The SciBite difference – It’s all about your data

In this blog post SciBite's CSO and Founder Lee Harland addresses a very common question we are often asked by potential customers and partners...

What makes SciBite different

I wanted to take a little time out to address a very common question we are asked by potential customers and partners, namely “I’ve just seen a press release on *company X* – how is SciBite different?”

It seems every day we see another new company come into the market set to “transform the way we use information thanks to the latest AI technology”. As we were once a start-up ourselves, we know how time consuming it is for customers to understand an increasingly crowded marketplace. However, when we look across the many different offerings in our space, there have four key principles here at SciBite that we believe set us apart from the rest…

1. First and foremost, it’s about your data

While tools, technologies and priorities come and go, an organisations data represents years of investment and intellectual effort. In the last few years most companies have elevated good data management from “should” to “must”. Inaccessible or poor-quality data is limiting the adoption of advanced analytics within life sciences, and also has very real financial consequences for research driven companies.

The primary focus of SciBite’s technology is to enrich your data, be it public literature, internal documents, ELN entries and many more. Our aim is to add value to your data, no matter what the future applications of that data might be. Systems which just consume data “as-is” without critiquing or remediating issues in that data are prone to generating meaningless results. At SciBite we’re passionate about #CleanData and are championing it as one of the most important initiatives any company should consider, and providing the tools to make it a reality.

2. An infrastructure not a product

There are many products on the market aimed at one very specific use-case, be it pharmacovigilance, literature alerts, text-mining, document management, target identification and so on. While these work well for that task, they do not support situations where scientists wish to replicate user experiences in other systems within the organisation or join data across them. Over time this leads to multiple point solutions, each speaking a different language and requiring yet more technology investment to join them together.

As a technology designed from the ground-up to be applied more broadly, the SciBite platform can be applied to dozens of diverse use-cases. Our systems all follow an “API-first” philosophy designed for integration into multiple downstream tools, to enable them to understand scientific content for better search and analysis. Often, we find that a third-party tool perhaps initially considered a SciBite competitor, becomes something we actually synergise with – exemplified by our growing partner ecosystem. The knowledge that the systems of today and the future can all communicate using a common language is something our customers consistently highlight as a key differentiator for SciBite.

3. Standards to unlock Machine Learning

SciBite is a machine learning company – we’ve been investing and developing these types of technology for over 8 years now. We’re a strong proponent of AI technologies such as machine learning and deep learning and have published studies we’ve done with companies such as Pfizer in this space. Yet we maintain a healthy scepticism of the continued hype in our sector, often claiming an “Industry leading AI” is all you need to “transform your data into actionable intelligence”. We strongly believe that without community driven data standards, machine learning can’t reach its full potential, something backed up by multiple studies.

Let’s consider the new ACME AI Target-Finder tool. It does some fancy AI and identifies this thing called “COX 1” is a potentially interesting new target for your disease of interest. Its here things get tricky. By COX 1, do we mean Cytochrome C Oxidase 1, after all “COX1” is its official gene identifier.  But wait, most papers in the literature that talk about COX1 actually refer to Prostaglandin G/H Synthase 1, an entirely different gene linked to very different of biology. Let’s say we have existing company data on both genes too. How can the AI make decisions on how to interpret this data unless we know there’s a common language that allows us to connect this data with high confidence? This is what data standards, such as ontologies, minimum information standards and FAIR bring to the table. Read our use case on more than FAIR: Unlocking the value of your bioassay data.

SciBite’s platform is built using these fundamental resources from the ground up. We don’t just recognise the key concepts in scientific data, but link them directly to these standards, helping describe, interpret and connect information. We invest in these standards too. Our “VOCabs” represent years of expert-driven enrichment of critical public ontologies in the research and clinical space and are unrivalled for depth and quality. Thus, unlike many other machine learning driven systems our unique philosophy of “standards driven machine learning” harnesses the synergistic power of both technologies to offer an unprecedented toolkit for scientific discovery. Watch our Webinar on scaling the data mountain with Ontologies, Deep Learning & FAIR.

4. Designed by scientists for scientists

It may sound like a cliché, but we really believe our team are a vital part of our offering. All of our leaders have extensive experience in the life sciences industry. For us, it’s not just “another vertical” but our sole focus and one where experience matters. All of our technical leaders have strong scientific backgrounds serving both industry and academia, many have actually been part of the bodies designing universal data standards or spent years working on industry-relevant use cases.

On the business side we have leaders with a long history supporting customers in this sector with a strong knowledge of the market. All of this means we understand the challenges facing our customers in this domain. We read the same papers, we go to the same conferences, we get excited about understanding the latest trends and fundamentally we view SciBite as an R&D-led organisation, just like many of our customers.

However, it’s our customer engagement philosophy we feel most strongly about. We’re not looking to simply sell some shrink-wrapped software then move on. By its very nature, a technology as flexible as ours means that there’s always a new use-case identified by our customers. We’re excited to work together with our users to work out how to make things fit, how to turn these new ideas into reality. We love to push the boundaries, engage in discussions, brainstorm solutions and explore what could be done in collaboration. Much of the work we have undertaken with our customers has helped to shape the technology we deliver today.

So, what makes SciBite different in the market?

When evaluating any software or solution there are a lot of things to consider, and we’re just one of many companies out there. We’re not right for every use-case, and when we don’t think we’re a good fit, we’ll say so (and even recommend someone if we can!).

However, for the majority of the world’s top pharmaceutical companies, SciBite has become a foundational layer of technology, proven to deliver against multiple-use cases and  helping to drive real value from data. SciBite is award-winning and our flexible, standards-driven architecture helps your organisation understand the language of science.

Don’t just take our word for it, watch our recent customer webinars to learn more:

GSK Webinar – Powering question-driven problem solving with Semantic integration
Hear how semantic integration can be made to ultimately become part of an integrated learning framework for more informed scientific decision making.

Watch webinar

Chr. Hansen Webinar – Unleashing knowledge through SciBite technology
Hear how they successfully using SciBite’s technology to enable an established system (eg. Electronic Lab Notebooks) to empower scientists with semantic search.

Watch webinar

Get in touch with the team today to find out how we can help you get more from your data.

Get in touch

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