IDBS Data Management Blog

Make science social! That’s our mantra as we head to Bio-IT World this week.

People believe that certain individuals are natural communicators: politicians, media moguls, celebrities, but R&D scientists? Science – the search for shared knowledge – is actually all about communication. Given the right environment R&D folk can be naturals. So what’s the best way to open the communication channels and keep conversations flowing?

Most innovation sparks when people come together. This is great across a coffee in the canteen with everyone bringing in their lab books but impossible if your organization is highly diversified, externalized and collaboration dependent. Enabling scientific collaboration in fast-growing, international R&D companies can therefore be a constant challenge.

It’s all about encouraging transparency and ‘right-time’ access to real experimental data. This is key to effective internal, external and multinational collaboration. There is nothing like experimental data to stimulate discussion, debate and innovation: the clash of challenge upon hard fact to generate new thinking.

Scientifically aware, scalable data management systems can also be instrumental in breaking down these barriers. Adapting emerging social norms such as tagging, commenting and sharing into the scientific environment boosts collaboration. Enabling virtual lab meetings also unlocks every company’s biggest asset: the innovation power of their own scientists.

Are you going to Bio-IT World? Don’t miss our own Paul Denny-Gouldson speaking on this topic in the drug discovery track, April 10, 12pm. We’re keen to share your thoughts and experiences on how social media tools can boost collaboration in the lab.

I’ve been talking recently about the idea that every R&D organization has an inner superpower waiting to be unleashed – but most of them are yet to discover it. In fact, many are squandering their most fearsome competitive advantage. What is our industry’s most constructive weapon? Data.

R&D centric companies across all sectors, from pharma to food, create, use and monetize information. Their raw asset – data – has value. It’s a capital asset. And when that data is added to, interpreted and shared, it becomes increasingly more complex and valuable. The creation of data assets requires a complex inter-dependent community of projects, supported by various teams, each providing skills and insight from discovery to delivery: an ecosystem of ideas and information.

Collaboration within Complex Iterative Processes

Too often today’s data ecosystems are suppressed by ineffective collaboration. Something as simple as efficiently moving data from one person to another, and effectively aligning data from internal or external collaborators, is continuously hampered. This is real life for the vast majority of researchers today and this status quo must be challenged and changed.

R&D scientists know that treating R&D as a linear process from basic research to product is flawed. And dangerously so. This heritage concept in no way reflects how teams really generate the information asset and, in practice, serves to entrench a siloed mentality.

In reality, data, information and knowledge are created and shared across complex, iterative processes that span research, development, patent filing, manufacture and post-market analysis. It’s a collaborative data ecosystem and an increasingly globalised, multiparty environment. The volume and complexity of the information has grown exponentially. Accepting this truth and working with it has profound meaning for how we use and further exploit both our internal, and global, communities to increase R&D productivity.

Collaboration is everywhere these days–especially within the pharma industry. But getting it to work well relies on proper planning and co-operation that spans disciplines to allow better use of, and access to, data.

A while ago we were planning a family trip to an adventure park. However, no one was going anywhere until my two boys had tidied their rooms! Faced with this herculean challenge I was impressed to see the planning that went into the collaboration that would achieve the task. My eldest quickly realized that he could reach high shelves more easily than his brother. While my youngest used the fact the he could crawl under the beds faster to great effect. That preparation helped get the job done more efficiently and successfully.

What’s more, they had worked out exactly what they needed to achieve and had evaluated their individual talents to get the job done fast and well. Once done they were more than happy to gauge their success by sharing their results and then receiving feedback. In turn it brought them a reward in the form of a great family day out.

That’s what I call successful collaboration. And dare I say that the life science industry could learn a lot from my two young ones.

True, the days of going it alone are, thankfully, behind us. Organizations have finally learned to play to their strengths and look outside for specialists with the skills they don’t have. The reason is simple–the job gets done faster with costs firmly under control, all of which leads to far better returns for your investment. What we now have to get past is a fear of using and sharing data with ‘the competition’. Keeping data hidden simply hinders collaboration.

The social media model makes it easy to share the lifeblood of collaboration: facts, information and –vitally – ideas. Adopting that approach means that the sheer number of people who can contribute (once the appropriate levels of interaction have been defined and put in place) can result in conversations that would never have normally taken place.

Virtual interaction also speeds up the process. Thanks to electronic laboratory notebooks, for example, paper becomes a thing of the past, as do transcription errors and misinterpretation. IP can be simply protected at the touch of a button. The right application can then select the correct data and analyze it with complete traceability of input from all parties to make reporting simpler than ever. All of which speeds up informed decision-making, leading to innovation.

