How to accelerate legacy software integration

While the data-driven revolution continues to take the business world by storm, many sectors still remain heavily reliant on specialist legacy systems.

The base architecture of these softwares is sometimes decades-old, designed in an era before cloud computing, AI-driven analytics, and modern data integration.

While once cutting-edge, these systems now act as barriers to efficiency, making it difficult for you to eliminate data silos, integrate new tools, and collaborate across departments.

In this guide, we’ll explore the three primary ways of integrating legacy software with modern data management. We’ll also show how this can be done without overburdening IT teams and sacrificing perfectly good existing tools!

The challenge of legacy software

Unlike industries that have rapidly adopted digital transformation, many sectors such as construction and the public sector still struggle with software systems designed in a bygone era.

While these solutions can be great at their primary functions, they’re often difficult to integrate with modern tools, slowing down workflows and burdening IT teams with maintenance and manual data reconciliation.

This diverts resources away from more strategic activities, and hampers the ability of project managers to make timely data-driven decisions. However, overcoming these challenges is not always as simple as upgrading all software to modern systems with high interoperability or integration ability.

The transition can be long-winded, highly disruptive, and extremely expensive. It can also require teams across the business to sacrifice the functionality they need and the tools they’re used to using.

The common approaches to legacy software integration

Let’s look at the three primary ways you can integrate existing tools with modern technologies to see if we can find a solution.

Rip and replace

The bluntest approach to modernising legacy software is to completely replace older systems with new platforms designed from the ground up for a modern approach to data management.

While this ensures you have access to cutting-edge technology, it comes with significant risk and challenges. For starters, it can be costly, often requiring extensive downtime and the retraining of staff on brand new systems. It can also risk the loss of crucial historical data without somewhere to store information currently held in the old systems.

The disruption caused by this transition can often lead to delays. It also introduces a window of hampered communication between departments and operational inefficiencies as teams adjust to new workflows and tools.

All of this is before considering that some older software is still the best in its class for specific applications, making it illogical to abandon them entirely simply because they aren’t interoperable with modern data platforms.

Plenty of businesses that go down this route ultimately find the transition overwhelming and costly, leading to frustration and difficulty getting buy-in and full user adoption.

If certain existing software is already overdue for an upgrade, then by all means let data interoperability be the straw that breaks the camel's back. However, in general, it’s best to look for a less disruptive approach that retains valuable functionality.

Middleware and APIs

A less heavy-handed approach is to bridge the gap between legacy software and modern data management with ‘middleware’ and custom-built APIs.

This lets you integrate new functionality without completely replacing perfectly functioning existing software, at first glance offering what seems like a middle ground between full replacement and doing nothing.

However, the short-term relief provided by middleware solutions often creates long-term technical debt. After creation, often by costly external IT specialists, APIs generally require ongoing maintenance.

For example, even minor updates to the tools being plugged into might cause compatibility issues that lead to APIs not running properly. Specialist support is then needed for hot fixes while productivity takes a hit.

Custom-built middleware also often lacks scalability, meaning as you grow and implement new tools, these then need further custom APIs to keep data flowing seamlessly between systems. This gets increasingly complex and difficult to manage as time goes on, and it can also become prohibitively expensive.

Middleware solutions also often struggle to standardise and restructure data, requiring an extra layer of manual intervention to ensure consistency across every platform. For complex job functions like project management that rely on data from multiple departments and tools, this hassle adds needless complexity.

Data lakes and data vaults

Data vaults and lakehouses generally provide the most efficient way to modernise data management without sacrificing functionality or productivity. This approach lets you centralise and standardise all data without disrupting existing workflows.

Using a data ‘vault’ to store legacy data involves collecting information from various systems of different ages and architectures and then normalising it to ensure it’s in the right format for use alongside data from newer tools. This allows you to keep crucial legacy software while still leveraging the power of modern data-driven decision-making.

The legacy data is stored in the cloud and can be accessed as and when needed while feeding the information to a unified ‘lake’ of data, just as the rest of your more modern software can.

This safe storage means all historical data can be easily retrieved for audit and compliance. It also means that tools like analytics can use this historical data for greater insights and accuracy, even though the original software wouldn't be compatible with modern data architecture.

Managing data in this way means your historical data works alongside your modern real-time data for seamless visibility of crucial information across the full timeline of the business.

Unlike a traditional data warehouse which requires the information to be rigidly structured by hand before being stored, data vaults allow for raw data ingestion. This data is then automatically formatted for use by the data lake.

The power of the data vault approach

Unlike traditional data storage solutions, the data vault approach centralises all company data in a structured, highly scalable format. This lets you extract value from historical data, keep functioning tools, and seamlessly integrate new technologies. Let’s look at why this is fast becoming the gold standard of legacy software integration.

Enabling seamless data integration and accessibility

A data vault approach supports the creation of a single source of truth for data across the business by feeding data into a unified platform. This breaks down data silos and ensures all departments have access to the same information even if they don’t personally use certain specialist tools in their day-to-day processes.

This happens automatically, removing the need for manual reconciliation and the potential for human error leading to inaccurate datasets.

By structuring data in a centralised, accessible format, you can:

• Automate reporting processes. Reduce the time spent on compiling spreadsheets and pulling numbers from multiple disparate systems.

• Improve cross-department collaboration. A data vault and data lake ensure that finance, operations, and project management teams work with the same data set.

• Enhance decision-making. Historical data lends context to real-time information providing an enhanced understanding of project performance, costs, and risks.

Future-proofing operations and reducing IT burdens

As we’ve touched on, one of the biggest advantages of the data vault approach is its scalability. As your business needs evolve and new technologies emerge, all data can work in tandem to provide unparalleled opportunities for analytics and data-driven decisions.

This ensures that historical data continues to be relevant even as wider business tools are onboarded, sunsetted, or altered throughout different projects.

It also reduces the burden on IT teams, since there’s no need to spend countless hours maintaining legacy integrations and fixing APIs or manually handling data inconsistencies.

How 5Y accelerates legacy software integration

5Y works with all of your existing tools and is built from the ground up to support companies in modernising their data management without disruption. Put simply, it’s a data lake solution that incorporates data vault methodology to enable you to retain legacy systems.

Whether you’re retaining or replacing legacy software, legacy data is archived and stored securely in a robust cloud-based data vault. This data is then incorporated into a modern reporting and analytics platform to enable you to leverage powerful insights. You can also search, access, and restore legacy data when needed.

Built on the Microsoft Azure architecture for seamless interoperability, the 5Y data platform can:

• Ingest data from any source. 5Y seamlessly integrates with ERP, CRM, BIM, CMS, and project management tools, ensuring that all relevant data is centralised.

• Structure data automatically. Rather than relying on manual processes, 5Y uses automated data modelling techniques to ensure that all information is formatted consistently and ready for analysis.

• Enable real-time analytics and forecasting. With all data stored in a structured single source of truth, all departments can generate reports instantly, make informed decisions, and identify trends proactively.

• Maintain security and compliance. 5Y provides a secure and compliant environment for data storage, helping you meet evolving industry regulations without additional complexity.

With 80% of reports and dashboards available out-of-the-box, 5Y can be rapidly tailored to fit your precise needs. That means you can start producing insights in days, not months.

5Y also works on a simple subscription basis, meaning you don’t need to buy the software upfront and we constantly add new features as business needs evolve.

And there’s no need to wait for a new migration project to start. By migrating your data to the 5Y platform now, you can streamline your data management processes, improve data quality, and simplify future transitions.

Download our guide to learn more.