Why are we conducting this survey?

The Agricultural Water Survey is conducted to gather information on water use, irrigation methods and practices, and sources and quality of water used for agricultural purposes. This survey is part of the Canadian Environmental Sustainability Indicators (CESI) program. The data collected will be used in CESI's reporting activities and to inform water use policy and development of programs for Canadian irrigators. Statistics Canada will also use the survey results to report on total water use by sector in Canada.

Your information may also be used by Statistics Canada for other statistical and research purposes.

Although voluntary, your participation is important so that the information collected is as accurate and complete as possible.

Other important information

Authorization to collect this information

Data are collected under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S-19.

Confidentiality

By law, Statistics Canada is prohibited from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent, or as permitted by the Statistics Act. Statistics Canada will use the information from this survey for statistical purposes only.

Record linkages

To enhance the data from this survey and to reduce the reporting burden, Statistics Canada may combine the acquired data with information from other surveys or from administrative sources.

Contact us if you have any questions or concerns about record linkage:

Email: infostats@statcan.gc.ca

Telephone: 1-877-949-9492

Mail:
Chief Statistician of Canada
Statistics Canada
Attention of Director, Enterprise Statistics Division
150 Tunney's Pasture Driveway
Ottawa, Ontario
K1A 0T6

Data-sharing agreements

Section 12 of the Statistics Act provides for the sharing of information with federal, provincial or territorial government organizations. Under Section 12, you may refuse to share your information with any of these organizations by writing a letter of objection to the Chief Statistician, specifying the organizations with which you do not want Statistics Canada to share your data and mailing it to the following address:

Chief Statistician of Canada
Statistics Canada
Attention of Director, Enterprise Statistics Division
150 Tunney's Pasture Driveway
Ottawa, Ontario
K1A 0T6

You may also contact the Statistics Canada Help Desk- this link will open in a new window by email statcan.esdhelpdesk-dsebureaudedepannage.statcan@statcan.gc.ca or by fax at 613-951-6583.

For this survey, there are Section 12 agreements with Agriculture and Agri-Food Canada, Environment and Climate Change Canada, the Institut de la statistique du Québec, Manitoba Agriculture, Food and Rural Development, and Alberta Agriculture and Rural Development.

For agreements with provincial government organizations, the shared data will be limited to information pertaining to business establishments located within the jurisdiction of the respective province.

Reporting instructions

• When precise figures are not available, provide your best estimates.
• Percentages should be rounded to whole numbers
• Enter '0' if there is no value to report

Who should complete this questionnaire?

This questionnaire should be completed by the person that has good knowledge of the irrigation activities practiced on this farm.

Deadline for completing this questionnaire

Please complete this questionnaire and submit it within 14 days of receipt.

Printing your completed questionnaire

You can print this questionnaire once you have completed and submitted it.

Business or organization and contact information

1. Verify or provide the business or organization's legal and operating name and correct where needed.

Note: Legal name modifications should only be done to correct a spelling error or typo.

Legal Name

The legal name is one recognized by law, thus it is the name liable for pursuit or for debts incurred by the business or organization. In the case of a corporation, it is the legal name as fixed by its charter or the statute by which the corporation was created.

Modifications to the legal name should only be done to correct a spelling error or typo.

To indicate a legal name of another legal entity you should instead indicate it in question 3 by selecting 'Not currently operational' and then choosing the applicable reason and providing the legal name of this other entity along with any other requested information.

Operating Name

The operating name is a name the business or organization is commonly known as if different from its legal name. The operating name is synonymous with trade name.

2. Verify or provide the contact information of the designated business or organization contact person for this questionnaire and correct where needed.

Note: The designated contact person is the person who should receive this questionnaire. The designated contact person may not always be the one who actually completes the questionnaire.

• First name
• Last name
• Title
• Preferred language of communication
  • English
  • French
• Mailing address (number and street)
• City
• Province, territory or state
• Postal code or ZIP code
• Country
  • Canada
  • United States
• Email address
• Telephone number (including area code)
• Extension number (if applicable)
  The maximum number of characters is 5.
• Fax number (including area code)

3. Please verify or provide the current operational status of the business or organization identified by the legal and operating name above.

• Operational
• Not currently operational
  Why is this business or organization not currently operational?
  • Seasonal operations
    • When did this business or organization close for the season?
      • Date
    • When does this business or organization expect to resume operations?
      • Date
  • Ceased operations
    • When did this business or organization cease operations?
      • Date
    • Why did this business or organization cease operations?
      • Bankruptcy
      • Liquidation
      • Dissolution
      • Other - Specify the other reasons for ceased operations
  • Sold operations
    • When was this business or organization sold?
      • Date
    • What is the legal name of the buyer?
  • Amalgamated with other businesses or organizations
    • When did this business or organization amalgamate?
      • Date
    • What is the legal name of the resulting or continuing business or organization?
    • What are the legal names of the other amalgamated businesses or organizations?
  • Temporarily inactive but will re-open
    • When did this business or organization become temporarily inactive?
      • Date
    • When does this business or organization expect to resume operations?
      • Date
    • Why is this business or organization temporarily inactive?
  • No longer operating due to other reasons
    • When did this business or organization cease operations?
      • Date
    • Why did this business or organization cease operations?

4. Verify or provide the current main activity of the business or organization identified by the legal and operating name above.

Note: The described activity was assigned using the North American Industry Classification System (NAICS).

This question verifies the business or organization's current main activity as classified by the North American Industry Classification System (NAICS). The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply-side or production-oriented principles, to ensure that industrial data, classified to NAICS, are suitable for the analysis of production-related issues such as industrial performance.

The target entity for which NAICS is designed are businesses and other organizations engaged in the production of goods and services. They include farms, incorporated and unincorporated businesses and government business enterprises. They also include government institutions and agencies engaged in the production of marketed and non-marketed services, as well as organizations such as professional associations and unions and charitable or non-profit organizations and the employees of households.

