Picture this: Your R&D department is under the gun to create a new line of shampoos and conditioners ahead of the competition. Yet, designing the right formulation requires accessing, integrating and analyzing an enormous amount of data-everything from information about the thousands of possible ingredients available to complex molecular simulations that show how various combinations will perform.
This data is also spread across a disparate array of formats and systems, including databases and Laboratory Information Management Systems (LIMSes), as well as Word documents, e-mails and visual models. Without a structured way to manage it all, the product design process can easily slow to a crawl, delaying time to market (TMM).
Companies selling consumer packaged goods, chemicals, materials and pharmaceuticals count on their R&D teams to develop the breakthrough drug or new house paint that will drive business growth. And, just like every other organizational department today, R&D is turning to IT to improve process efficiencies, slash operational expenses and do more with less.
The good news? Enterprise-class data management systems have basically passed over R&D, so this vital group is now primed to achieve serious data management and business process improvements. The bad news? These benefits will not be achieved by retrofitting standard business intelligence-type tools.
So, how can IT help scientists, production engineers and other key players in the R&D process to leverage the vast wealth of research information available to speed the cycle of innovation?
Four ways IT can speed innovation:
1. Target processes that can be automated
While the most creative and experimental elements of the primary research process are difficult to control, these “more art than science” aspects of product design only constitute roughly 10 percent of the innovation cycle. The remaining 90 percent is taken up by more methodical and routine discovery, engineering and product extension activities-most of which rely heavily on the access, sharing and analysis of research data. Currently, much of this information is managed in an ad hoc or manual manner, which leaves the door open for big efficiency improvements through the smart application of IT.
2. Look beyond one-size-fits-all BI
If you are considering traditional BI, master data management (MDM) or data warehousing tools to solve this problem, think again. These one-size-fits-all solutions were built for transactional data, which is generally structured and numerical in nature. While they excel for the finance, human resources, marketing and customer service departments, these solutions will never suit the complexities of the science and research world. In the R&D arena, chemical and biological data (and experimental results) span all types of structured and unstructured formats-and reside in a diverse number of silos.
For example, formulation recipes may be saved in a Word document on a researcher’s hard drive or in a homegrown database, while spectroscopic data needs to be extracted from another analytical device or it exists only as a printout. In order to fully leverage the vast and disparate quantities of R&D intelligence within their enterprises, organizations require a solution capable of handling highly-complex scientific data, in a variety of formats and across many disciplines.
3. It’s not just what you have, it’s what you do with it
Making sure that scientific and research data is readily accessible (no small task in itself) is only part of the challenge, however. It is also important to consider how information is used among scientists and research engineers, as well as across the enterprise. The power users may range from senior scientists and bench chemists to process engineers. But eventually managers across production, manufacturing, supplier selection and product marketing will also require their own views and interactions with the information. In order to leverage data both effectively and efficiently, an ability to integrate, analyze and generate reports from all types of research intelligence is key.
4. Flexibility is required
The R&D process is focused on discovery and, as a consequence, there is no single way that scientists, chemists, engineers and other enterprise users will be looking at, manipulating and analyzing data. Thus, a flexible approach is required-one that empowers all levels of users to view information in the manner most effective for their needs (which may range from Web portals to sophisticated three-dimensional visualization). In essence, rather than relying on standard templates, users should be able to configure what they want to see and how it is presented. This type of approach leaves room for the innovation so vital to the success of R&D initiatives, but at the same time, provides a framework for faster decision making and, ultimately, faster results and TTM.
Scientists and engineers are the lifeblood of innovation and market leadership at virtually every company. Investing in the right approach to solving their challenge of not merely accessing a very complex array of data, but also collaboratively reusing this information and improving general productivity will pay off in spades.
Frank Brown, PhD, has served as Senior Vice President and Chief Science Officer for Accelrys since October 2006. Frank has extensive experience in the areas of computational chemistry and chemoinformatics. He is responsible for both the scientific direction of the company and all collaborative research with academic, government and industrial partners. Prior to joining Accelrys, Frank held positions of increasing responsibility at Johnson & Johnson, most recently as senior research fellow within the Office of the CIO. In this position, Frank oversaw the development of architecture for all R&D in the organization’s pharma sector. Before Johnson & Johnson, Frank started the first chemoinformatics group in the industry at Glaxo Research Institute, and launched software products targeted to the pharmaceutical industry as vice president for product and business development at Oxford Molecular Group.
Frank has also served as an adjunct associate professor in the Department of Medicinal Chemistry, School of Pharmacy, at the University of North Carolina at Chapel Hill. He has also served as a chair for the American Chemical Society (ACS), Computers in Chemistry section, and on an NIH Special Study. Frank holds a PhD in physical organic chemistry from the University of Pittsburgh and a post-doctoral studies degree in bio physics from the University of California at San Francisco. He can be reached at email@example.com.