Improving the Innovation Process by Harnessing the Usage of Content Management Tools Coupled with Visualization Tools

  • Houcine Dammak
  • Mickaël GardoniEmail author
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 540)


Having an intelligent and a creative workplace in which employees, machines and stakeholders are more productive by accessing easily to the information and making better decisions is becoming one of the main areas that organizations are investing in nowadays in order to stay competitive in the market.

Today, organizations have access to multiple information that contains valuable data from internal and external sources. These information are present everywhere in the workplace and represent a huge amount of data. Content Management tools appeared to manage this huge amount of information. It was proved that Big Data has a contribution to creativity, the next step would be to show how Content Management solutions could be used to improve innovation process.

We believe that using Big Data and Content Management tools will make a positive difference in the first steps of the innovation process, that is to say the creativity/ideation part. We will be also coupling Content Management tools and visualization tools to support our approach. Indeed, having a framework that integrates these tools, Big Data, Content Management and visualization tools, in the day to day, should improve the workplace innovation process to make it more “intelligent” and “creative”.


Content management Big data Innovation process Ideation Creativity Knowledge management Graph Visualization 


  1. 1.
  2. 2.
    AIIM: Association for Information and Image Management.
  3. 3.
    Usman, M., Muzaffar, A.W., Rauf, A.: Enterprise content management (ECM): needs, challenges and recommendations. In: 2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009, pp. 283–289. IEEE, August 2009.
  4. 4.
    Alalwan, J.A., Thomas, M.A., Weistroffer, H.R.: Decision support capabilities of enterprise content management systems: an empirical investigation. Decis. Support Syst. 68, 39–48 (2014). Scholar
  5. 5.
    Cooper, R.G., Edgett, S.J.: Generating Breakthrough New Product Ideas: Feeding the Innovation Funnel. Product Development Institute (2009)Google Scholar
  6. 6.
    Von Krogh, G., Nonaka, I., Aben, M.: Making the most of your company’s knowledge: a strategic framework. Long Range Plann. 34(4), 421–439 (2001). Scholar
  7. 7.
    Fleming, L., Szigety, M.: Exploring the tail of creativity: an evolutionary model of breakthrough invention. In: Ecology and Strategy, pp. 335–359. Emerald Group Publishing Limited (2006)Google Scholar
  8. 8.
    Escandon-Quintanilla, M.L.: Effects of data exploration and use of data mining tools to extract knowledge from databases (KDD) in early stages of the Engineering design process (EDP). Doctoral dissertation, École de technologie supérieure (2017)Google Scholar
  9. 9.
    Ackoff, R.L.: From data to wisdom. J. Appl. Syst. Anal. 16(1), 3–9 (1989)Google Scholar
  10. 10.
    Manyika, J., et al.: Big data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey, New York (2011)Google Scholar
  11. 11.
    Soliman, F., Youssef, M.: The role of critical information in enterprise knowledge management. Ind. Manag. Data Syst. 103(7), 484–490 (2003)CrossRefGoogle Scholar
  12. 12.
    Jacobs, A.: The pathologies of big data. Commun. ACM 52(8), 36–44 (2009)CrossRefGoogle Scholar
  13. 13.
    Polanyi, M.: Personal Knowledge: Toward a Post-critical Philosophy. University of Chicago, Chicago (1958)Google Scholar
  14. 14.
    Nonaka, I.: The Knowledge-Creating Company Harvard Business Review, November–December, pp. 96–104 (1991)Google Scholar
  15. 15.
    Kabir, N., Carayannis, E.: Big data, tacit knowledge and organizational competitiveness. In: Proceedings of the 10th International Conference on Intellectual Capital, Knowledge Management and Organisational Learning, ICICKM, p. 220, January 2013Google Scholar
  16. 16.
    Nonaka, I.: Chishiki-Souzou no Keiei (A Theory of Organizational Knowledge Creation). Nihon Keizai Shimbun-sha, Tokyo (1990). (in Japanese)Google Scholar
  17. 17.
    Nonaka, I., Byosiere, P., Borucki, C.C., Konno, N.: Organizational knowledge creation theory: a first comprehensive test. Int. Bus. Rev. 3(4), 337–351 (1994)CrossRefGoogle Scholar
  18. 18.
    Provost, F., Fawcett, T.: Data science and its relationship to big data and data-driven decision making. Big Data 1(1), 51–59 (2013)CrossRefGoogle Scholar
  19. 19.
    Chen, Y., Argentinis, J.E., Weber, G.: IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clin. Ther. 38(4), 688–701 (2016)CrossRefGoogle Scholar
  20. 20.
    Smith, H.A., McKeen, J.D.: Developments in practice VIII: enterprise content management. Commun. Assoc. Inf. Syst. 11(1), 41 (2003)Google Scholar
  21. 21.
    Vom Brocke, J., Seidel, S., Simons, A.: Bridging the gap between enterprise content management and creativity: a research framework. In: 2010 43rd Hawaii International Conference on System Sciences (HICSS), pp. 1–10. IEEE, January 2010Google Scholar
  22. 22.
    Gregory, R.L.: L’oeil et le cerveau: la psychologie de la vision. De Boeck Supérieur (2000)Google Scholar
  23. 23.
    Dkhil, A.: Identification systématique de structures visuelles de flux physique de production. Doctoral dissertation, Strasbourg (2011)Google Scholar
  24. 24.
    Card, S.K., Mackinlay, J.D., Shneiderman, B. (eds.): Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann, London (1999)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.École de technologie SupérieureMontrealCanada
  2. 2.Institut National des Sciences AppliquéesStrasbourgFrance

Personalised recommendations