Knowledge Management In Scientific and Technical Settings

Knowledge Management In Scientific and Technical Settings


Knowledge Management In Scientific and Technical Settings

Four of the five partners—3M, Boeing Rocketdyne, NASA and JPL, and Millennium—produce highly technical products based on scientific and technical disciplines. (The World Bank’s advice and decisions to lend are certainly influenced by technical issues, but sociopolitical and economic constraints are at least as strong.) Does effective KM face any distinctive issues in scientific and technical settings?

The answer is “yes.” The distinctions appear to arise from two sources: the nature of the knowledge itself and the nature of the professions doing the knowledge work.

The Nature of the Knowledge and Innovation Being Managed

First, with the exception of the World Bank, the partners must create breakthroughs and designs within the constraints of the laws of science: physics, chemistry, biology, genetics, and material science. Designs must conform to a century or more of research and practice in thermodynamics, aerodynamics, protein science, and strength of materials, for example. Communities of practice and design teams may debate how these laws will manifest themselves in unexpected or extreme settings such as deep space or hidden in the mitochondria of a cell in a sick patient. They may devise clever methods and machines to manipulate and circumvent these laws. But at the end of the day, what will work is not a matter of opinion.

Second, the pace of new knowledge from experimentation, inside and outside the partners, is expanding at a breathtaking rate, which forces practitioners to narrow their field of work. It also leads to a strong dependency on information management to update and deliver targeted content to users.

Third, this knowledge is being used in situations where the stakes are high: loss of money and time invested, the potential risk to human life and well being, the loss of public confidence and funding, and the potential loss of market share and competitive advantage. A mistake on a Mars mission can cost billions; the opportunity cost of not being the first to bring a drug to market can cost $800 million dollars and 15 years of research; and the grief from a passenger jet crash is incalculable. The study partners feel they are playing in a very high-risk game; the knowledge they use or reuse must be of the highest quality, relevance, and trustworthiness. Some of the study partners have encountered risk aversion because the consequences are so high. This leads to a fear of reusing someone else’s solution that might not be successful in the current circumstances.

The Nature of the Professionals Doing the Work

The second major influence of these settings on KM appears to arise from the professionals doing the work. Many of the staff working at the study partners joined because they wanted to do novel work, excel in their disciplines, or make a unique contribution to the mission. This leads to dilemmas for designing KM approaches that value sharing and reuse.

  • There is a strong bias for invention and against reuse. How does one design a collaborative KM-sharing culture and processes (and gain efficiency in innovation) if everyone wants to be a hero, get their bust in the Smithsonian Air and Space Museum, or win a Nobel Prize for breakthrough science? Innovation in these settings requires teamwork and cross-disciplinary information and knowledge sharing because of the complexity of the systems being modified and designed or, in the cases of Millennium and the World Bank, the number of perspectives and stakeholders that are engaged in the creation and implementation.

  • The nature of the professions highlights another dilemma. On the one hand, scientists and engineers see the value of knowledge sharing. Their disciplines reward publishing and speaking. On the other hand, intellectual property issues and professional jealousy can cause experts to hide their knowledge from suppliers and alliance partners who might get the jump on them in the marketplace or peers who might publish first.

  • Because the professions and the organizations revere heroes and winners, many people fear admitting mistakes or failures, even though the value of that information to the organization would be high.

  • The knowledge domains and experience base used in design can be so specialized that the loss of key people is even more devastating than in other settings.

Some of the consequences of, and responses to, these two influences on KM in scientific and technical settings will be first be discussed in Chapter 2 on context and culture, but their influence is so pervasive it will be seen throughout the report. A brief preview of some of the consequences for KM follows.

  • There is a heavy emphasis on content management systems and information management for the study partners, including the World Bank. Content management systems refer to the people, processes, and technology required to manage the huge amounts of content and information in all of our partners. There is also more reliance on databases and repositories and the processes to manage and access them than previously observed in KM research conducted by APQC.

  • Organizations must have processes and people to scan and synthesize internal and external databases and other kinds of knowledge into meaningful “packages” for delivery to experts and project teams.

  • Facilitating effective cross-disciplinary teams is essential. KM and innovation requires that people learn how to engage constructively with multiple subject matter experts, both academics and practitioners, in their teams or as mentors and advisers to the innovation process. Each study partner has a robust mechanism for expertise location.

  • Validation of the accuracy of the content is critical because the stakes, both financial and human, are so high. Library scientists play a much larger role in the study partner’s KM efforts, both serving on the steering committees and being integral players in delivering content.

  • There is a tremendous need to create relationships—social capital—across projects, disciplines, time, and geography.

These issues influence the KM strategies and approaches used to support innovation. To give a structure around which to discuss all the issues and approaches, the APQC study team developed a model of knowledge-enabled innovation.



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