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Release the Inner Superpower by Liberating your Brightest and Best

Mar 11 2013

Author: Chris Molloy on behalf of IDBS

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Are we missing the obvious and overcomplicating the simple? In our drive towards success, are we squandering what could be our most fearsome competitive edge? I believe that every R&D organisation has an inner superpower waiting to be unleashed – and what’s more, most of them are yet to discover it. Those smart enough to see what is staring everyone in the face have the opportunity to harness their industry’s most constructive weapon - data.
When R&D centric companies create, use and monetise information, across all sectors, from pharma to food, their raw asset – data – has value. It is a capital asset. And when that data is added to, interpreted and shared, it becomes increasingly more complex and valuable. The creation of capital assets requires a complex inter-dependent community of projects, supported by various teams, each providing skills and insight to move products from inception to delivery: an ecosystem of ideas, data and information.

Collaboration within Complex Iterative Processes
What if the data ecosystem’s potential is constantly stifled and undermined by ineffective collaboration, and 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 has been traditionally considered to be a linear progression through multidisciplinary teams chained together to provide; basic research, new product discovery, regulated trials and manufacturing. This heritage concept does not reflect the way that these teams really generate the information asset and, in practice, serves to entrench a siloed mentality, often reinforced by historical management and informatics structures.
In reality, data, information and knowledge are created through complex iterative processes that span research, development, patent filing, manufacture and post-market. It has always been a collaborative data ecosystem and over recent years, it has become an increasingly globalised, multiparty environment. The volume and complexity of the information we share grows exponentially and this has profound meaning for how we use and further exploit both our internal, and the new global, communities to increase R&D productivity.
But today’s data ecosystem is unstable. It is highly fragmented with researchers having to use multiple, often disjointed systems to capture, compute and structure their data. Notable is the prevalence of legacy in-house systems. These represent niches within the ecosystem that are often vestigial: an important workaround from some time in history that is now an impediment.
Maintaining the Flow of Ideas
Leaders in R&D believe that data and processes, however disparate they may be, should be interoperable across the enterprise and they use this as a vision to change their culture. Siloes of activity and of thinking build up through a simple lack of visibility of one another, which breeds mistrust. Once a real-time data connection can be made between them it is possible to start aligning decision-making and process steps. This enables organisational change, process insight and innovation, with unparalleled flexibility. As John Reynders, head of R&D Information at AstraZeneca points out with regards to accelerating R&D, “the role of informatics and information is crucial”[1].
R&D entities undertake constant organisational change to harness and exploit the best minds. Companies have tried grouping by region, by small business units and by discipline. There is probably not a winning model based upon this alone. Organisational structures define barriers. Therefore, as Reynders describes, the critical function of each part of the organisation is to understand how to work across these interfaces and maintain the flow of ideas.
The world’s pharmaceutical giants are not the only ones taking this approach. Industrial Research and Development-to-Manufacturing Organisations (RDMOs) such as BASF, Total, Cargill, L’Oreal, Kemin, Danone, Becton Dickinson and others have recognised its importance for continuous business improvement (CBI) and are rapidly coming to value their data. Their benefits are not just institutional but quantifiable, with Solae (now Dupont) disclosing a saving of 5-8 hours per scientist per week at a meeting in Berlin [2] last year.
High Quality Data is Born by Capturing with Context
Everyone in today’s distributed R&D model can and should be provided with access to a data platform that enables them to share high context secure access to what everyone else is doing and how they are doing it. Gartner Inc recently highlighted the availability of global R&D knowledge management systems that support multidiscipline collaboration [3].
Enterprise class systems can offer high quality data capture, ontology control and security as well as vital contextualisation. Unfortunately, often this vital context and provenance is lost, ignored, or forgotten, dramatically reducing the data’s ability to be compared or used. When this happens community trust in the data is reduced or lost entirely, and given the value of today’s data and IP assets, this is wholly unacceptable.
