March 1, 2016

When is a data-driven approach of technology and corporate foresight sensible?

Together with 170 Technology and Innovation experts and decision makers from around the DACH region, our CEO - Peter, and Head of Sales and Consultancy - Luis, discussed the experience in data-drive approaches in Technology and Innovation Management during the Disruptive Technologies and Innovation Foresight Minds Conference 2016 (DTIM) in Berlin last week.

Luis Sperr encouraging DTIM participants to (re-)consider their strategic foresights

“Challenge your peers” session at the round table was definitely fun for our two “mapegies”.

That is why we want to share their key findings of the four most-discussed topics on data-driven approaches in Technology and Innovation Management as follows:

  1. Current status
  3. Vision: in which direction to go
  5. Dealing with disruption
  7. Contra & Pro arguments of Data and Experts


Top innovation players tend to use a lot of scientific data to scout and monitor new business fields, technologies and competitors according to the Economist Intelligence survey of 400 senior executives. Nevertheless, existing data-based tools are often used selectively, unsystematically or are considered as only IP-related by the DTIM participants. Google is the tool mainly used by them. This approach does not seem to be in line with the growing flow of data and its significance nowadays. It results only in higher cost for companies, spent on expensive case studies and consultancy projects. What are the reasons for the unwillingness to accept the IT-bases approaches then?

The Top 4 answers were:

  1. The main challenge is not the technology itself but the corporate culture to accept it.
  3. Most of the users are not familiar with data and IT-based approaches to analyze it. Those missing data science skills further hinder the acceptance of the technology and tools.
  5. There is fear of losing sensitive company-related information.
  7. Many tools are not user-friendly, intuitive and are often perceived as not effective enough.


  1. The data-driven IT-based tools in Technology and Innovation Management should be intuitive tools which give a quick overview and important strategic insights. Those tools need to provide access not only to single R&D-related data documents (such as patents), but also to all available R&D relevant sources (such as news, technical standards, scientific literature, trademarks, patents, etc) which are useful for companies to gain relevant strategic knowledge and recommendations for their future performance.
  3. Innovation should not be a “responsibility” only to a few experts and professionals within the company. An organization can only be “innovative” if it manages to regularly, collaboratively and inclusively integrate every innovator it has within its ecosystem (internal, external, customers, suppliers). Data and IT-based tools can and should encourage this.

MAPEGY CEO, Peter Walde, problematizing the pros and cons of Data and Experts-driven insights.


Purely technological disruption is a rare phenomenon. It makes more sense to call it “a disruptive consequence" from the interaction of technology, people and society which leads to new business models and innovation.

The way to identify disruption goes together with systematically scouting for context changes and asking the right questions (calibrating the tools). Yet, disruption usually comes from new market players not from existing ones. So the old and well-known competitor monitoring techniques are not that useful whereas automated identification of new market entrants or moving existing technologies to a completely new market/industry environment work pretty well with the use of IT data-driven approaches. For example, the entrance of Google or Apple in the automotive industry could have been easily identified as a “weak signal” of disruption ten years ago. More challenging is, however, the identification of new business models which are increasingly changing business fields nowadays. They consist of tacit strategic knowledge which is not published and consequently automated analyses of new business models are very difficult for implementation. In this case, the combination of IT and data-based methods with expert knowledge is the key to success.


Data and Experts: Success can be achieved only through the combination of data-driven approaches and expert knowledge. The so-called "Augmented Intelligence" uses benefits of data and IT-based approaches and derives insights from them to support innovators.

(+) Data-driven approaches are: effective; provide a quick overview and insights; are individual; contextual; offer more transparency and are based on hard facts.

(-) Data-driven approaches do not deliver: action-oriented knowledge, contextual review, the right timing, knowledge, strategy, implicit knowledge, or automated credible strategic foresight.[/vc_column_text][/vc_column][/vc_row]

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