedule a meeting to reflect on the migration results. Work on post-migration strategy and long-term development goals. This will help the users to adapt quicker and more efficiently.
Data Strategy Post Migration
After executing legacy systems migration, you will need a proper transition period to ensure data consistency and to avoid poor changeover, as it can become extremely time-consuming to fix. A necessary process is change management, the incorporation of new technologies into the final business processes. This may involve training employees, changing operational activities, reorganizing workflows, etc.
Consider collaborating with the data migration team to establish an appropriate technological foundation. This helps smoothen the transition and new technologies implementation. Another tip is to prepare the materials which would help end-users adapt to the changes, like support center guides, helpdesks, or training materials.
Mitigating Data Migration Risks to Succeed
Although migrating from legacy data systems is desirable, the process is not easy. As we already mentioned, there can be many challenges and risks along the way. To ensure a successful outcome, it is important to be aware of these beforehand.
The risks of data migration of legacy systems to be aware of are poor data quality, lack of competence, poor performance, and unmapped cross-object dependencies.
Mitigating Poor Data Quality
Migrating from legacy software means that the quality of data and metadata can be outdated and noncompliant with modern standards. This is why it is important to audit and assess the current state of your databases and their dependencies before migrating. After the audit, it’s easier to know the exact changes and improvements that need to be made, with particular attention needed to the data’s relation to business logic. The IT team might need to cooperate with business stakeholders to decide which data to delete or update.
To avoid poor data quality, hire responsible experts (e.g., data quality analysts) who can ensure the data’s integrity and consistency at every stage of interacting with it. Setting realistic timelines is also crucial, as the race to deliver solutions quickly can cause unwarranted damage to databases. Don’t forget about safety measures and reviewing configurations carefully.
Addressing Lack of Competence
For migration to succeed, team members must be on the same page in understanding the importance of the data migration process. Most importantly, they need to have adequate skills for data migration. This means having experience in both the old and new systems being migrated to, as well as competency in database management.
If your company needs more experts in migration, consider engaging external service providers.
Improving SQL Performance
SQL (Structured Query Language) is a standard language used for interacting with relational databases and performing various operations such as retrieving, inserting, updating, and deleting data. Slow data migration from legacy systems to SQL server processing is a common problem that can lead to delays and potential disruptions in business operations.
To address the issue, optimize database schema and indexes, improve data transformation processes, upgrade hardware resources, and optimize network connectivity. Additionally, apply performance-tuning techniques, such as query optimization and caching, to enhance the overall efficiency of SQL queries during data migration.
Mapping Cross-Object Dependencies
Migrating data that have interdependencies between different objects or entities within a system is challenging. These dependencies can complicate the data migration process and potentially lead to errors or inconsistencies if not properly managed. Moreover, discovering dependencies only after the launch is still common.
To address the challenge, establish a migration sequence that considers the dependencies between objects migrating the objects in the appropriate order, update the data mappings, and implement robust validation and testing procedures to verify the correctness of the migrated data.
Need Help Migrating Data from Legacy Systems?
At Maven Solutions, we use our own application modernization and legacy migration tools following industry best practices. During the Inception phase, we analyze your software in detail, conduct an audit, and create a plan that helps deliver the most profitable migration result and prevent failure. Only after that, we proceed with the Development and Change Management stages.
With 13 years of experience working with businesses of different niches (e.g., Travel, Fashion, Goods for children, Food, Logistics, and Branded products) and sizes (e.g., from startups to Fortune 500 and the Inc.5000), 100+ successful projects and 20,000+ features developed, Maven has earned the trust of many companies, most of which continue long-term cooperation with us.
Data migration from legacy systems is a challenging process that requires thorough preparation. In order to succeed and benefit, follow a few migration framework recommendations.
Before beginning the migration, ensure that you created a separate migration project, estimated costs correctly, checked data quality, hired the right people, and considered using automation tools. Then, follow the aforementioned step-by-step plan and prepare to deal with some common challenges.
If you are looking for a service provider to help you choose the best legacy system migration methodology and migrate trouble-free, Maven Solutions can be just the partner you need. Contact us to discuss your new strategy.
What is legacy data migration?
Legacy data migration is the process of transferring data from outdated or unsupported systems, applications, or storage formats to modern, up-to-date systems or formats, often to ensure data integrity, accessibility, and usability.
When do I need to migrate legacy data to a new environment?
The need to migrate legacy data occurs when the existing environment is no longer capable of meeting your organization’s evolving needs, whether it’s due to technological advancements, compliance requirements, performance issues, or cost considerations.
What is the ROI of cloud migration?
By migrating, you can significantly save costs for hardware and maintenance, provide scalability and agility, increase efficiency and productivity, enhance security and compliance, enhance resilience, and minimize downtime in the event of a disaster or system failure.
How long does the migration process take?
On average, the migration process takes 3-4 months, depending on the strategy, data scope, and complexity.
What are the steps in a data migration?
To migrate the data, you first need to make an audit of your existing data and systems, then create a migration plan, build a backup, create or choose the new environment, run, test, and support the new solution.