Challenges of Implementing Data Analytics Tools With Legacy Systems


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Big data is all the rage today and for a good reason: it has enabled businesses to increase their efficiency and understand the market at a much deeper level than ever.

Coupled with modern analytics and machine learning, it has optimized the decision-making process and ushered in new technological marvels, including artificial intelligence and predictive maintenance.

Not all businesses have been able to harness the full power of big data. Some businesses don’t have a strategy to use the data they are collecting. Other businesses are having a hard time transitioning from making decisions based on instincts to making decisions based on facts and data.

And, some businesses might have an infrastructure problem. They might rely on legacy systems, i.e. software that has proven very useful over the past few decades. Yet, today, these same systems might be hindering them from making the most of what industry 4.0 has to offer.

With that said, let’s take a closer look at legacy systems and see why they can pose a challenge to modernization.

What are legacy systems?

Before cloud computing took over, businesses had to have their own servers and build their own infrastructure to host their applications. It was a very costly process that required a sizable investment from the business.

In return, businesses expected their investment to pay off. They relied on the technology to run their day-to-day operations, and they needed both the software and the hardware to perform at their peak.

Additionally, given the size of the investment, businesses demanded that their systems survive successive releases and versions instead of being replaced every year.

As a result, businesses came to depend on these systems, which is why changing them or updating them can be challenging. In fact, the IRS is still using a legacy system that is more than 60 years old to file taxes and manage refunds.

However, legacy systems aren’t only defined by their age. From a functional standpoint, a legacy system is one that hinders the organization and impedes its growth. This could be because the system in question lacks the necessary IT support or can no longer fulfill the organization’s needs.

Why do legacy systems present a problem for big data?

Despite being archaic, legacy systems are bursting at the seams with valuable data. After all, these systems have been collecting data for years, if not decades; this data can prove instrumental in business intelligence or analytics.

That said, legacy systems suffer from a couple of problems that make them unsuitable from a data analytics standpoint:

  • To begin with, most legacy systems are rigid. They were built for a specific purpose for a specific company, making them inflexible when it comes to other use cases.
  • Legacy systems tend to be slow, making them ill-suited to deal with large amounts of data. This becomes all the more pertinent when you consider the massive amounts of data generated by companies nowadays. And, as IoT devices spread and become more popular, the amount of data generated is only going to skyrocket.
  • Moreover, seeing as these systems tend to be old, ensuring their security from malicious attackers can be a hassle.

Therefore, businesses can greatly benefit from upgrading their legacy systems and bringing them into the 21st century.

Not only will it enable them to benefit from the data they have stored, but it will also give them a competitive advantage. Adopting a more flexible, modern system can spur growth while keeping the data more secure than it ever was on an on-premise server.

What are the challenges of integrating legacy systems with more advanced solutions?

For most companies, it is better to integrate legacy systems with more modern solutions, such as cloud computing, rather than having to overhaul the company’s entire infrastructure.

In addition to preserving the legacy data, an integration would ensure that the company is still using the software it is comfortable with while benefiting from more modern solutions that are better suited to handling big data.

Unfortunately, companies that want to integrate the new with the old are bound to face several challenges.

For starters, a lot of the solutions offered today, be it cloud computing or SaaS, are incompatible with antiquated legacy systems. As a result, companies will have to spend a significant amount of time writing custom code to merge the old and new systems.

Without this custom code, accessing the data in the legacy system and combining it with another database can be an arduous process.

Without proper integration, most companies already have a problem where their data exists in silos. The thing is that each department generates its own data, and the lack of a solution such as a modern ERP means that each department is incapable of accessing the data it needs.

Gathering all this data, cleaning it, and putting it in one central repository will take some time.

Another problem is the sluggishness of legacy systems compared to modern solutions. The data refresh rate alone may pose a problem as any long delay can harm the business, forcing the analysts to grapple with outdated information or to be patient and wait a while for up-to-date data.

For many employees, legacy technology may be burdensome. It will be slow compared to more modern solutions. While there are ways to speed up some of the legacy equipment, without the right talent that can write custom code, it can be very difficult to have software systems communicate with modern platforms.

Alternatively, if a company wanted to migrate entirely to a new platform, it would still face significant challenges.

Mainly, such a migration would be so costly and complicated that it would be near impossible. Replacing the business logic alone would be around quintuple the cost of reuse. Replacing the legacy system wholesale comes with its own risks that increase the overall cost of the migration.

How can a company integrate data analytics with legacy systems?

The first thing to bear in mind is the importance of being strategic; digital transformation is a marathon, not a sprint.

With that in mind, there are a few best practices companies might want to follow. For instance, companies should avoid modernizing all their systems in one fell swoop; instead, it might be better to approach the task in a piece-wise fashion.

After all, slow and steady wins the race.

Michelle Laurey works as a VA for small businesses. She loves talking business, and productivity, and share her experience with others. Outside her keyboard, she spends time with her Kindle library or binge-watching Billions. Her superpower? Vinyasa flow! Talk to her on Twitter @michelle_laurey.

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