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An Exercise in Exploring Big Data for Producing Reliable Statistical Information.

Pilar Rey-Del-Castillo1, Jesús Cardeñosa1

  • 1Artificial Intelligence Department, School of Computer Engineering, Universidad Politécnica de Madrid , Madrid, Spain .

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This summary is machine-generated.

Official statistics can leverage Big Data, but challenges like confidentiality and representativeness persist. This study uses Call Detail Records to highlight these issues and proposes a graphical method for quality assessment.

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Area of Science:

  • Statistics
  • Data Science
  • Social Sciences

Background:

  • Growing availability of Big Data presents opportunities for official statistics production.
  • National statistical organizations are exploring new methods, potentially shifting statistical production paradigms.
  • Current production systems rarely utilize Big Data sources due to significant challenges.

Purpose of the Study:

  • To illustrate the issues in producing statistical indicators from Big Data sources.
  • To demonstrate the challenges related to confidentiality, data ownership, and representativeness.
  • To propose a graphical method for evaluating the quality of statistical figures derived from Big Data.

Main Methods:

  • Utilized Call Detail Records (CDRs) from Ivory Coast as a case study.
  • Analyzed the practical challenges encountered when processing Big Data for statistical purposes.
  • Developed and presented a novel graphical method for quality assessment of computed figures.

Main Results:

  • Identified key obstacles (confidentiality, ownership, representativeness) hindering Big Data integration in official statistics.
  • Demonstrated the utility of Call Detail Records for producing statistical indicators.
  • Showcased that the proposed graphical method enhances the evaluation of data quality compared to traditional methods.

Conclusions:

  • Producing official statistics from Big Data sources is complex and requires addressing significant hurdles.
  • The proposed graphical method offers improved insights into the quality of statistical indicators derived from Big Data.
  • Further research and development are needed to overcome barriers and fully leverage Big Data in official statistics.