The associated NAICS should reflect those activities conducted by the business or organizational units targeted by this questionnaire only, as identified in the 'Answering this questionnaire' section and which can be identified by the specified legal and operating name. The main activity is the activity which most defines the targeted business or organization's main purpose or reason for existence. For a business or organization that is for-profit, it is normally the activity that generates the majority of the revenue for the entity.

The NAICS classification contains a limited number of activity classifications; the associated classification might be applicable for this business or organization even if it is not exactly how you would describe this business or organization's main activity.

Description and examples

• This is the current main activity.
• This is not the current main activity.
  Please provide a brief but precise description of this business or organization's main activity.
  e.g., breakfast cereal manufacturing, shoe store, software development

Main activity

5. You indicated that is not the current main activity. Was this business or organization's main activity ever classified as: {specified activity}?

• Yes
• No
  When did the main activity change?
  Date

6. Search and select the industry classification code that best corresponds to this business or organization's main activity.
Select this business or organization's activity sector (optional)

• Farming or logging operation
• Construction company or general contractor
• Manufacturer
• Wholesaler
• Retailer
• Provider of passenger or freight transportation
• Provider of investment, savings or insurance products
• Real estate agency, real estate brokerage or leasing company
• Provider of professional, scientific or technical services
• Provider of health care or social services
• Restaurant, bar, hotel, motel or other lodging establishment
• Other sector

Crops

1. Which of the following crops did this operation produce or grow during the 20XX season?

Exclude:

• land rented or leased to others
• summerfallow
• Christmas trees
• greenhouses, nurseries and sod operations.

Select all that apply.

• Forage crops
  • Include hay, improved pasture and silage
• Field crops
  • Include potatoes
• Fruit crops
  • Include all berries
• Vegetable crops
  • Exclude potatoes

OR

• Did not produce or grow any of the above crops during the 20XX season

Irrigated Crops

2. Did this operation irrigate forage crops during the 20XX season?

Irrigation is defined as applying water to the soil to assist the growing of crops in dry areas during periods of inadequate rainfall, to prevent soil compaction or erosion, or to protect plants against frost.

Include hay, improved pasture and silage.

Exclude land rented or leased to others

• Yes
• No

3. Did this operation irrigate field crops during the 20XX season?

Irrigation is defined as applying water to the soil to assist the growing of crops in dry areas during periods of inadequate rainfall, to prevent soil compaction or erosion, or to protect plants against frost.

Include potatoes.

Exclude land rented or leased to others

• Yes
• No

4. Did this operation irrigate fruit crops during the 20XX season?

Irrigation is defined as applying water to the soil to assist the growing of crops in dry areas during periods of inadequate rainfall, to prevent soil compaction or erosion, or to protect plants against frost.

Include all berries.

Exclude land rented or leased to others

• Yes
• No

5. Did this operation irrigate vegetable crops during the 20XX season?

Irrigation is defined as applying water to the soil to assist the growing of crops in dry areas during periods of inadequate rainfall, to prevent soil compaction or erosion, or to protect plants against frost.

Exclude

• land rented or leased to others
• potatoes

• Yes
• No

6. During the 20XX season, what were the reasons this operation was unable to irrigate?

Exclude land rented or leased to others

Select all that apply.

• Shortage of on-farm surface water
  • Include lakes, rivers, creeks or streams, ponds or dugouts.
• Shortage of on-farm underground water or well water
  • Include shallow wells and deep wells.
• Poor water quality
• High cost of obtaining water
  • E.g., cost of deliver, cost of fuel to operate pumps, cost of installing irrigation system
• Irrigation ban
• Equipment breakdown
• Lack or personnel
• Other
  • Specify other reason

OR

• Nothing prevented this operation from irrigating

Units of measure

7. What are the units of measure that will be used to report the land areas and quantity of water used for irrigation throughout the questionnaire?

Land areas

• Acres
• Hectares
• Arpents

Quantity of water used for irrigation

• Inches per acre
• Millimeters (mm)
• Imperial gallons (imp. gal.)
• U.S. gallons (U.S. gal)
• Litres (L)
• Cubic metres (m³)
• Total acre-feet
• Acres_feet per acre
• Imperial gallons per acre
• U.S. gallons per acre

Forage crops

8. What area of land was irrigation applied to for forage crops during the 20XX season?

Report the areas only once, even if more than one forage crop will be grown in the same area.

Include hay, improved pasture and silage.

Exclude land rented or leased to others.

When exact figures are not available, please provide your best estimate

Total irrigated land area for forage crops:

9. How many acres of forage crops was irrigation applied to for the following months?

Specify the total irrigated area for forage crops that received irrigation during the 20XX season.

When exact figures are not available, please provide your best estimate.

How many acres of forage crops was irrigation applied to for the following months?

	20XX Irrigated land area	20XX Quantity of water used
April 20XX
May 20XX
June 20XX
July 20XX
August 20XX
September 20XX
October 20XX
Total

10. What irrigation methods were used to irrigate forage crops during the 20XX season?

Select all that apply.

• Sprinkler irrigation
  • Include hand move; solid or permanent set; side roll; wheel line, wheel move or wheel roll; traveller, volume gun, travelling gunwalker, overhead or circular; linear move; and centre pivot.
• Micro-irrigation
  • Include surface drip, sub-surface drip, micro-sprinklers, bubblers, micro jets and hand watering.
• Surface irrigation
  • Include down rows, furrows, corrugations, border dyke, level basins, uncontrolled flooding and back flooding. Exclude surface drip irrigation.

Field crops

11. What area of land was irrigation applied to for field crops during the 20XX season?

Report the areas only once, even if more than one forage crop will be grown in the same area.

Include potatoes.

Exclude land rented or leased to others.

When exact figures are not available, please provide your best estimate.

Total irrigated land area for field crops:

12. How many acres of field crops was irrigation applied to for the following months?

Specify the total irrigated area for field crops that received irrigation during the 20XX season.

When exact figures are not available, please provide your best estimate.

How many acres of field crops was irrigation applied to for the following months?

