Forecasting Innovation Pathways of Big Data & Analytics

PROJECT OVERVIEW

This project will provide a case study to improve five analytical processes, identified as vital to improve the methodology of forecasting innovation pathways. The case to be analyzed is “big data & analytics,” a topic of great national importance. Figuring out how to gain advantage from large data sets will impact national scientific progress, industrial productivity, and defense. At the same time, big data poses issues of privacy and security, among others. The topic of big data was selected because it is under study by the U.S. Government Accountability Office (GAO). We are sharing information on methods and findings with GAO to gain insight into ways to make our methodology more useful.

This research project has two main elements – 1) ‘tech mining’ (empirical analyses of literature and patents to discern R&D trends and active players) and 2) engagement of stakeholders and experts to help understand developmental prospects and implications.  To date, we have emphasized the first — empirical analyses of Big Data Analytics.  Visiting scholars Ying Huang and Yi Zhang from China, and Jannik Schuehle from Germany, have contributed greatly to bibliometric and text analyses of Big Data related activity.  This draws on search and retrieval of abstract records from multiple databases: Web of Science, INSPEC, ABI Inform, NSF and NSFC (National Natural Science Foundation of China) awards, and Derwent Innovation Index patents. These analyses show amazing growth in R&D and broader attention to Big Data since 2008.  They identify prominence of the U.S. and China in advancing Big Data related R&D globally.  Mapping Big Data related articles indexed in Web of Science shows remarkable dispersal of interest in using such resources to advance research in many fields [view Map].

Currently we are pursuing the second element.  We seek to engage various experts and interested parties to review and enhance our efforts to identify diverse Big Data Analytics applications and their potential benefits, costs and risks. For more information see: http://bigdatagt.org/

INVESTIGATORS

Alan Porter (PI), Search Technologies; Jan Youtie (Co-PI), Georgia Institute of Technology

SPONSOR

NSF, Science of Science Policy, Award #1527370 

Journal Papers

  1. Zhang, Y., Zhang, G., Chen, H., Porter, A. L., Zhu, D., and Lu, J. (2016). Topic analysis and forecasting for science, technology and innovation: Methodology and a case study focusing on big data research, Technological Forecasting and Social Change, 105, 179-191. doi:1016/j.techfore.2016.01.015. TC=5
  2. Zhang, Y., Robinson, D., Porter, A. L., Zhu, D., Zhang, G., Lu, J. (2016). Technology roadmapping for competitive technical intelligence, Technological Forecasting and Social Change, 110, 175-186. doi:1016/j.techfore.2015.11.029. TC=1
  3. Huang, Y., Schuehle, J., Porter, A. L., & Youtie, J. (2015). A systematic method to create search strategies for emerging technologies based on the web of science: illustrated for ‘Big Data’. Scientometrics, 105(3), 2005-2022. DOI 10.1007/s11192-015-1638-y. TC=5
  4. Youtie, J., Porter, A.L., & Huang, Y. (2017). Early social science research about Big Data. Science and Public Policy, 44(1), 65-74. doi:1093/scipol/scw021. TC=0
  5. Huang, Y., Zhang, Y., Youtie, J., Porter A.L., and Wang, X. (2016), How does national scientific funding support emerging interdisciplinary research: A comparison study of Big Data research in the US and China, PLoS One 11 (5): doi:10.1371/journal.pone.0154509. TC=0
  6. Huang, Y., Zhu, D., Lv, Q., Porter, A.L., Robinson, D.K.R., & Wang, X. (2017). Early insights on the Emerging Sources Citation Index (ESCI): An overlay map-based bibliometric study. Scientometrics. doi:1007/s11192-017-2349-3
  7. Huang, Y., Porter, A.L., CunninghamW., Robinson D. K. R., Liu, J., & Zhu, D. (under revision), A Technology Delivery System model for characterising the supply side of technology emergence: Illustrated for Big Data & Analytics, Technological Forecasting and Social Change.
  8. Liu, J., Guo, Y., Huang, Y., Porter, A.L., & Robinson, D.K.R. (under revision), Technology assessment for ‘Big Data Analytics’: A systematic approach, Technology Analysis and Strategic Management.

