5 Signs It's Time to Say Goodbye to Your Legacy Informatics Platform
Goodbyes are hard, especially after you’ve spent a pretty penny on technology that everyone’s gotten used to. As research and development becomes more complex, scientists need tools and technologies that not only accelerate innovation but can grow with their needs. More often than not, legacy systems create unforeseen challenges with a good reason to force consideration of a new ELN or LIMS software.
So how do you know that it’s time to break things off? Not everyone in your organization might be seeing the same signs you’ve been noticing for years. While their hesitancy might give you pause, here are five questions that can help reveal telltale signs of when it’s time to say goodbye:
1. Is the legacy platform expensive to maintain?
After spending several years implementing a platform and evangelizing its usage across R&D teams, it’s a rewarding experience to never have to touch it again—for a couple of weeks or so.
In reality, legacy systems become more expensive and cumbersome to maintain over time. For one, legacy systems can’t effectively centralize data. In an attempt to mitigate this, enterprises either connect disparate systems one-by-one, or build in-house custom solutions, which are extremely expensive, complex, and time-consuming to maintain.
As a result, data becomes siloed, preventing you from extracting meaningful R&D insights or collaborating efficiently between teams. Inevitably, you end up with a system that doesn’t work as intended, breaks often, and is so difficult to use that scientists and managers will effectively ignore it.
“...the average cost of developing and launching a new drug [is] around $1.4 billion, while the ROI is expected to be below 4%. It’s obvious that there’s plenty of room for improvement and expenditure optimization. A big part of these costs occur due to the outdated legacy infrastructure in the life sciences industry…”
—Lily Smirnova, Pharma Legacy Transformation. Growing old vs growing wise.
Ask yourself: Is the cost of upgrading and using a scalable system greater than staying on my current legacy system?
If you need help assessing this, here’s a guide on how to measure the value that your digital systems are providing to your business.
2. Do you have a trusted partner in your solution provider?
On average, customer success and support make up only 7% of the workforce of legacy life science software providers. (By contrast, they make up 14% of Benchling’s workforce.) In addition, many providers lack the domain expertise necessary to support modern science and cannot effectively deliver success.
If your organization needs to change or scale R&D initiatives, the software and the people supporting those initiatives should be able to follow suit. Legacy software often becomes a bottleneck, forcing scientists to change the way they work in order to accommodate the limitations of the software, rather than the other way around.
Determine whether or not your current software partners are structured to engineer solutions around your goals and evolve with your growing needs.
Ask yourself:How has my incumbent solution reduced the burden of maintaining and modifying the platform to meet evolving business and scientific needs?
3. How compatible is this platform with other critical technologies?
As acquisition trends and global mergers steadily continue, larger R&D organizations are left with an amalgam of tools, technologies, and instruments. Legacy systems are decentralized and disconnected from other data sources and cannot automatically pull and push data out of your platform.
Most older platforms also leverage private APIs, and it becomes an added cost since users must pay vendors in order to simply access these APIs and create integrations. This is highly inefficient when scientists require real-time data to meet changing mandates and initiatives.
The superior option is a system with public APIs, which allow designated administrators to create integrations without vendor involvement or additional cost. Having an open developer platform that’s fast, well-documented, and contains extensive public APIs will ensure all systems are connected. It will only become more critical for your digital systems to integrate with lab instruments and robotics as scientific workflows evolve and throughput continue to increase. The only path forward is to find a solution that helps unify your R&D data ecosystem to support the increasing diversity, complexity, and scale of your data.
Ask yourself:Does the immediate convenience of staying on my current platform outweigh its inability to integrate smoothly with the larger informatics ecosystem?
4. Is this platform still useful?
Whether due to changing IT infrastructure, increases in data volume, outsourcing, or globalization, many legacy platforms have become costly to maintain while failing to adapt to new business requirements. Here’s how you can quickly evaluate the incumbent platform’s usefulness:
What was its original purpose and does it still serve that purpose effectively?
What is this system currently used for?
How often is it used?
Has the original goal changed?
What are other similar companies using to scale their business?
Today’s life science labs operate differently from those of even a decade ago. Datasets are larger, teams are more multidisciplinary, and projects are more complex. Companies that want to stay ahead of the competition and make faster, data-driven decisions must look towards flexible and future-forward solutions.
Ask yourself: If the current platform is no longer useful or no longer meets the original purpose it was intended for, then is it still worth using?
5. Is it easy to use?
Legacy systems are notoriously difficult to use. They weren’t built to support the scale of today’s research, and they weren’t built using modern standards of software design. Your scientists know this too. The need to constantly persuade scientists to use a legacy system that they just don’t like using is tedious and time-consuming, for both IT and R&D. Scientists end up spending more time struggling with these systems than they save—or worse, they opt to do things manually instead.
Look for intuitive software that:
Empowers scientistsby achieving time to value as quickly as possible
Reduces the burden on IT teamsand infrastructure with out-of-the-box workflow configuration and fully-documented APIs for platform extensibility
Ask yourself: Does my current system have an intuitive user interface and can keep pace with the need for rapid process iteration? Does the burden of managing manual processes fall on scientists?
Change is hard, but trying to get ahead using outdated systems in the fast-paced world of life sciences R&D will prove to be more difficult. Even if your system “still sort of works”, it’s always a good time to consider how moving on to a newer, more modern solution might better support your business in the years to come.
If you’re currently relying on legacy ELNs and LIMS that silo your data, or custom-built systems that require constant IT upkeep, you could unlock greater efficiency with a modern platform. Learn how a global biopharma leader improved scientists’ satisfaction with data capture tools by 3X by moving off of their legacy software.