	20XX Irrigated land area	20XX Quantity of water used
April 20XX
May 20XX
June 20XX
July 20XX
August 20XX
September 20XX
October 20XX
Total

13. What irrigation methods were used to irrigate field crops during the 20XX season?

Select all that apply.

• Sprinkler irrigation
  • Include hand move; solid or permanent set; side roll; wheel line, wheel move or wheel roll; traveller, volume gun, travelling gun, walker, overhead or circular; linear move; and centre pivot.
• Micro-irrigation
  • Include surface drip, sub-surface drip, micro-sprinklers, bubblers, micro jets and hand watering.
• Surface irrigation
  • Include down rows, furrows, corrugations, border dyke, level basins, uncontrolled flooding and back flooding. Exclude surface drip irrigation.

Fruit crops

14. What area of land was irrigation applied to for fruit crops during the 20XX season?

Report the areas only once, even if more than one field crop will be grown in the same area.

Include all berries.

Exclude land rented or leased to others.

When exact figures are not available, please provide your best estimate

Total irrigated land area for field crops:

15. How many acres of fruit crops was irrigation applied to for the following months?

Specify the total irrigated area for fruit crops that received irrigation during the 20XX season.

When exact figures are not available, please provide your best estimate.

How many acres of fruit crops was irrigation applied to for the following months?

	20XX Irrigated land area	20XX Quantity of water used
April 20XX
May 20XX
June 20XX
July 20XX
August 20XX
September 20XX
October 20XX
Total

16. What irrigation methods were used to irrigate fruit crops during the 20XX season?

Select all that apply.

• Sprinkler irrigation
  • Include hand move; solid or permanent set; side roll; wheel line, wheel move or wheel roll; traveller, volume gun, travelling gun, walker, overhead or circular; linear move; and centre pivot.
• Micro-irrigation
  • Include surface drip, sub-surface drip, micro-sprinklers, bubblers, micro jets and hand watering.
• Surface irrigation
  • Include down rows, furrows, corrugations, border dyke, level basins, uncontrolled flooding and back flooding. Exclude surface drip irrigation.

Vegetable crops

17. What area of land was irrigation applied to for vegetable crops during the 20XX season?

Report the areas only once, even if more than one vegetable crop will be grown in the same area.

Exclude

• land rented or leased to others.
• Potatoes.

When exact figures are not available, please provide your best estimate

Total irrigated land area for vegetable crops:

18. How many acres of vegetable crops was irrigation applied to for the following months?

Specify the total irrigated area for vegetable crops that received irrigation during the 20XX season.

When exact figures are not available, please provide your best estimate.

How many acres of vegetable crops was irrigation applied to for the following months?

	20XX Irrigated land area	20XX Quantity of water used
April 20XX
May 20XX
June 20XX
July 20XX
August 20XX
September 20XX
October 20XX
Total

19. What irrigation methods were used to irrigate forage crops during the 20XX season?
Select all that apply.

• Sprinkler irrigation
  • Include hand move; solid or permanent set; side roll; wheel line, wheel move or wheel roll; traveller, volume gun, travelling gun, walker, overhead or circular; linear move; and centre pivot.
• Micro-irrigation
  • Include surface drip, sub-surface drip, micro-sprinklers, bubblers, micro jets and hand watering.
• Surface irrigation
  • Include down rows, furrows, corrugations, border dyke, level basins, uncontrolled flooding and back flooding. Exclude surface drip irrigation.

Summary

20. This is a summary of this operation's total irrigated area and quantity of water used during the 20XX season.

This is a summary of this operation's total irrigated area and quantity of water used during the 20XX season.

	20XX Irrigated land area	20XX Quantity of water used
a. Forage crops
b. Field crops
c. Fruit crops
d. Vegetable crops
Total

Irrigation practices and water sources

21. Of all the water used for irrigation during the 20XX season, what percentage came from each of the following?

Of all the water used for irrigation during the 20XX season, what percentage came from each of the following?

	20XX Percentage
a. On-farm, underground water or well water Include shallow wells and deep wells.
b. On-farm lakes, rivers, creeks or streams, ponds or dugouts. i.e., direct access (surface water)
c. Off-farm water transported to the farm. e.g., via pipeline, canal system or vehicle, including municipal water and any surface water located off-farm
d. Other Specify
Total

Off-farm water transported to the farm

22. Of all the off-farmwater used for irrigation during the 20XX season, what percentage came from each of the following?

Of all the off-farmwater used for irrigation during the 20XX season, what percentage came from each of the following?

	20XX Percentage
a. Tap water
b. Treated wastewater
c. Provincial water sources e.g., irrigation district, irrigation project
d. Private sources
e. Other Specify other source
Total

23. Why did this operation need to obtain water from an off-farm source during the 20XX season?

Select all that apply

• No water or not enough water available on the farm for irrigation
• Poor quality of on-farm water for irrigation
• Equipment breakdown
• Other
  • Specify other reason

Water and energy conservation practices

24. Which of the following practices were used during the 20XX season to conserve water or energy?

Select all that apply

• Wind breaks
  • e.g., fences, tree lines, hedges
• Leaving stubble on fields
  • E.g., minimum tillage, direct seeding
• Watering at night or in the morning
• Pressure reduction
• Water or energy saving nozzles
• Incorporating compost or other material into soil to increase soil water retention
• Other
  • Specify other practice

OR

• No water or energy saving practices were used during the 20XX season

Crops

25. Did this operation ever produce or grow forage, field, fruit or vegetable crops in previous seasons?

• Yes
• No

Irrigated crops

26. Did this operation apply irrigation to any of its crops in previous seasons?

Irrigation is defined as applying water to the soil to assist the growing of crops in dry areas during periods of inadequate rainfall, to prevent soil compaction or erosion, or to protect plants against frost.

• Yes
• No

Changes or events

27. Indicate any changes or evens that affected the reported values for this operation compared with the last reporting period.

