by Michael Doyle, Ph.D.
To what degree will polysaccharides derived from the ocean improve skin protection? Which active ingredients are most effective as an antiperspirant? Are functional polymers in o/w emulsions better than traditional invert emulsions for ethnic hair care products? The truth is, maybe none of it matters.
That is, none of it matters unless it has direct impact on consumers.
Smart innovation is not about simply churning out “new and improved” products faster and more cheaply than the next guy, or enhancing everything and anything ranging from acidity and pH balance to formulation distribution. It’s about concentrating research efforts on the key attributes and features that will differentiate a product in a person’s mind and entice him or her to buy. And connecting or tying research to purchase motivation requires more than just technical superiority such as having the most advanced lab equipment, the most sophisticated analytics or the most extensive database. Developing winning products requires a shift away from individual “centers of excellence” and toward a more collaborative, inter-disciplinary approach to scientific informatics—one that focuses innovation not on every area, but on the right areas, whether this involves creating more environmentally-friendly hair dyes, or skin creams that include active anti-aging ingredients or cosmetic products that have sun and skin protection agents. Following are three golden rules for achieving the Holy Grail of consumer relevance.
Reward Collaborative Excellence. Bringing a new product to market requires a lot of domain-specific knowledge and expertise, as well as cross-disciplinary interaction. Market development teams investigate what ethnic groups, age brackets, income levels and geographic regions are good or fruitful targets and create models that examine primary purchase motivators. Chemists focus on discovering new compounds and materials that can be used in formulations. Biologists consider how the body’s systems will react with a new formula, screening for factors such as degree of potency or toxicity. There are also environmental and regulatory or product safety experts needed—the list goes on. While excellence in each of these areas is certainly valuable, it is equally important for organizations to reward collaborative excellence, i.e., the ease with which all research, development and testing stakeholders work together to achieve the common goal. If, for example, market studies show tactile performance of shampoos trumps visual aspects such as color, all of the domain experts should have that same objective in mind, whether they are investigating formulation compounds, studying skin cell cultures or carrying out toxicology tests. This requires highly streamlined information sharing and data integration, which is where new advances in scientific information management have an important role to play.
Tear Down Those Walls. In the research world, domain experts have historically operated within their own silos, using their own processes and systems, and speaking their own language. Smart innovation requires these diverse teams tear down the technical, process and semantic walls that separate them. Traditionally, this has been tricky to achieve, as data from a single research team, much less an inter-disciplinary group, is often spread across a diverse array of formats and proprietary systems, such as text documents saved in an electronic lab notebook, images generated by a microscope or information stored in a toxicology database. Also, scientists are trained to investigate problems in depth and this leads to a sense of information or knowledge ownership. But with the rise of service-oriented architecture and Web 2.0 technologies, a cultural change is beginning. A Web services-based IT foundation for scientific information management can facilitate the exploration, exploitation and integration of data across many disciplines, so that stakeholders can access and aggregate both structured and unstructured data from multiple research areas, conduct advanced scientific analysis and more effectively collaborate. Architectural flexibility is key to making this work, so organizations can support both present and future systems and processes with minimal tweaking of the underlying IT infrastructure required.
Automate. Process automation is the third critical step in driving smart innovation. Without it, the cost of achieving inter-disciplinary collaboration will be too high, and solutions will stay as point and not cross functional capabilities. Although some of the more creative elements of product development are difficult to control, the reality is that much of the work involves routine, mundane and repetitive discovery, data management and analysis tasks. Automating these activities greatly improves research efficiency, and frees project teams to focus on cooperation (and discovering those relevant product attributes) rather than administration.
Looking towards the future, the line between cosmeceuticals and pharmaceuticals will continue to blur, and the quest for relevance will involve the use of more active ingredients that fall under regulatory scrutiny. Smart innovation practices that focus on collaboration, integrated scientific informatics and automation will not only drive winning product discoveries today, but also ensure that organizations are better prepared to successfully navigate an even more complex research environment tomorrow.
Michael Doyle is Principal Application Scientist at Accelrys (Accelrys.com), a leading provider of scientific business intelligence software and services for the life sciences, energy, chemicals, aerospace and consumer products industries.