In an increasingly federated data environment all this context has another important role: only if there are high context, connected stores can federated data be effectively aggregated and assimilated. This aggregation, integral to the new hype term of ‘data discovery’ is vital in creating a high quality information landscape; one that can be made available to relevant decision makers, enterprise analytics and the chosen community at large.
Welcome to Virtual Lab Meetings –
Enabling Conversation
Scientists are naturally social creatures when you put them together. However, if there is a stereotype or characteristic of R&D folks it seems to be that they are very much better at communicating locally and personally rather than between groups or over distances. Long distance relationships need relentless conversations, driven by high context and right-time access to each other’s data. Trying to convince people to communicate by trading documents or scavenging from a drop-box reduces social interaction. Scientific arguments should be peer to peer, not by paper and PowerPoint™, so how do we overcome these barriers? What we need to do is blow open the pigeonhole and create the virtual lab meeting.
What do scientists in lab meetings talk about? Concepts, protocols, process perhaps? All of these, but there is nothing quite like real data to stimulate discussion, debate and innovation; the clash of challenge upon hard fact to generate new thinking. This is all well and good across a coffee in the canteen with everyone bringing in their lab books, but nigh on impossible if your organisation is one of today's highly diversified, externalised and collaboration dependant. Thomas Stallkamp, Director of Baxter and founder of Collaborative Management LLC says, "the secret is to gang up on the problem, rather than each other."  And if scientists are to ‘gang up’ effectively, we need to enable this conversation.
Yet again this is actually all about managing data and information properly. It can be solved in part by software but its mentor is effective business change: breaking down the barriers that today are stopping scientists being scientists; recognising that the dialogue between colleagues is a valuable piece of knowledge as important as the data they are debating.
Social Media Tools for Scientists –
Creating More Valuable Context
We are high tech creatures now: connected and ready for our 4G upgrade. Many of us use social media in our private lives. But are we extending today’s power of managed data into our professional worlds? A recent report from McKinsey [4]  discusses the untapped value from social technologies lying in “improved communications and collaboration within and across enterprises.” This idea of social media as a serious business tool is an important one. Adapting emerging social norms such as tagging, commenting and sharing into the scientific environment requires thought about how these concepts work, applied with closeness and context to the data being shared. The rapid expansion of the electronic notebook environment is the key to introducing social interaction into the data generation and capture environment. Leading R&D information systems like IDBS’ E-WorkBook now allow the secure social tagging of comments, experiments and even the data within them. Telling your co-workers that they should look at the experiment or real-time report, to check out this image, trace or graph is just what you would do around the canteen table. Pointing out that certain work has already been done elsewhere, or that you have confounding data, is also vital to the mix. It enables crowdsourcing of comment and a virtual lab meeting of opinions to be garnered and - most importantly - stored. This collation of interaction is not transactional Twitteresque noise. It is the collective brains of the organisation doing what they are paid to do: adding to the corpus of knowledge.
Time to Release the Superpower
We no longer need to accept the limitations of the past. New thinking and today’s best data technology is now needed in these exciting, challenging times. We must free up our scientists, to enable them to fully collaborate, peer to peer, and do what they do best: discuss, debate and innovate. We must give them the ability and social media tools to create the highest context, highest value distributed datasets to make data reusable. It is now more about the quality of material our Big Data analytics has to work on, rather than the choice of algorithm. It’s about how good you are at closing the ‘data gaps’ rather than dodging them.
Liberating the brightest and best means using the power of managed data to tear down the barriers that artificially divide them. We use foundation principles of context, provenance, curation and connectivity so that the highest quality datasets and collaboration tools can be created. And that really will release the inner R&D superpower in us all.
[1] Bio IT World, John Reynders on the Role of R&D Information at AstraZeneca,
(July 25, 2102).
[2] IDBS Connect, May 2012
[3] Manufacturers Must Consider Scientific Domain Expertise During ELN Selection,
Michael Shanler, Gartner, published January11, 2013.
[4] McKinsey: The Social Economy: Unlocking value and productivity through social technologies (July 2012)

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