Many times, companies invest in top-of-the-line hardware and software systems for their business to operate at its peak. When this happens, companies may not be thinking long-term or have their overall vision in mind. As time passes and companies grow, or technology changes many are apprehensive to make another investment to update or replace their hardware and software systems.
These original systems are known as legacy systems because they function the way they were designed to, but they cannot expand and integrate with new technology.
Why do some companies resist updating their legacy systems?
Maintaining your legacy system becomes more expensive as time goes by, but in many cases, those costs and benefits can seem to remain lower than the cost of upgrading to an entirely new system, both in terms of money spent and staff resources.
Many times, fear of change causes companies to keep their legacy systems in place. Companies that have tenured employees may not see the benefit of modernization services because they are outweighed by the difficulties of changing to a new system, which can cause resistance and frustration in their employee’s day-to-day operations.
Current legacy systems may have been constructed using a programming language that is older and not as popular as today’s languages, making integrations and mobility features more difficult. The documentation about the legacy system may no longer be in the company, and in those cases, the planning process for upgrading to a new system becomes increasingly more difficult. Excel SoftSources has experienced both of these situations and has experienced developers to work through those problems and smooth the way to an upgraded system.
Is upgrading legacy systems necessary?
At some point, keeping up maintenance on a legacy system will no longer be worth the expenditure. Once the updates are too time consuming and difficult to make, or the system completely fails, your company may not have any backup data stored.
Along with always increasing maintenance costs, many times your data is confined to silos in your legacy systems. When most legacy systems were built, integration with other systems was never a consideration.
For larger companies, one team might decide to upgrade, while the rest of the company might not – or one team might decide to save money and stay with the legacy system while others choose to move on. There will be difficulties in communication between the teams, and moving data back and forth will become a hassle.
When data becomes more difficult to store, find, and share, compliance becomes a problem.
An example of this is GDPR, which mandates that companies know (and can prove) what customer data they have, where it’s stored, and who has access to it. For siloed systems, meeting compliance requirements becomes a more onerous process.
One final reason to take advantage of legacy application modernization services is that companies that do not move to upgrade suffer from weakened security over time.
Many legacy systems still have security measures such as passwords that have been hard-coded. Although a new innovation when those systems were developed, it has become a liability in today’s world.
What should legacy application modernization include?
The most significant benefit of legacy application modernization services has to do with migrating your data to a system that is not only secure but also easier to share with multiple people in your company.
When it comes to migration, there are five steps to consider:
- Exporting Data – In legacy systems, data is often duplicated, fragmented, or just incomplete. Step one in any migration is ensuring a safe extraction of all necessary data.
- Updating Data Formats – Using data mapping, changing the data so that it meets the requirements of the new system is key in a successful migration. Because of the lack of compatibility between the legacy system(s) and the new system, this can be the most sensitive and time-intensive part of the process.
- Cleaning Up Existing Data – This is when duplicates are removed, incomplete data gets completed, and unnecessary data is removed.
- Data Validation – This involves importing a sample data set to test for errors and problems to see if unforeseen issues have arisen.
- Data Import – The final step involves uploading all of the cleaned data into the modern system for use.