Encyclopedia of Big Data

Living Edition
| Editors: Laurie A. Schintler, Connie L. McNeely

Data-Information-Knowledge-Action Model

  • Xiaogang Ma
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32001-4_64-1



Facing the massive amounts and various subjects of datasets in the Big Data era, it is impossible for humans to handle the datasets alone. Machines are needed in data manipulation, and a model of Data-Information-Knowledge-Action will help guide us through the process of applying big data to tackle scientific and societal issues. Knowledge is one’s expertise or familiarity with a subject under working. Knowledge is necessary in the process to generate information from data about a certain issue, and then take actions. New knowledge can be generated on both the individual level and the community level, and certain explicit knowledge can be encoded as machine readable knowledge bases and be used as tools to facilitate the process of data management and analysis.

Understand the Concepts

The four concepts data, information, knowledge and action are often seen in the language people used in...

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Further Readings

  1. Clampitt, P. G. (2012). Communicating for managerial effectiveness: Problems, strategies, solutions. Thousand Oaks: SAGE Publications.Google Scholar
  2. Jensen, P. E. (2005). A contextual theory of learning and the learning organization. Knowledge and Process Management, 12(1), 53–64.CrossRefGoogle Scholar
  3. Ma, X., Carranza, E. J. M., Wu, C., & van der Meer, F. D. (2012). Ontology-aided annotation, visualization and generalization of geological time scale information from online geological map services. Computers & Geosciences, 40, 107–119.CrossRefGoogle Scholar
  4. McDermott, R. (1999). Why information technology inspired but cannot deliver knowledge management. California Management Review, 41(4), 103–117.CrossRefGoogle Scholar
  5. Petrides, L. A. (2002). Organizational learning and the case for knowledge-based systems. New Directions for Institutional Research, 2002(113), 69–84.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of IdahoMoscowUSA