Select all that apply

• Strike or lock-out
• Natural disaster
• Acquisition of business or business units
• Other
  • Specify the other changes or events

OR

• No changes or events

Contact person

28. Statistics Canada may need to contact the person who completed this questionnaire for further information.

Is the respondent given names and respondent family names the best person to contact?

• Yes
• No

Who is the best person to contact about this questionnaire?

• First name
• Last name
• Title
• Email address
• Telephone number (including area code)
• Extension number (if applicable)
• Fax number (including area code)

Feedback

29. How long did it take to complete this questionnaire?

Include the time spent gathering the necessary information.

• Hours
• Minutes

30. Do you have any comments about this questionnaire?

In July 2025, questions measuring the Labour Market Indicators were added to the Labour Force Survey as a supplement.

Questionnaire flow within the collection application is controlled dynamically based on responses provided throughout the survey. Therefore, some respondents will not receive all questions, and there is a small chance that some households will not receive any questions at all. This is based on their answers to certain LFS questions.

Labour Market Indicators

ENTRY_Q1 / EQ 1 - From the following list, please select the household member that will be completing this questionnaire on behalf of the entire household.

EDU_Q01 / EQ 2 - Since January 2025, have you attended college or university?

1. Yes
2. No

JREL_Q01 / EQ 3 - How closely is your main job or business related to your field of study?

1. Closely related
2. Somewhat related 
3. Not at all related

JTRA_Q01 / EQ 4 - Where you provided informal or on-the-job training from co-workers or supervisors for your main job?

1. Yes
2. No

JTRA_Q02 / EQ 5 - Was this informal or on-the-job training useful for you in your main job?

1. Yes
2. No

ERET_Q01 / EQ 6 - Do you intend to work for your main employer after the completion of your studies?

1. Yes
2. No

ERET_Q02 / EQ 7- What is the main reason you would work for your main employer again?

Is it:

1. Salary and benefits
2. Flexibility
   e.g., work-life balance, schedule, work location
3. Enriching work
4. Relationship with supervisor and colleagues
5. Career advancement 
6. Other
   • Specify

ERET_Q03 / EQ 8 - What is the main reason you would not work for your main employer again?

Is it:

1. Poor salary and benefits
2. Lack of flexibility
   e.g., work-life balance, schedule, work location
3. Lack of enriching work
4. Poor relationship with supervisor and colleagues
5. Lack of career advancement
6. Would like to work in a different field
7. Other
   • Specify

ITEP_Q01 / EQ 9 - To what extent are you confident in your employment prospects in your current line of work?

1. Very confident 
2. Quite confident 
3. Somewhat confident 
4. Not very confident  
5. Not at all confident

ITEP_Q02 / EQ 10 - What is the main reason why you do not feel confident about your employment prospects in your line of work?

Is it:

1. Layoffs in your industry
2. International trade and tariff-related economic uncertainty
3. Artificial Intelligence (AI) or automation 
4. Lack of skills or experience
5. Lack of education or credentials
6. Other
   • Specify

Two countries, both alike in dignity,
In North America, where we lay our scene,
From ancient friendship break to new trade tragedy,
Of data on trade and tariffs, what might we glean?

Join us as we speak with Guy Gellatly, Chief Economic Advisor, at Statistics Canada, as we explore what’s been going on with trade with our closest neighbour, what the early numbers suggest about the conflict, and what impact it might have on Canada’s economy.

Focus on Canada and the United States

Host

Tegan Bridge

Guest

Guy Gellatly

Listen to audio

The Census of Population provides high-quality information on key socioeconomic trends, supporting Canadians as they make important decisions that affect their families, neighbourhoods, and businesses. Census information can help communities plan services that support employment, childcare, schools, public transportation, and healthcare.

The Census of Agriculture is conducted at the same time and collects information about every agricultural operation in Canada. The Census of Agriculture is Canada's sole source of community-level data on agriculture and provides valuable information to farmers, agricultural organizations, and policy makers at all levels of government.

In preparation for each census cycle, Statistics Canada follows a detailed content determination process, which includes several stages of consultative engagement, testing and evaluation, to recommend questionnaire content for the next census cycle.

Consulting with census data users and the Canadian public allows Statistics Canada to identify whether the questions asked in the Census of Population are relevant, how census data are used and how important the census is to Canadians. A formal consultation is set at the start of each census cycle. During that time, Statistics Canada invites data users, stakeholders, and the public to provide feedback on what information they use, for what purpose they use it and what (if any) data gaps Statistics Canada should consider addressing in the next census cycle.

A similar approach is used for the Census of Agriculture, with Statistics Canada conducting extensive consultations with different governments, agricultural operators, farm organizations, and data users.

Following the consultation, Statistics Canada qualitatively and quantitively tests new and/or modified questions for the Census of Population and the Census of Agriculture to ensure data quality and the continued relevance of the census content.

Census questions are reviewed by the Cabinet of Canada and approved by the Governor in Council, who issues an order in council, as required by the Statistics Act. The census questions are then published in the Canada Gazette.

For details on the census content changes for 2026, visit the Road to the 2026 Census.

To learn more about the 2026 Census of Agriculture, visit Census of Agriculture.

July 4, 2025 | Ottawa, Ontario

Statistics Canada is pleased to announce that the questions for the 2026 Census of Population and the 2026 Census of Agriculture have been published in the Canada Gazette and are now available to all Canadians on the Statistics Canada website.

Every five years, Canadians and agricultural businesses are invited to participate in the census to help paint a portrait of Canada. The information they provide allows us to better understand Canada's economy and society.

Between each census cycle, Statistics Canada conducts comprehensive research, consultations and extensive testing to ensure the census fully captures the diversified profiles of Canadians and agriculture businesses.

Statistics Canada is committed to keeping the questions relevant, accessible, and as easy to answer as possible for all respondents. This ensures that each census provides the high-quality data that Canadians rely on to make informed decisions. See the backgrounder for more details on how content for the 2026 Census questionnaires is determined.

Collection for the 2026 Census will begin in May 2026, except for the most northern and remote areas of the country where collection will begin in February 2026.

To learn more about the 2026 Census, visit the Road to the 2026 Census.