Presentation & Conference Papers

  1. Porter, A.L., Huang, Y., Schuehle, J., & Youtie, J. (2015). Meta data: Big Data research evolving across disciplines, players, and topics. 2015 IEEE International Congress on Big Data, New York, NY, 262-267. doi:1109/BigDataCongress.2015.44.
  2. Huang Y, Zhang Y, Porter, A.L., Jan, Y., Luciano, K., & Zhu D. Funding Proposal Overlap Mapping: A Tool for Science and Technology Management. Atlanta: 5th Global TechMining Conference, 2015.
  3. Porter, A.L., Robinson. D.K., and Huang, Y. (2015) Tech Mining for “FIP 2.0” – The case of ‘Big Data’ – 5th Annual Global Tech Mining Conference, Atlanta (September). [3-hour workshop exploring advanced Forecasting Innovation Pathway methods].
  4. Porter, A.L., (2016), Forecasting Innovation Pathways: The case of big data, Portland International Conference on Management of Engineering and Technology (PICMET), Honolulu, HI (September).
  5. Porter, A.L., and Huang, Y. (2016), Forecasting future innovation pathways with big data analytics, CIMS Innovation Management Report, 8-13 (July/August), Poole College of Management, NC State University, Raleigh. [This article seeks to forecast innovation pathways for “Big Data.” It introduces the VantagePoint Emergence Indicator script to help identify emerging topics within Big Data.]
  6. Liu, J., Guo, Y., Porter, A.L., & Huang, Y. (2016). A systematic method for technology assessment: illustrated for ‘Big Data’. 2016 Proceedings of PICMET ’16: Technology Management for Social Innovation, 2762-2769. doi:1109/PICMET.2016.7806836.   [This paper presents a systematic method for technology assessment as a part of the suite of tools for forecasting innovation pathways (FIP). We explore means to combine tech mining tools with human intelligence in several idea exchange rounds to uncover potential secondary effects, and array them in terms of likelihood and magnitude.]
  7. Huang, Y., Youtie, J., Porter, A.L., Robinson, D. K.R., Cunningham, S. W., & Zhu, D. (2016). Big Data and Business: Tech mining to capture business interests and activities around Big Data. 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Atlanta, GA, 145-150. doi:1109/BDCloud-SocialCom-SustainCom.2016.32.
  8. Huang, Y.,Zhu, D., Lv, Q., Porter A. L., & Wang, X. (2016). Early insights of Emerging Sources Citation Index (ESCI): a bibliometrics analysis and overlap mapping method, València: 6th Global TechMining Conference, 2016.
  9. Guo, Y., Sun, G., Huang, Y., Fu, Y., & Qian, Y. (2016). Study on main delivery actors in Technology Delivery System (TDS) based on multi-data sources. 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bali, Indonesia, 661-665. doi:1109/IEEM.2016.7797958
  10. Zhang, Y., Huang, Y., Porter, A.L. Zhang, G., & Lu, J. (2017). Discovering interactions in Big Data research: A learning-enhanced bibliometric study. 2017 Proceedings of PICMET ’17: Technology Management for Social Innovation.
  11. Guo, Y., Liu, J., Porter, A.L. (2017). A Systematic Method for Technology Assessment: Illustrated for Big Data Analytics. Annual Conference on Big Data and Business Analytics, Shanghai.

Project Outcomes related:

US Government Accountability Office (September, 2016), Data and Analytics Innovation:  Emerging Opportunities and Challenges   GAO-16-659SP, http://www.gao.gov/assets/680/679903.pdf [we were listed as “Experts Consulted” based on our work].