Big Data Project Inventory

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The GWG Big Data Inventory is a catalog of Big Data projects that are relevant for official statistics, SDG indicators and other statistics needed for decision-making on public policies, as well as for management and monitoring of public sector programs/projects. This inventory is a joint product of the World Bank and the United Nations Statistics Division (UNSD) put together on behalf of the UN Global Working Group (GWG) on Big Data for Official Statistics. The tasks related to the content of the inventory are led by the World Bank and UNSD, and the technical side is serviced by the UNSD technical team.


If you are working on a project that you would like to be considered for inclusion in this Inventory, even if the project is in an initial phase, please fill out this application form.

Please note that the project should either use Big Data sources and/or utilize Big Data techniques, and ideally have some relevance or implications for official statistics, SDG indicators or other statistics needed for decision-making on public policies. The Global Working Group will review submissions and include those projects that meet these criteria, or possibly contact you for further information. Please note that the information submitted below, once approved, will be made public on the GWG Big Data Project Inventory website.

Using scanner data for compilation of CPI

Country/Area: Denmark
Organization / Dept: Denmark - Statistics Denmark
    Data sources:
  • Scanner data

Contact information

Project description:

The purpose of this project is to test whether scanner data from the two major supermarket chains in Denmark can be used in the production of the CPI.


  • Pilot intended to go to production to replace existing data

Statistics Area:

  • Price statistics

  • Data providers: Not Specified
  • Other partners:
    • Other
  • Partnerships Comments: Supermarket chains

SDG Indicators
  • SDG Goals: Not Specified
  • SDG Comments: Not Specified
  • SDG Relevance: Not Specified

Data Access
  • Data Access Rights: Only for this project
  • Intermediary: No

Data Coverage
  • Coverage Period: 2014/2015
  • Data Coverage: All available data
  • Coverage Geo Pop: Whole country / high % of market
  • Cost Implication: Free

Data Quality
  • Validation With Training Data: Yes
  • Validation Comments: We are using usual CPI data collected by 'price inspectors' to validate the data.
  • Quality Framework: Quality of source/input
  • Data Quality Concerns: Yes
  • Data Quality Concerns Comments: We are comparing to ordinary CPI data.
  • Quality Aspects Evaluated:
    • Accuracy, including selectivity
    • Coherence, including linkability to other sources
    • Validity

  • Methods Used:
    • Traditional statistical methods
  • Developed New Methods: No

  • Technologies Used:
    • Other

  • Timeframe To Produce Indicator: NA