Reduce Redundancy of Data Requests Across Government Departments

1. Description of the Initiative

In response to the recommendation to reduce redundancy of data requests across government departments, Statistics Canada will reduce redundancy in its requests for financial and/or payroll information by different federal departments or agencies, by:

  1. Collaborating with the Canada Revenue Agency (CRA) to further substitute survey data with tax data based on information provided by businesses;
  2. Assessing the feasibility of substituting survey data with data from other sources (federal, provincial, municipal, or others);
  3. Working with departments, in particular Agriculture and Agri-food Canada, to rationalize data needs and seek opportunities to substitute surveys with existing or new administrative data, or other types of data (such as remote sensing, traceability, etc.);
  4. Collaborating with other federal government departments to align and coordinate information needs, regardless of purpose.

2. How will Red Tape be reduced?

In all cases where data from other sources can be used (taking into account quality, coverage, and timeliness), Statistics Canada will not collect certain financial data through respondent surveys. This will result in time saved for surveyed companies from reduced information requirements or certain respondents no longer being surveyed for those specific statistical programs.

3. How will business benefit?

Many businesses will benefit from reduced compliance costs through fewer duplicate data demands.

4. Who are the participating departments?

  • Lead department: Statistics Canada
  • Supporting departments: Canada Revenue Agency, Agriculture and Agri-Food Canada, Finance Canada

5. What are the implementation milestones?

  • 2012-2013: Report on revised data requirements completed and recommendations made from review of Agriculture Statistics Program.
  • 2013-2014: Conduct and evaluate feasibility studies on substitution of administrative data for survey data; discussions with OGDs; identify opportunities for any further data requirement reductions.