To learn more about the 2026 Census of Agriculture, visit Census of Agriculture.

Quote

"Every five years, the Census of Population and the Census of Agriculture capture the most complete and accurate portrait of Canada, providing invaluable insights about our country. These are data that matter, and it all begins by asking the right questions. A lot of work and consultation go into the development of the census questionnaires. We are incredibly grateful to everyone who contributed to this work by sharing their feedback and taking part in census tests. This will ensure the census continues to deliver the reliable data Canadians have come to expect."

André Loranger, Chief Statistician of Canada

Contact

Statistics Canada
Media Relations
613-951-4636
statcan.mediahotline-ligneinfomedias.statcan@statcan.gc.ca

Canadian Centre for Justice and Community Safety Statistics
June 2025

Executive summary

In response to increasing demands for better disaggregated data to help shed light on individuals' diverse experiences, Statistics Canada and the Canadian Association of Chiefs of Police have committed to collaborating on an initiative to collect data on the Indigenous and racialized identity of all victims and accused persons through the Uniform Crime Reporting (UCR) Survey. The current article provides an update on the status and next steps of this initiative—the Police-reported Indigenous and Racialized Identity Data (PIRID) project.

On September 1, 2022, Statistics Canada published the interim Report and Draft Recommendations: Police-Reported Indigenous and Racialized Identity Statistics via the Uniform Crime Reporting Survey (hereafter referred to as "the September report"). This report was a culmination of feedback received through engagements with various partners as part of the initial step toward the collection of police-reported data on the Indigenous and racialized identity of all victims and accused persons through the UCR Survey. The September report included reassurances, guiding principles and recommendations to inform the next steps of the initiative. There were six recommendations developed as part of the first phase; expanded to 12 throughout Phase II, which are informing the work currently underway related to the PIRID project. These recommendations can be found in Appendix A below.

Following the release of the September report, Phase II of the project began with the goal of operationalizing the recommendations outlined in Phase I and developing a process roadmap for interpreting the draft recommendations from this report into concrete data collection and analysis methods and plans. Similar to Phase I, Statistics Canada conducted a second phase of engagements with various partners with diverse perspectives to obtain their feedback on the recommendations and assess any concerns or considerations related to the operationalization. The "What we have done" section below outlines the efforts undertaken related to operationalizing the initiative during Phase II, and the beginning of implementation during Phase III.

Background

On July 15, 2020, a joint announcement was made by Statistics Canada and the Canadian Association of Chiefs of Police (CACP) committing to working collaboratively on the collection of Indigenous and racialized identity data of all accused and victims of criminal incidents through the Uniform Crime Reporting (UCR) Survey. The announcement was in response to increasing demands for better disaggregated data that would speak to the disparate treatment and overrepresentation of Indigenous and racialized persons in the Canadian criminal justice system. A lack of national-level data has made it difficult to assess the extent of these issues accurately and to develop effective solutions (David & Mitchell, 2021; Millar & Owusu-Bempah, 2011; Samuels-Wortley, 2021; Truth and Reconciliation Commission of Canada, 2015; Wortley, 1999).

Although UCR Survey data are limited to criminal incidents, the collection of data on the Indigenous and racialized identity of all accused and victims of crime may prove critical in understanding and addressing disparities in the criminal justice system (David & Mitchell, 2021; Millar & Owusu-Bempah, 2011). As a first point of entry into the criminal justice system, UCR Survey data may also allow for further analyses of Indigenous and racialized groups' experiences with other aspects of the criminal justice system, through the linkage of policing, courts and corrections data.

Using a multi-phased approach, Statistics Canada has begun working toward the goal of enabling the collection of these data by police services. The remainder of this document will discuss the completed  phases of the initiative, the current status of the initiative and next steps. The chart below shows the major deliverables and timelines of the initiative.

Following the joint announcement in 2020, Statistics Canada consulted internally and with its key partners on how best to approach the initiative to collect Indigenous and racialized identity data of victims and accused persons through the UCR Survey. In Phase I of the initiative, Statistics Canada embarked on an engagement process to seek feedback from diverse perspectives, including Indigenous and racialized group organizations, police services, academics, and other parties of interest at the national, provincial or territorial, municipal, and local government levels. Next, Statistics Canada distributed to over 500 identified stakeholders a written engagement document with a discussion guide to seek feedback and input. The agency also held virtual roundtables between July 2021 and February 2022. These engagements led to Statistics Canada publishing a draft report and recommendations in September 2022 to guide the next phases of the initiative. The activities undertaken since the September report are briefly outlined below. For more details on Phase I of the initiative, please see the full September report.

In Phase II of the initiative (see "What we have done"), the focus was to develop a process roadmap for translating the draft recommendations from the September report into concrete data collection and analysis methods and plans. Similar to Phase I, Statistics Canada conducted a second phase of engagements with various partners with diverse perspectives, including community organizations, academics, police services, police associations and other parties of interest at the national, provincial or territorial, municipal, and local government levels to obtain their feedback on the recommendations and assess any concerns or considerations related to operationalization. The "What we have done" section below outlines the efforts undertaken related to operationalizing the initiative during this phase.

Following the completion of Phase II, the focus of the initiative will be shifted to Phase III (implementation). In the "Where we are headed" section, the goals of this phase are briefly outlined.

To stay up to date on this initiative, please visit Statistics Canada's Crime and Justice Statistics.

What we have done

Following the release of the draft report and recommendations in September 2022, the second phase of the project began with the goal of operationalizing the recommendations outlined in the first phase. The focus of Phase II (operationalization) was to conduct a second round of engagements to seek further input, address any additional concerns, and develop a process roadmap for translating the draft recommendations into concrete data collection and analysis methods and plans.

Stakeholder engagement

During Phase II, Statistics Canada held debriefs with partners, stakeholders and other interested parties on the results of Phase I, seeking their feedback and reactions.

