Abstract
Leaders in Research and Development-to-Manufacturing Organizations (RDMOs) ensure that the right people are in the right roles and have the support they need to work together. Beyond that, the quality of management depends on the quality of its information and its ability to act on it. Thomas Goetz reminded us that “We can profoundly change our behavior once we are provided with the relevant data.” 1
RDMOs are highly multidisciplinary. They require that people with uncommon and differing skills collaborate to turn ideas into innovations. They expect that some groups work the blue sky and do a different thing each day. They also demand that others do the same thing every day and maintain the regulatory compliance for product safety and reliability. But, as Chris Viehbacher, Sanofi's Chief Executive Officer (CEO), said, “Major groups are not great sources of innovation.” 2 Greater agility should be a key goal, particularly for larger organizations; they need to become more agile, more ideas-to-actions driven, more orchestrated, more Apollo 13.
In our current communications-driven world, the ability to collaborate across time zones and nations has made RDMOs eager to access external talent and resources that were, until very recently, kept firmly behind high walls. Whether characterized as externalization, globalization, virtualization, open collaboration, or open innovation, the concept is similar. Collaboration is a recognition that even the largest companies should focus on what they do best. Chris Thoen, MD, Director of Innovation and Knowledge Management at Procter & Gamble (Cincinnati, OH), captured this concept when he said, “Only do what only you can do.” 3 But what is the product of all this local and distributed effort? What context needs to be communicated between players? The answer is both simple and complex: data.
Data as a Capital Asset
Data is a capital asset of RDMOs, as the companies they create feed on and ultimately monetize information. Even raw assets have value and as they increase and are interpreted and shared they become increasingly complex and valuable. This is an example of a classic food chain. The action that follows from this understanding, helped along by the need for creative disruption of the R&D model, is a stepwise effort from the ground up to secure and exploit those assets.
Traditionally, the RDMO process has followed a linear progression through a multidisciplinary set of teams chained together to provide basic research, new product discovery, regulated trials, and manufacturing. This concept, popular for more than 30 years, does not reflect the way that these teams really collaborate. In fact, it serves to entrench a siloed mentality that is often reinforced by separate historical management and informatics structures. In reality, the process is a complex, interdependent community of projects, supported by various teams that each provide skills and guidance to move products from inception to delivery: an ecosystem of ideas, data, and information.
Orchestrating Your Organization
Gathering together the brightest and most diligent minds in every sector of an RDMO—even a highly distributed one—is vital. But how do you ensure that you are getting the most that each player has to offer while keeping the flow of projects and innovation smooth and coherent? Here, the model of a symphony orchestra can be helpful. Consider high context, multidisciplinary organization designed for innovation with high pressure and time critical activities. What can we learn from a few hundred years of music-making? Everyone is on the same platform, can hear each other, and can see the conductor. It does not matter that each player can only see the notes they need to play because they can get the context they need from their colleagues in real time—by actively listening—and can respond accordingly.
A recent survey carried out by IDBS (Guildford, UK) and Scientific Computing (Rockaway, NJ) reviewed the ability of 682 researchers to work within that data ecosystem. The results showed that researchers wish to, but fail to, collaborate effectively. In many cases this is simply because they cannot efficiently move data from one person to another. The survey showed that 91% of researchers could not effectively align data from internal or external collaborators. The data ecosystem is not a stable platform; it is highly fragmented with researchers having to use multiple, often disjointed systems to capture, compute, and structure their information. The prevalence of legacy in-house systems is notable. They represent niches within the ecosystem that are often vestigial, and the important workaround from some time in history is now an impediment.
The research showed that most scientific data in collaborative networks are still shared using documented reports or rudimentary, summarized data. Trading documents instead of trading data reduces the effectiveness of communication, abolishes the ability for real-time decision-making, and often airbrushes out vital context needed by the consumer of the data. This is like part of a symphony orchestra trying to play in New York with the string section joining in via a bad phone line from Sydney, Australia.
Whereas it would be unrealistic to expect to put everyone in an RDMO together as in an orchestral arena or in Mission Control, it is both realistic and feasible to provide them with a platform of data that enables them to have real-time, high context, secure access to what everyone else is doing. Making the data and process interoperable and consumable by those who need it is, therefore, not just a headache for Chief Information Officers, but rather a strategic necessity for CEOs. This starts from the ground up; at the inception of ideas and the capture of data and process. Furthermore, it will not be solved simply by the use or hype of Big Data.
