Enterprise-class data management systems have passed over R&D, making this vital department primed for serious data management and business process improvements. But these benefits will not be achieved by retrofitting standard business intelligence tools. Through the smart application of IT, Knowledge Center contributor Frank Brown illustrates how scientists and production engineers in the R&D process can leverage the vast wealth of research information available to speed the innovation cycle.
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
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
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
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.