Additionally, Statistics Canada conducted a second phase of engagements with various partners with diverse perspectives, including community organizations, academics, police services, police associations and other parties of interest at the national, provincial or territorial, municipal, and local government levels. While seeking feedback on all of the recommendations and planned next steps, these engagements primarily focused on how to operationalize recommendations 1 and 2:

Recommendation 1
The collection of information on the Indigenous and racialized identity of accused persons and victims of crimes through the Uniform Crime Reporting Survey should be conducted through both the "officer perception" method and the "self-identification" method.

Recommendation 2
The collection of information on the Indigenous and racialized identity of accused persons and victims of crimes through the Uniform Crime Reporting Survey should be conducted using Statistics Canada's standardized population group categories for both the "self-identification" method and "officer perception" method.

More specifically, these engagements sought input on

• operational needs, concerns and issues
• guidelines on how to best collect Indigenous and racialized information
• guidelines on when it is appropriate to collect Indigenous and racialized information
• ways to develop the standards and guidelines
• provincial legislation and privacy acts that may affect Indigenous and racialized identity data collection
• the use of data after collection.

Statistics Canada will continue to work with its partners toward the collective goal of providing jurisdictions with guidelines and standards for data collection and analysis, while also acknowledging that jurisdictional nuances and contexts must be considered when adopting a data collection approach. For example, Statistics Canada will work with the policing community and propose variables for the collection of officer perception data with an option to roll up detailed Indigenous and racialized identity categories that will align with existing jurisdictional standards to ensure consistency in reporting.

Launch of the Canadian Association of Chiefs of Police Special Purpose Committee

Recommendation 3
of the September report stipulates:
The Canadian Association of Chiefs of Police should work together with Statistics Canada and other parties of interest to establish national collection standards and guidelines that will integrate with police procedures, processes, and workflow.

To action this recommendation, Statistics Canada and the CACP collaborated to establish the CACP Special Purpose Committee to guide the next steps of the initiative, particularly as they relate to operationalizing the September recommendations and producing guidelines (Operational Guidelines) for police services to implement data collection. Endorsed by the CACP Board of Directors, the special purpose committee (SPC) consists of over 40 members from across different police services of different sizes, and from different regions, and other policing and non-policing CACP representatives. The work of the SPC was supported by five working groups established to bring together experts from across the participating police services and CACP committees. Working group participants had a range of experience and expertise, including front line policing, policing for Indigenous and racialized communities, outreach and engagement, records management systems or technical expertise, standards development, training, research and analysis and working on equity, diversity and inclusion initiatives.

In particular, each of the five working groups addressed a specific aspect of the Operational Guidelines:

• Community Engagement
• Legislative and Regulatory;
• Education and Awareness
• Data Standards; and
• Data Analysis and Dissemination.

Note: The Data Analysis and Dissemination working group was also tasked with developing recommendations and suggestions for the Analytical Framework.

The committee is intended to provide assessments and decisions to develop guidelines for technical and operational changes and implementation, Records Management System (RMS) changes, training, communications, and community engagement. The CACP Special Purpose Committee also has been a key support in the development of guidelines and best practices for the analysis, presentation, interpretation and dissemination of the data. They aided in developing methods for identifying appropriate benchmarks and approaches for properly contextualizing the results of analysis, in order to align with Recommendation 5 from the September report.

Recommendation 5
The analysis and use of information on the Indigenous and racialized identity of accused persons and victims of crimes should be done in a manner that reflects the realities experienced by Indigenous and racialized communities through the inclusion of context in all publications and related dissemination products.

Training Development

Police training is a critical aspect of implementing the enhancements to the UCR Survey. The Education and Awareness Working Group under the CACP Special Purpose Committee, has developed introductory training (hosted on Articulate360) for police services, with emphasis on the purposes of the data collection, as well as how and when the data should be collected.

Three Foundational Reports

Statistics Canada will be publishing three foundational reports throughout Spring/Summer 2025. These reports are: A What we Heard Report on the results of the Phase 2 engagements by StatCan: Planned release date: May 26th, 2025), an Analytical framework promoting the ethical and responsible use, analysis and dissemination of the data (Planned release date: July 16th, 2025); and the Operational guidelines for supporting police services in implementing data collection and reporting through the UCR (Planned release date: July 16th, 2025).

Other activities

In addition to the Phase II engagements and launch of the CACP Special Purpose Committee, other activities completed during this phase include the following:

• UCR system updates at Statistics Canada
  This included technical changes to develop a new version of the UCR Survey (UCR 2.5) that was released in February 2024.^{Footnote 1}
• Continued debriefs and collaboration
  Statistics Canada continues to provide updates to and engage partners. This includes presentations at various conferences and to working groups and various police services boards, as well as ad hoc presentation requests. Further, to align this national initiative with existing jurisdictional priorities, Statistics Canada is communicating and coordinating with various police services and other partners to ensure they can meet both national and jurisdictional or provincial priorities.

Where we are headed

As Phase II of the initiative draws to a close -- with the publication of the three foundational documents  (What We Heard Report, Analytical Framework, and Operational Guidelines) in Summer of 2025, Statistics Canada has now begun work on Phase III. In brief, Phase III (implementation) includes the following established goals:

• support of continued training for police, beginning with the introductory training through the Articulate 360 platform prepared by the Education and Awareness Working Group under the SPC; and
• planning and launch of PIRID (UCR 2.5) pilot projects with first adopters.

More information on pilot Implementation of PIRID Data Collection through the UCR

In the coming months, Statistics Canada aims to lead the implementation of a multi-site pilot project on collecting Police-Reported Indigenous and Racialized Identity Data (PIRID) for accused persons and victims involved in criminal incidents, through the Uniform Crime Reporting (UCR). This pilot project will serve as a springboard for the national rollout of PIRID data collection. By gathering valuable data, taking stock of lessons learned, and refining data collection procedures for both officer perception and self-identification methods, the pilot will help optimize the different components of the PIRID framework and pave the way for scaling the initiative up to the national level.

Additional details on plans and activities related to upcoming phases of the initiative will be shared at a later date.

