When people start a conversation about legacy systems, there’s a good chance that replacing the system comes up. And why not? Legacy systems don’t always have the newest bells and whistles, and they may not immediately signal a tech-forward business strategy. Boardrooms and shareholders want to see signs of digital transformation, which could mean adopting new technology solutions to replace older ones.

For many businesses, offloading a legacy system isn’t so simple. Choosing to replace a legacy system is a careful dance between cost and cost savings, innovation and reliability, and balancing history with the future.

The Modern Data Company (Modern) is here to ask whether everyone has been thinking about this dance all wrong. Do older systems need to go simply because they’re older, or is there a better way to revitalize an existing data architecture? In Modern’s point of view, legacy systems can still offer value if they’re integrated effectively as-is through the use of a modern integration technology. In other words, it is possible to modernize how a legacy system is integrated and utilized rather than replacing it completely. Despite the pressure to upgrade legacy systems, there are several good reasons not to do so.

 

The system can be reliable

Companies could keep their legacy systems in place as part of an overall data strategy unless the system is unreliable or insufficient from an operations, scale, functionality, or security perspective. The classic saying “if it ain’t broke, don’t fix it!” comes to mind. If a legacy system is performing well and meeting the needs of its users, it doesn’t need to be replaced. This is especially true if you can implement a solution that will modernize how you make use of the legacy system.

 

Legacy systems might be more cost-effective

Upgrading a legacy system might save costs in the long run…but it might not. If the cost of upgrading outpaces business value for the foreseeable future, companies might be better off sticking with their legacy systems and avoiding both the costs and distraction of a migration. The opportunity cost of time spent on a migration has to be accounted for along with the hard costs.

In addition, training team members already used to the legacy system might seem like a waste from an operational perspective. If the system is still performing its function well, the upheaval within departments to learn a new system might cause unnecessary downtime, errors, or decreased productivity. There are some real risks involved in a migration, so make sure it will be worth those risks.

 

“Old” doesn’t necessarily mean “outdated”

For many companies, legacy systems are integral to current business processes up and downstream within the existing architecture. It’s only when a company decides to purchase a new solution for some aspect of the business that integration challenges arise.

Businesses also inherit other legacy systems during acquisitions. A single umbrella brand may have multiple legacy systems that still work for individual arms of the enterprise but struggle to work together. This is not a reason to offload all legacy systems but rather an opportunity to think about the entire architecture differently. Much like an old car or an old pair of jeans might not be as fashionable as they once were, if they still work well and are comfortable and functional for you, don’t feel pressure to get rid of them. A legacy system migration should be due to real need and projected incremental value, not because of pressure to be fashionable by using the hottest new options on the market.

 

It’s time to consider an entirely new approach to the legacy system challenge

Right now, companies purchase new technology and data solutions to solve their data challenges in a piecemeal fashion. They’re having trouble with one aspect of their architecture, so they find a solution that addresses that aspect.

Then they have two problems: the legacy system and the integration challenges of the new tool. So, they purchase a bridge solution that costs more money, more time, and more training. And it works fine until the company needs to shift priorities and make more changes, sending the entire structure into a state of alarm.

One common issue is that companies leverage third-party vendors to help them move to the cloud and then lose the ability to control their own data. Another common issue occurs when companies purchase a service designed to deliver faster insights and then pay enormous amounts to upload and store that data with that vendor.

Modern suggests a different approach: our data operating system. A data operating system works as a connective overlay that unites all tools within a company’s current architecture. There’s no need to upgrade legacy systems, and companies don’t have to adapt their entire operations model to suit their new data tool. Instead, a data operating system offers a flexible and composable alternative that allows teams to extract maximum value from existing tools by reducing complexity.

In addition, a data operating system allows non-technical users to build and customize their reports, queries, and dashboards through a right-to-left approach to data. Users simply ask the system for what they need and then it builds the right pipeline for them without the need for coding expertise or involving IT.

 

Revitalize your legacy systems and protect your data with DataOS

Modern’s DataOS is a first-of-its-kind data operating system designed to free data for real-time use. It offers a connective tissue for legacy systems so that there’s no risk of data loss and no need to choose which systems to upgrade and which to keep. DataOS brings all data under one single roof without needing to migrate data to a new location.

Legacy systems can still offer value. They only need a comprehensive solution that addresses all their challenges with a single operating layer. Contact us to find out how DataOS can transform your data into an all-encompassing tool for business and tech users alike.