Is your R&D cloud platform outdated? Here’s how to check

Finding the right R&D data management platform can feel like decoding the human genome. Between point solutions, platforms, and seemingly infinite features, there are too many choices and combinations to count. 

Add to that the specific demands of biotech R&D—ELNs, LIMS, LES, workflow tools, molecular design tools, analytical software, etc—and it’s hard to find a platform that can actually deliver these needs in an integrated solution.

The truth is, any platform can claim to be “integrated” or “modern,” but few can actually live up to those promises. Some platforms are built for biotechnology research, while some legacy platforms—like Dotmatics, for instance—make big promises yet struggle to meet biotech’s complex needs.

Ultimately, buyers face a choice: Which platform should you use? 

Yet there’s another question you must answer first: What makes a data management platform superior?

We know there are tons of options. You’re probably even looking at a few of our competitors. Since you’re likely already feeling overloaded with information, we want to give you guidelines to help you wade through the muck.

(Hint: “Not breaking” is the bare minimum.)

As the biotech industry’s most widely adopted life sciences R&D cloud platform, we’ve spent years honing our platform to meet the needs of scientists with great success. Here’s what we’ve found are the most important key features to look for when choosing an R&D data management platform.

1. Your platform should be easier to use than a paper notebook.

First and foremost, your platform needs to be user-friendly. A system is only as good as the data that’s added to it, so even the smartest, flashiest, most advanced system is useless if scientists find it too cumbersome to use. When a system is too complex, scientists resort to tracking in paper notebooks simply because it’s easier. This results in data being entered twice and increases the risk of data errors.

The goal? Find a platform that scientists can use as quickly and efficiently as a paper notebook, while being able to take advantage of all the benefits of being digital.

What to look for:

• An intuitive interface: Look for a platform with a notebook that has the benefits of paper (flexibility, ease of use) and software (templates, auto-calculations, auto fill-in, etc.). Does the layout mimic what you are used to? How easy will it be for you to add data on the fly?

• Codeless configuration: Your workflows are constantly evolving as scientific understanding progresses, so your platform needs flexibility. Waiting on platform vendors to make changes will just slow down your process. When assessing the platform, ask how easy it is to change your templates. 

2. Your platform should provide a straightforward way to share data and work together on projects.

One of your platform’s main responsibilities should be bringing people and data together in one central location. When different teams (or team members) use different point solutions, it creates information silos and delays the progress of research – the exact thing you're looking to prevent. Even within platform providers, legacy systems still have on-prem options, multiple registries for different sample types, and outdated/complex search capabilities. 

A modern system should make it easy for you to collaborate with others on your team and across groups. This looks like different teams on the same project being able to cross-reference experimental results from upstream teams and have the ability to easily view the entire sample history.

What to look for:

• Cloud infrastructure: Look for a platform built on cloud infrastructure to promote collaboration across teams and sites.

• Shared projects: Your platform should make it easy for you to collaborate with others on your team and across groups.

• Single registry within a project: Your platform should allow for a single registration process for all of your sample types, from biologics to chemical compounds. This helps organizations track the entire life cycle of any sample across experiments, projects, and groups.

• Unified search: Finding answers should be easy, no matter which team you’re on or which project you’re looking for. Ask if there’s a quick way to search broadly across the platform (including notes, attachments, sample data, and specific sequences) and filter down as needed. Modern search features, like live search prompts, also enhance the user experience and can become vital when your database includes thousands of samples and entries.

3. You should only have to enter data in one place.

They say we’re integrated. We say we’re integrated. What actually matters to your R&D? 

Older infrastructures, and ones that aren’t really integrated, pose workflow challenges for your scientists. They’re often built based on multiple underlying data structures, which means your team will be forced to input data in multiple places across applications, even if they are seemingly integrated. This leads to lengthier onboardings, slower workflows due to overlapping applications with confusing capabilities, and lower data integrity across applications.

Even when legacy solutions put on a new face, systems built on older infrastructures can have painful lag times. It only gets worse as you collect more and more data, resulting in increasingly sluggish systems.

Instead, look for a modern platform with a truly integrated solution, meaning you only have to enter data in one place. Being able to access data from a central source with a single user interface saves you time, ensures you always have the most up-to-date, accurate information, and improves productivity.

What to look for:

• Cloud infrastructure: Everything starts from the ground up. Platforms built on a modern cloud infrastructure are forward-facing and help ensure efficiency. 

• Interconnected data across applications: It is crucial that the data is accessible across applications. Ask your platform provider: Is there a single underlying database and defined data structure to ensure information is easily accessible?

• Built to scale: As the amount of data scientists are producing continues to increase, modern systems should be able to keep up with the scale, without sacrificing performance. 

4. Your platform should be backed by experienced and attentive customer support.

The superior scientific software platform is not static. You need a platform that can grow with you and adapt to your needs—and that means paying attention to the people behind the platform. Who are you partnering with, and what is their experience in biotech?

Support services are historically underemphasized. Yet no matter how simple a solution you select, customer support will greatly impact your platform’s success. Inexperienced customer support leads to long, drawn-out implementation and lengthy turnaround times for system updates.

Look for a platform provider that not only demonstrates proactive customer support, but is well-versed in biology specifics. You’ll spend less time on implementation, and receive timely responses to system updates, customization requests, and bug fixes.

What to look for:

• Experience with similar companies: Do they know your workflows and unique needs? Search for a company that has worked with other bio research teams to ensure they can address the intricacies of your field. 

• Defined project plans: Ask if they have project plans in place that are tried and tested.

• Post-implementation plan: What happens once the implementation process has finished? Ensure there’s a plan to help you succeed after you’ve launched the system.

5. Your platform provider should have a history of product innovation.

The world of biotech is constantly evolving, and the tools we use are also evolving. You need a system that will keep up with scientific advancements—and keep up with you. This is why your platform provider should have a history of product innovation, and a future-focused mindset in their partnership with you.

Warning signs that your platform is not innovative are rare product updates, and a platform that is wildly behind the latest technology advancements. An innovative platform will be in line with current technologies, with regular product releases for constant improvement and optimal performance.

What to look for:

• Product roadmaps: First, ask if they have a product roadmap—it can be a big red flag if they don’t. If they do, make sure it contains plans for improvements you’d like to see in the platform.

• New product releases: Have they released any new products lately? While endless new products can get tiresome, new releases mean they’re attempting to stay ahead of the curve.

• Product updates: How often do they update their products? If you don’t see frequent updates, they’re not continuously improving their platform.

We’re the platform that puts it all together.

Consider this your litmus test for R&D data platforms. 

Don’t be fooled by fancy words our competitors use to describe their products. Words like “modern,” “integrated” and “promotes collaboration” don’t mean anything on their own. Anyone can claim those descriptors, but only the best can actually back them up.

Use this as your guide to help you find the right R&D platform for you and your team—the one that is flexible and easy to use, prioritizes collaboration, has a modern and scalable underlying infrastructure, offers the highest level of support and services, and has a demonstrated history of continuous product innovation. This is the platform that will bring you the most value, and set you up for long-term success.

Learn more about Benchling R&D Cloud today.