For information and to remain up to date on the progress of this initiative, please visit Crime and Justice Statistics.

Conclusion

Statistics Canada recognizes that communities across Canada are diverse, and that the collection of police-reported identity information must consider the specific characteristics, existing relationships and contexts of the policing organizations and the communities they serve.

From a national standpoint, this initiative aims to develop evergreen national standards and guidelines for data collection and analysis that build on expertise, established frameworks, lessons learned and best practices. The agency looks forward to continuing to work with police, communities and other parties of interest to enable data collection and analysis of police-reported identity information and finding a balance that results in sound and meaningful data to help shed light on the experiences of Indigenous and racialized communities with police and the criminal justice system.

References

David, J.D., & Mitchell, M. (2021). Contacts with the Police and the Over-representation of Indigenous Peoples in the Canadian Criminal Justice System. Canadian Journal of Criminology and Criminal Justice, 63(2), 23-45.

Millar, P., & Owusu-Bempah, A. (2011). Whitewashing Criminal Justice in Canada: Preventing Research through Data Suppression. Canadian Journal of Law and Society, 26(3), 653-661.

Samuels-Wortley, K. (2021). To Serve and Protect Whom? Using Composite Counter-Storytelling to Explore Black and Indigenous Youth Experiences and Perceptions of the Police in Canada. Crime & Delinquency, 67(8), 1137-1164.

Truth and Reconciliation Commission of Canada. (2015). Calls to Action.

Wortley, S. (1999). A Northern Taboo: Research on Race, Crime, and Criminal Justice in Canada. Canadian Journal of Criminology, 41(2), 261-274.

Appendix A: Recommendations

Based on the feedback received throughout the engagement for the PIRID initiative, the following final recommendations were developed to support the implementation of a national data collection strategy.

Recommendation 1

The collection of information on the Indigenous and racialized identity of accused persons and victims of crimes through the Uniform Crime Reporting Survey should be conducted through both the "officer perception" method and the "self-identification" method.

Recommendation 2

The collection of information on the Indigenous and racialized identity of accused persons and victims of crimes through the Uniform Crime Reporting Survey should be conducted using Statistics Canada's standardized population group categories for both the "self-identification" method and the "officer perception" method.

Recommendation 3

The Canadian Association of Chiefs of Police should work together with Statistics Canada and other parties of interest to establish national collection standards and guidelines that will integrate with police procedures, processes and workflow.

Recommendation 4

Any training delivered by Statistics Canada, or the police community, should emphasize the importance of the data collection initiative and the benefits for the Canadian population, policy makers and the police.

Recommendation 5

In developing or delivering any additional training related to the collection of Indigenous and racialized identity data, police services should consider including components related to systemic racism, the purposes of collecting these data, power differentials, the importance of informed consent without reprisal when collecting self-identification data, cultural competency, sensitivity training, and ongoing training and evaluation to address evolving needs and best practices.

Recommendation 6

Police services should consider how the voices of local community members can be incorporated in the development and implementation of a data collection initiative and related training through meaningful engagement and collaboration throughout the entire process, from initial planning to implementation and evaluation. Community advisory boards or working groups can provide ongoing input and guidance.

Recommendation 7

The analysis and use of information on the Indigenous and racialized identity of accused persons and victims of crimes should be done in a manner that reflects the realities experienced by Indigenous and racialized communities through the inclusion of context (e.g., colonialism, ongoing systemic barriers, the social determinants of health and inequities for Indigenous and racialized peoples, etc.) in all publications and related dissemination products.

Recommendation 8

To ensure consistency, the standards developed in the context of this initiative should be considered for future data collection within the justice and community safety sectors.

Recommendation 9

Police services should develop plans for implementing the standards and guidelines co-developed by Statistics Canada and the Canadian Association of Chiefs of Police in their data collection initiatives or processes, considering their local contexts and the need for flexibility and adaptability. Leveraging technology and data analytics can improve data collection and analysis.

Recommendation 10

The Canadian Association of Chiefs of Police should develop mechanisms that discourage police services from using any part of the self-identification data collection process as a performance metric and recommend systems of reassurance for police service members and the communities they serve. Performance metrics should focus on outcomes and impact, rather than on the quantity of data collected. Consideration should be given to developing a code of ethics for the collection and use of Indigenous and racialized identity data.

Recommendation 11

Statistics Canada should develop guardrails to ensure the responsible use of Indigenous and racialized identity data collected through the Uniform Crime Reporting Survey. Transparency, accountability and a data governance framework should be prioritized to oversee the collection, storage and use of these data.

Recommendation 12

Police services should consider expanding the collection of Indigenous and racialized identity information for specific types of police incidents beyond criminal incidents, such as traffic stops, use of force incidents and calls for service, and for other involved persons, such as persons of interest and subjects of various interactions, to provide a more comprehensive picture of policing interactions.

Release date: September 27, 2017 More Information

The objective of this toolkit is to raise awareness about
data quality assurance practices.

1. Context
2. Quality attributes
3. Data quality assurance practices
4. Checklists

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1. Context

Question: what do you get when you combine "data" and "quality"?

Answer: the Data quality toolkit!

Well, technically, you just get "data quality", but in the context of this webpage you can consider the toolkit something of an extremely useful bonus.

But, talk of bonuses aside, a question remains: what, exactly, is data quality?

To answer this question, we must first answer two more:

• What are data?
• What is quality?

Data are made up of numbers, letters and symbols. When organized into sets, phrases, or patterns, data become information. We use information to identify needs, measure impacts and inform decisions. If the data underlying that information are incorrect in some respect, then our conclusions and decisions could also be wrong or misleading.

Quality is gauged in terms of various attributes discussed in the following section, which will vary depending on from which point of view we are assessing things.

With respect to the data producer, measures of quality include reproducibility of the process, timeliness and punctuality in delivery of data and metadata, willingness and availability to support users of the data and perception of authority and trustworthiness; with respect to the actual data and metadata, quality measures include relevance and usefulness, coverage, granularity, accuracy and reliability, and standardization or conformance.

