Tech Mining to Drive Open Innovation

First International Conference on Technology Innovation, Risk Management and Supply Chain Management (TIRMSCM), Beijing, Oct., 2007. [keynote]

Alan L. Porter (Search Technology, Inc. and Technology Policy & Assessment Center, Georgia Tech)

Published: 2007


Content

In summary, this paper draws together a lot of concepts and approaches to deal with technological innovation. I hope this is not too confusing. One purpose in gathering so many concepts (though, by no means all of the aspects to innovation) is to draw attention to innovation's multi-dimensional nature. We need to keep these in mind to guide innovation processes effectively, including especially:

  • Technical + contextual forces (and attendant intelligence)
  • Incremental + radical changes
  • Internal + external resources
  • Multiple innovation process stakeholders, with differing intelligence requirements

Tech Mining provides vital intelligence to help manage Open Innovation processes. It helps identify technical thrusts. "Innovation mapping" extend these to suggest promising developmental paths forward. Combined, this strategic intelligence can help managers accelerate all forms of innovation for their organization. 

Open Innovation demands fast, effective competitive technical intelligence (CTI). "Boundary spanners" are individuals who direct attention to gathering intelligence especially from external resources and delivering this in ways that relate to internal considerations. We are developing an Open Innovation Machine to facilitate this.

What is different here?

  • Attention to Open Innovation is moving CTI from a niche to a mainstream position in corporate life, so CTI results must be effectively conveyed to a broader user base.
  • Complete CTI needs to draw upon multiple information resources.
  • CTI must be woven into business decision processes (c.f., Brenner, 2005).
  • CTI analyses must be expedited.

The Center for Innovation Management Studies (CIMS) at North Carolina State University offers a multidimensional innovation orientation [http://cims.ncsu.edu/]. "Ambidextrous companies" are those that attend well to both incremental innovation processes, on the one hand, and radical innovation on the other. The "Technology Delivery System" framework can help plan both incremental and radical innovations. Envisioning new products, and the technological platforms upon which they would stand, is essential.

We distinguish three goal levels in developing our Open Innovation Machine:

A.  6-month
B.  Intermediate
C.  Ultimate

Goal level A - building upon current capabilities, exploit widely used science & technology (S&T) data bases (e.g., Derwent World Patent Index, Web of Science, INSPEC) and deployed text mining software (we use VantagePoint). The novelty is to draw upon a framework of technology management issues, questions, and empirical innovation indicators to program routines to semi-automatically generate results - Tech Mining.

Goal Level C would thoroughly integrate the six types of information (Table 1). This would be digested to provide familiar visual outputs that address recurring questions in Open Innovation.

Goal Level B (in the middle) is, perhaps, most interesting. Over a time frame of 1-3 years, Search Technology aspires to sharply enhance the available intelligence to make OI decisions in both directions - recruiting external emerging technologies and licensing out technologies. Key elements include:

  • Combine S&T and contextual content mined from database searches to yield "greater than the sum" insights
  • Improve agent retrieval and formatting of internet content to enable seamless integration with the database findings.
  • Reach beyond direct relationship text mining to filter indirect relationship measures, enabling "knowledge discovery" that goes beyond information retrieval.
  • Devise processes to enlist human expertise to review, correct, and embellish the empirical knowledge compositions.
  • Pattern the resulting intelligence to fit with growth modeling to provide insight into alternative development paths to populate scenario explorations (future innovation options and implications) (c.f., Robinson and Propp, 2006).
  • Devise visualizations that convey trends and relationships effectively.

 

 

Home  Contact Us          © 2001 - 2018 Search Technology, Inc.  |  Privacy Statement