Big Data, Big Questions
Big Data analytics, which at present apply only to very specific high data niches within R&D, do offer future promise for RDMOs, as in the analysis and use of cross-enterprise information. If tapped and analyzed it can contribute to new insights and better decision-making. However, there are barriers that need to be overcome. One is the ability to access and analyze highly distributed data where it is generated, rather than creating ‘Death Star’ mega-warehouses. A more important, but rarely discussed, issue with Big Data is data quality. Even the best algorithms and expert systems are only as strong as the data they have to work on. Trying to solve underlying data quality issues with Big Data analytics is analogous to trying to run a Ferrari engine on crude oil.
Data quality starts with the capture of raw data with context and travels up the food chain through to computed and interpreted information. This is key, particularly in an increasingly distributed, collaborative environment. It is important to remember that the Apollo 13 disaster started with a tiny sensor failure, the lowest form of data. It went unnoticed because of a lack of context and allowed a disastrous decision to be made 200,000 miles away. The concept of “never mind the quality, feel the width” for data cannot fix that disconnect and does not apply to RDMOs.
If we are to benefit from the future of high velocity, intelligent algorithms that can provide valuable insights, then the data, context, and process used have to be captured and curated correctly with quality control at the earliest point. Systems such as IDBS' E-WorkBook provide RDMOs with a multidisciplinary data management platform and modular R&D notebook that bridge research, development, and manufacturing. Getting the data “right from the start” is the foundation of this data architecture. With that in place, business-to-business collaboration and data consumption become easier, and the corporate data asset is instantly more valuable.
Data as a Tool of Organizational Change
Leaders of enterprising RDMOs know that data and processes should be interoperable across an RDMO, and they use this as a vision to change their culture. Siloes of activity and of thinking often build up through a simple lack of visibility of one another, which breeds mistrust. Too often these siloes are reinforced by organizational hierarchies. Once a real-time data connection is made between groups, it can start to align decision-making and process steps, enabling organizational change. It also allows a degree of experimentation with innovation, similar to the introduction of cellular manufacturing systems adopted in the 1990s and 2000s.
The world's pharmaceutical giants are not the only ones taking this approach. Industrial RDMOs such as BASF (Ludwigshafen, Germany), Total (Courbevoie, France), Cargill (Minnetonka, MN), L'Oréal (Paris, France), Kemin (Des Moines, IA), Danone (Paris, France), Becton Dickinson (Franklin Lakes, NJ), and others have recognized its importance for continuous business improvement and are rapidly coming to value their data. The benefits are not just institutional but quantifiable, with Solae (St. Louis, MO), for example, recently disclosing savings of five to eight hours per scientist per week. 4
IP Capture in a Changing Patent Environment
Data, and the information it generates, are the bedrock of the intellectual property (IP) of any RDMO, but the ground is shifting here too. First, IP emphasis is turning away from pure ownership of the discovery and toward its use. In addition, what represents valuable IP is also changing, to the point where methods of analysis, manufacture, markers, and measurement are now equally as important as the discovery. Finally, as the America Invents Act became law, the concept of First to Invent is being superseded by the First to File principle.
What does all this mean? It means that rapid access to enterprise data and the context in which it was generated is now more important than ever. It requires RDMOs to ensure that their data reside in places where they can be accessed, quickly integrated, and filed before their competitors can do so, irrespective of the date when the work was done.
Data Management Decisions Drive Competitor Advantage
None of this theory is really new. The Apollo team had the world's best technology but still relied on making sense of a stream of data, raw innovation, and informed collaboration. In the end they saved lives—and the space program–using data, brain power, bravery, and duct tape. For RDMOs, the ability to harness the best and brightest brainpower relies on bridging the interfaces with data and building trust, so that when the pressure is on, those teams will have a strong belief in one another's skills. The solution to having people work effectively together is to give them a solid foundation for their data and process and, from there, let them configure how they can best work together. This strategy will save time and deliver on continuous business improvement (CBI).
Licensing enterprise software is more important to short-term productivity and long-term innovation than licensing that next big technology before your competitor does. Consider carefully whether your organization could handle that call from 207,000 miles away: “Houston, we've had a problem.” If it could, it would be among the leading pack of innovators and not just running enterprise R&D but rather enterprising R&D. If not, your data and your innovators could take you there, if you give them the adequate platform.