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business models, strategies and technologies

From silo thinking to Benchling

R&D results and know-how are worth more than the sum of their parts – how can we manage and implement them more effectively?

Applying scarce research resources better

A lot of money and many grey cells get poured into technical and scientific research and experimentation, worldwide. At the same time, the challenges facing our planet and our society have become so grave and profound that mankind needs to apply a s***load of know-how to tackle these proliferating problems, and to determine whether our future is one of demise or development.

Know-how (= the results of research) and its effective management and implementation can be a key decider and/or enabler.

Upping the visibility and value of R&D implementation

Biotech and pharma are two particular industries that depend heavily on massive volumes of ongoing R&D. Know-how harvested from life sciences R&D is radically transforming our world, but to move at the rapidly increasing pace of scientific and technical discovery and innovation, we also need technology that helps us access and apply the results faster and better.

One company that seems to be adopting such macro-leveraging tools and capabilities specifically for biotech and the life sciences is Benchling. This San Francisco-based shop claims that 200,000 scientists working across Fortune 500 companies, startups and academic institutions now use Benchling to help them get their breakthrough ideas, research results and products to development milestones as well as markets faster and more cost-effectively.

Benchling’s website claim

It seems Benchling uses an open cloud platform to help scientists accelerate the end-to-end R&D life cycle – consisting of data, collaboration and insights – to unlock new discoveries, accelerate innovation and speed up time-to-market as well as many aspects of compliance verification.

Science is changing the world and Benchling is changing science

Effective enablers for biotech R&D

The idea behind Benchling apparently stems from an academic, research-driven realisation that the software technology needed to support the complexity and pace of scientific development – and particularly its increasingly crucial collaborative aspects – wasn’t available. I won’t attempt to delve into the details of this platform’s capabilities – they’re beyond my pay grade and levels of comprehension – but they seem to centre around:

        • Capture, standardise and centralise R&D data by establishing shared ontologies and standardised data formats that make it easier to search for information as well as collate data sets.
        • Manage large-scale, interconnected data types that include experimental data and results, operational procedures and observations/conclusions.
        • Provide risk-minimising data governance tools that manage access and ensure consistent standards – and compliance with these.

The Benchling R&D cloud is pitched as built for scientific work by discovery and development teams, aimed at maximising productivity by making it easy and glitch-free to collaborate using an integrated, extensible platform, whether it involves developing new medicines, crops, foods or materials. “Benchling powers the possibilities of tomorrow”, they boldly and blithely declare.

  • Benchling customers discover and develop breakthrough innovations faster, accelerating workflow cycle time by 38%

  • Structured collaboration

Benchling has been called the “Google Docs for R&D”. Strangely enough, this underlying aspect of the company’s business model is perhaps/probably where the greatest overall importance lies. The whole idea of documents as isolated repositories of information that get dealt with sequentially (= usually emailed back and forth!) has been one of the prime conceptual memes of digital information exchange for something like half a century. It only began to change with things like Google Docs and Dropbox Paper and was then followed by more specialised collaborative information-building platforms like Manifold, Almanac, Coda, Craft, etc. All of these software platforms are really content-neutral enablers for ideas, know-how and information, regardless of form, flavour or discipline.

And once the necessary collaborative tools, APIs and data taxonomy are in place to serve as integration and compatibility pathways, it becomes much easier to factor in the rapidly accelerating AI smarts to achieve a whole lot more with our know-how – faster and with less hassle.

Knowledge management – the enabler capability

To be clear, the nitty-gritty of biotech and the life sciences isn’t a field I’ve had much experience with. But from a wider perspective the really interesting thing about tools and platforms like Benchling lies in their knowledge management capabilities – what they make possible in terms of time to market, avoiding duplication of effort, the cost-effective/planet guardianship use of resources and careful focus in applying hard-won, costly know-how. If know-how has value, such tools can probably amp up that value x 10.

Much scientific and technical development is now data-driven, and therefore requires the ability to manage massively complex statistical/experimental datasets. Benchling et al make it easier to integrate these massive data dollops into connected/accessible and repurposable formats. Even more importantly, they pave the way to de-siloed collaborative processes that can turn inklings and ideas into scientific progress, and improve the lives of people everywhere.

Benchling seems to have become an entire lingua franca framework for development and collaboration in many specific and specialised fields of scientific endeavour – an ecosystem for effective data-driven collaboration. These are capabilities that can help make sure our shared society gets the most out of all the resources poured into R&D a.k.a. new ideas and how best to put these to best use. Such tools can accelerate our technical capabilities as well as how quickly we can put these capabilities to work.