Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Data Quality Management

  • Living reference work entry
  • First Online:
Encyclopedia of Big Data

  • 132 Accesses

Introduction

With the increasing availability of Big Data and their attendant analytics, the importance of data quality management has increased. Poor data quality represents one of the greatest hurdles to effective data analytics, computational linguistics, machine learning, and artificial intelligence. If the data are inaccurate, incomprehensible, or unusable, it does not matter how sophisticated our algorithms and paradigms are, or how intelligent our “machines.”

J. M. Juran provides a definition of data quality that is applicable to current Big Data environments: “Data are of high quality if they are fit for their intended use in operations, decision making, and planning” (Juran and Godfrey 1999, p. 34.9). In this context, quality means that Big Data are relevant to their intended uses and are of sufficient detail and quantity, with a high degree of accuracy and completeness, of known provenance, consistent with their metadata, and presented in appropriate ways.

Big Data provide...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Further Readings

  • Acock, A. C. (2005). Working with missing values. Journal of Marriage and Family, 67, 1012–1028.

    Article  Google Scholar 

  • Allison, P. A. (2002). Missing data. Thousand Oaks: Sage Publications.

    Book  Google Scholar 

  • Juran, J. M., & Godfrey, A. B. (1999). Juran’s quality handbook (Fifth ed.). New York: McGraw-Hill.

    Google Scholar 

  • Labouseur, A. G., & Matheus, C. (2017). An introduction to dynamic data quality challenges. ACM Journal of Data and Information Quality, 8(2), 1–3.

    Article  Google Scholar 

  • Little, R. J. A., & Rubin, D. B. (1997). Statistical analysis with missing data. New York: Wiley.

    Google Scholar 

  • Pipino, L. L. Y. W. L., & Wang, R. Y. (2002). Data quality assessment. Communications of the ACM, 45(4), 211–218.

    Article  Google Scholar 

  • Saunders, J. A., Morrow-Howell, N., Spitznagel, E., Dore, P., Proctor, E. K., & Pescarino, R. (2006). Imputing missing data: A comparison of methods for social workers. Social Work Research, 30(1), 19–30.

    Article  Google Scholar 

  • Strong, D. M., Lee, Y. W., & Wang, R. Y. (1997). Data quality in context. Communications of the ACM, 40(5), 103–110.

    Article  Google Scholar 

  • Truong, H.-L., Murguzur, A., & Yang, E. (2018). Challenges in enabling quality of analytics in the cloud. Journal of Data and Information Quality, 9(2), 1–4.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. George Mason University, Arlington, VA, USA

    Erik W. Kuiler

Authors
  1. Erik W. Kuiler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erik W. Kuiler .

Editor information

Editors and Affiliations

  1. School of Policy, Government and International Affairs, George Mason University, Fairfax, VA, USA

    Laurie A. Schintler

  2. School of Policy, Government and International Affairs, George Mason University, Fairfax, VA, USA

    Connie L. McNeely

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Kuiler, E.W. (2019). Data Quality Management. In: Schintler, L., McNeely, C. (eds) Encyclopedia of Big Data. Springer, Cham. https://doi.org/10.1007/978-3-319-32001-4_317-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-32001-4_317-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32001-4

  • Online ISBN: 978-3-319-32001-4

  • eBook Packages: Springer Reference Business and ManagementReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Search

Navigation