Speeding the innovation cycle
Scientific information management is an approach specifically suited to the complexities of the innovation process. It unlocks high-value scientific data to streamline R&D and drive business growth.
Frank K. Brown, PhD -- Manufacturing Business Technology, 7/21/2009 11:27:00 AM
Today's top chemical, materials, and manufacturing companies face a host of competing challenges: Keep up with regulatory requirements, slash expenses, do more with less - all while continuing to deliver innovative products that drive business growth and competitive advantage.
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Time-to-market is of the essence, yet according to a recent Boston Consulting Group/BusinessWeek survey of more than 2,700 business executives, speed was the most commonly identified challenge to innovation. The time it takes to move from idea generation to initial sales was noted as a problem by 45 percent of respondents.
In the race to develop a new fuel additive, industrial adhesive or silicon chip, manufacturing enterprises must find better and faster ways to manage the enormous volume of data critical to product development and early production activities. But how?
Pillars of scientific information management
Unlike conventional "one size fits all" business intelligence, scientific information management is an approach specifically suited to the complexities of the innovation process. Its aim is to unlock high-value data that is traditionally isolated in a variety of disparate formats, systems and applications, as well as across many disciplines, including chemistry, material science, engineering, and nanotechnology. The pillars of scientific information management include:
• Integration. Linking a powerful and diverse range of information management functionalities on a single platform promises to revolutionize product development efficiency. Through the integration of disparate data formats, applications and algorithms from multiple research areas, systems and sources (these may include anything from laboratory notes and infrared X-ray data to two- and three- dimensional images,) organizations can create automated workflows that streamline highly complex experiments, production scale-up processes, and other product development activities.
• Analysis. Beyond accessing and aggregating information, scientists, chemists, process engineers, and other stakeholders also need to be able to transform raw data into the knowledge required to make better decisions, faster. The resulting insight across the organization can have great impact on the quality, safety, and profitability of the final product. Thus, an ability to analyze advanced scientific information related to chemistry, process engineering, materials science and more is critical. An extensive array of statistical methods should be available, ranging from simple statistical indicators to advanced modeling methods. Users also should be able to "drill down" to the information behind high-level analysis when questions or problems require more in-depth investigation.
• Information Delivery. To derive optimal value from their data, participants in the product development process must have the flexibility to choose a delivery method that best suits their needs. In some cases, this may be a Web-based client that enables easy access for non-experts such as business executives, and in other cases, it will be a customizable client or visual model that helps scientists or chemists make specific research decisions. User-centric views of information can also provide both high-level insight into broad organizational status, or deliver targeted intelligence about a particular development project or experiment. Finally, it's important that the complete context of experimental findings be archived for easy retrieval and re-use, so that years of existing research, intellectual property, and embedded "intuition" can be applied to ongoing innovation activities.
Chemical, materials and manufacturing companies need to innovate quickly to stay competitive. At the same time, product development efficiency is challenged by the need to manage huge volumes of complex data, derived from many different sources, and in many different formats.
This requires highly specialized, scientifically aware software solutions.
Such solutions must be able to integrate, analyze, and report on disparate data sources in real time. They must expose knowledge in a controlled fashion to all parts of the enterprise. By streamlining R&D and early production activities, manufacturing enterprises will be better equipped to transform raw data into knowledge, speed innovation, and achieve bottom line results.
Frank K. Brown, PhD served as the senior research fellow the Office of the CIO at Johnson & Johnson. He currently is chief science officer for Accelrys Software, a supplier of business intelligence solutions for mining and analyzing complex data.
For more information:
"Speed Innovation and Reduce R&D Costs with Scientific Information Management," a whitepaper from Accelrys Software
