Tech mining to accelerate radical innovation

Title: Tech mining to accelerate radical innovation
Format: Conference
Publication Date: December 2007
Description: Technology managers' track record in managing incremental innovations is uneven. They confront notably more difficult challenges in managing radical innovation processes due to the greater complexities and uncertainties. S-curve characterizations of the respective incremental and radical innovation processes highlight the critical challenges in generating effective intelligence upon which to base managerial decisions. I distinguish six information resources for "ARTIP" -Accelerated, Radical Technical Intelligence Processes. This paper only treats the first of these - Science, Technology & Innovation (ST&I) information consolidated in publicly accessible databases. Drawing an analogy to geographical mapping, to know where one is going, I suggest we want to map where a particular radical innovation could be heading to help decide how our organization might best participate. "Technology Delivery System" mapping of the key players, resources, barriers, and opportunities helps understand current issues and prospects [4, 8]. New scenario approaches, particularly one called "Multi-Path Mapping" provide a way to forecast alternative development pathways over the next 5-10 years [7]. The key is to recognize various emerging technological capabilities, alternative platforms that could coalesce such capabilities, and alternative application types. Once such mapping has been initiated, one seeks intelligence to enable sound management - ARTIP. Our framework for this is called "Tech Management" [1]. This purports to augment expert opinion based knowledge with empirically derived knowledge. It does so by beginning with the technology management issues, thence specifying particular questions to be answered. Only then do we turn to the information resources to generate "innovation indicators"" that speak to those questions. I overview how software tools (ours is VantagePoint -// are applied to ST&I database search results at three levels. Lists tally the extent of activity. Matrices relate that activity - e.g., to show the leading R&D organizations' relative emphases. Maps help recognize relationships - e.g., which inventors collaborate, to generate "knowledge maps" of an organization's capabilities. These manipulations enable us to answer the basic reporter's questions of "who, what, when, and where?" Results can be composed into "one-pagers" - i.e., carefully selected visual representations of the pertinent innovation indicators to answer a prime question. An organization can determine which representations serve its ongoing business decision processes (e.g., Stage-gate processes) [2]. The last part of the paper illustrates Tech Mining applications in the "nano" arena. These derive largely from work at Georgia Tech to build a substantial project dataset of nano-related articles and patents since 1990. As of early 2007, we have over 1,000,000 paper abstract records and some 60,000 international patent families. Analyses continue [6]. The first cases illustrate broad "research landscaping" [5]. We show different ways to discern patterns across the global R&D enterprise. For ARTIP, such analyses typically pursue a mid-level of detail. Overall "nano trends" make almost no sense as the field is a general purpose technology with diffuse elements [9]. Rather, we need to parse these data to uncover technological thrusts that could lead to emergent capabilities of interest. The three illustrations show: □ Global patenting trends broken out by major topical area □ Global map showing metropolitan patenting concentrations □ Breakouts of nano patenting by objectives: raw materials, intermediates, or final products [1] These results suggest the potentials of analytical probing. For instance, suppose one of the intermediate areas (e.g., a novel catalytic capability) caught the ARTIP analyst's attention. This prompts detailed recovery of related activity to spotlight who (organizations, researchers) is doing what (e.g., alternative formulations, possible adaptations) toward what ends (e.g., application families, industrial sectors). Further discovery pursues second-order potentials (e.g., what problems show parallels that we might stretch this novel capability to resolve?) [3]. The second set of cases illustrates narrow "zoom-in," targeting a particular technical area or a particular organization. This case examines nano research at Purdue University. [This was generated in support of a workshop conducted by North Carolina State colleagues to facilitate tech transfer between academic research and industrial commercialization interests - see Note 2.] We located over 2000 nano publications with Purdue authors. We explored these to help the workshop organizers identify themes and engage key players. The first illustration shows a way to map research networks based on collaboration patterns. VantagePoint can also map researcher networks based on shared topical interests or consolidate topical themes [7]. The second illustration shows a custom profile generated to break out indicators pertinent to the question at hand. Here we see leading Purdue nano researchers, what percentage of their papers were authored in the recent 4 year period, and two measures of their topical emphases. In summary, Tech Mining provides vital intelligence to help manage radical innovation processes. It helps identify technical thrusts. Innovation mapping extend these to suggest promising developmental paths forward. Combined, this strategic intelligence can aid managers accelerate radical innovation for their organization. © 2007 PICMET.
Ivan Allen College Contributors:
Citation: 851 - 867. DOI 10.1109/PICMET.2007.4349402.
Related Departments:
  • School of Public Policy