Now I’m not saying that internal and external collaboration has suddenly become child’s play. It will doubtless require a shift in the collective mind-set of R&D teams. But if you are willing to share your data and analysis openly so that your findings can be assessed, and if the knowledge you have generated can be easily retrieved, true innovations will get to market faster and more cost effectively.

My young lads have already shown how open collaboration can accelerate a positive result up in their bedrooms. Now it’s up to us to follow their lead to see how we can make it work across the world.

…and the odd can of worms!

There can be little doubt that the future for the pharma and R&D industries lies in open collaboration and innovation. And countless establishments are revising their IT strategies to take this into account to benefit from advances.

Innovation takes ideas and turns them into products that people want. Capturing data, turning it into information and sharing it with others in a collaborative network is the life-blood of innovation. Without that process innovation simply won’t happen. Properly managed, scalable and fully secure information systems are a necessary feature of all collaborative environments.

Externalized R&D can take innovation to a new level. Discrete collaborations right up to multi-party collaborations are increasingly looking to pre-competitive and open innovation models.

These changes offer amazing opportunities. Open collaboration offers an almost limitless way to access the best brains in the world. That changes everything. But it can make the structure of many organizations far more complex than before. For multi-party international organizations these collaborations have to be carefully managed.

Who created what?

The key challenge in open and pre-competitive innovation is to know precisely where the intellectual property is generated. This isn’t helped by new challenges in information management – how scientists communicate. They have to establish mutual trust and be sure of the scientific capabilities of all parties.

Large organizations all want efficiency and innovation. But these can be two contrasting goals – efficiency often lacks the flexibility needed to embrace innovation. That’s why open collaboration offers such huge benefits to large R&D companies. It lets them tap into the innovation of small, agile, flexible companies, while keeping efficiency and control of all the intellectual property assets that are generated. To put it simply, it’s a must for R&D organizations that want to grow and keep ahead of the times.

It’s all about data access

Good internal IT makes data and process interoperable across R&D organizations from basic research to manufacturing and QA. But that same approach can be externalized to discrete or multi-party collaborations, contract collaborations and then open collaborations. Ensuring that the flow of data is uninterrupted means that researchers can work with documentation that is easily searched, with the information they need, in real time at the point of use.

I believe that collaboration in all its myriad forms has a bright future providing that all relationships are properly managed on a person-to-person basis.

Only by getting that right will collaboration fully deliver on its promise of revealing new science and exciting new understanding.

Do you agree with Chris? Let us know what you think about the future of Open Collaboration.

This is part III of a series of blogs analyzing the results of our recent R&D Collaboration trends survey. See R&D Informatics – are you ready for 2012? and  R&D Informatics – It’s  all about reducing complexity, improving collaboration & protecting IP for the earlier posts.

R&D INFORMATICS: READINESS CHECKLIST

Based on our analysis of the survey findings and the input of dozens of leading R&D organizations that we work with at IDBS, we’d like to propose the following practical strategies to help you tackle R&D Informatics in 2012.

Step 1: Reduce Complexity:

• Legacy system de-cluttering – can you identify systems you don’t want to interact with anymore?
• Explore where you can use flexible, multi-purpose, multi-domain systems. Take steps to future proof your systems for inevitable business change
• Don’t think that removing a system removes capability, it may enable implementation of better processes
• Don’t accept “we can’t do that, the system won’t let us”

Step 2: Improve Collaboration:

• Think data, not documents – all data (yes, even raw data) should be easily accessible in real time, accessed controlled, searchable and reportable
• Who are your partners in the ecosystem? Consider who are the consumers and contributors of your data. Where do you fit? Can they see your data?
• Maximize online access to data – employees expect to be able to collaborate as easily with their work systems as they do with their social media tools. A reliance on summary level information only allows superficial collaboration
• Don’t use different systems for internal and external collaboration – you’re just creating more silos

Step 3: Maximize Capture of IP & Innovation:

• Explore how IP capture can be automated from existing data capture process – can you make it an inherent element of every stage of research, so that it is not an additional activity?
• Where are the opportunities to harvest IP from your existing processes? Don’t create a new IP capture activity – this simply makes more work for the scientists
• What are the barriers to current IP capture?
• Can you share your innovative processes with others?
• Don’t think it’s someone else’s problem – it is the responsibility of everyone to capture and share their innovations

Discover more by listening to our ondemand webinar.

WHAT’S YOUR PLAN OF ATTACK FOR 2012? 

Let me know your thoughts on these approaches and your own strategies for addressing R&D Informatics in 2012.