All of this leads us to data quality, a concept which is shaped by the two just outlined above and which in turn provides two ways to determine whether data are likely to be correct or not:

• describe what was done during the gathering and processing of the data to ensure that the data are correct
• observe measurable characteristics of the data

Following good data quality assurance practices does not guarantee that the data are correct, but it does reduce the likelihood of errors. Completing a data quality assessment is a way of measuring the extent to which the data are protected against errors, and sharing that assessment with data users gives them confidence in the quality of the data.

2. Quality attributes

Quality attributes related to the data producer^{Definition of data producer}

Quality assurance practices^{Examples of quality assurance practices}: The extent to which targeted and documented quality assurance practices were followed in the gathering and processing of the data, both through commitment of the data producer at an organizational level and implementation of monitoring and reporting practices at the working level.

Reproducibility of the process: The extent to which the data production process is reproducible or repeatable. Examples of non-reproducible processes would be ad-hoc processes or instances where intermediate steps or data files were not archived and cannot be recreated.

Timeliness and punctuality: Timeliness refers to the delay between the end of the reference period to which the data pertain, and when they are available to users. Ideally this delay is brief, and the data and metadata should be available at the same time. Punctuality refers to how reliably the data and metadata are available at the expected time, as scheduled or promised by the data producer.

Contactability: The willingness and accessibility of the data producer to discuss the data with potential users, and even to facilitate usage of the data.

Viability: The extent to which one can expect the data producer to continue producing these data for a reasonable length of time into the future.

Perception of authority, impartiality and trustworthiness: The extent to which the data producer is perceived as authoritative on the subject matter of the data, is immune to undue influence of its stakeholders or other external bodies and is worthy of trust.

Security: The extent to which data security is protected in all holdings and transmissions, and access to data during production is restricted to only those with appropriate training and authority. In particular, access is granted on a "need to know" basis.

Quality attributes related to the data and metadata^{Definition of metadata}

Relevance and usefulness: The extent to which the data pertain to the desired phenomenon. Data would be considered less relevant if they are too old, or do not include information about topics of interest. Usefulness of metadata refers to the extent to which it describes the data in terms of methods, concepts, limitations, assumptions made, and quality assurance practices followed.

Coverage: The extent to which the data represent the entire desired phenomenon. This could be assessed in terms of temporal or geographic coverage, or coverage of population units (i.e., people, households, businesses). Coverage is sometimes referred to as completeness (particularly when referring to metadata).

Granularity: Granularity refers to the unit or level of a single record in the dataset. For example a highly granular dataset could contain records of people, medical procedures or lakes, while a less granular dataset could contain records aggregated to the level of a province, or a year. The more granular or local a dataset, the greater the perceived value, balanced by greater need to protect data from unauthorized disclosure. It is usually straight-forward to aggregate or roll-up from granular data to a less granular level, but rolling down from an aggregate level is not usually possible.

Accuracy and reliability: Accuracy refers to the extent to which the data correctly describes the phenomenon they are supposed to measure. Reliability is the extent to which the data are accurate consistently over time. Accuracy is often decomposed into precision, which measures how similar are repeated measurements of the same thing, and bias, which measures any systematic departures from reality in the data. Other factors contributing to accuracy and reliability are validity, the extent to which variables in the dataset have values that correspond to expected outcomes, and consistency, the extent to which the data are free of contradiction.

Standardization or conformance: The extent to which the data and metadata follow recognized standards in terms of formats and naming conventions, and conform to recognized dissemination standards such as SDMX for statistical products. Other aspects of standardization and conformance are the use of industry-standard software and file formats, and controlled vocabulary for data values where appropriate.

Protection of sensitive information: Unless consent has been explicitly given, it is not acceptable to disclose sensitive information in datasets made available to users beyond those granted specific access. Sensitive information includes, but is not limited to, identifiers that would associate granular data to a person, household or business, or sufficient detail in aggregate data such that one could deduce attributes of a person, household or business. There are various methods for protecting data against disclosure of sensitive information, depending on the nature and granularity of the data. Examples include suppression of sensitive information and introduction of random disturbance to data values. Many disclosure control algorithms provide diagnostics of the level of protection achieved.

Combinability or linkability: The extent to which it is possible to integrate two or more sources of data. For example unique identifiers such as social insurance number (SIN), business number, health insurance number can be matched directly, while somewhat unique identifiers such as combinations of name, sex, date of birth, address can be linked using statistical matching algorithms based on probabilities. The success of integrating datasets is improved when the concept of what is represented by a single record from each dataset is well aligned.

Accessibility: The ease with which users can obtain and use the data and metadata. Highly accessible data and metadata have relevant and appropriate labels, keywords and tags so that they are discoverable electronically; are in commonly used formats and software; are downloadable or available through transparent or navigable processes. Accessibility also involves reducing barriers to access, including cost.

Processability and understandability: The ease with which users can manipulate, interpret, explore, analyze, or otherwise use the data and metadata. An important component of this is the extent to which metadata and other support from the data producer lead to correct use of the data, for example through the inclusion of appropriate quality indicators.

Perception of reliability and credibility: The extent to which the data are perceived to be reliable and the metadata are perceived to be credible.

3. Data quality assurance practices

This is a set of good practices that can be followed by any organization producing data. Data producers can adapt these practices to their own environment, and are encouraged to document the data quality assurance practices that they follow and to share that documentation with their data users. Knowing what data quality assurance practices were followed in the production of data builds confidence that the data themselves are of good quality. These quality assurance practices are a subset of those found in Statistics Canada's Quality Assurance Framework and Quality Guidelines.

Data quality assurance practices for producing registers and databases

• Use known unique identifiers (SIN, Business Number, health card number, …), with appropriate protection of sensitive information
• Use check-digits on known unique identifiers to ensure valid values
• Use drop-down menus, look-up tables or reference lists for variables that should have a fixed codeset^{Definition of codeset}
• Use recognized standard formats wherever possible, i.e., ISO 8601 for dates (YYYYMMDD) and time (HH:MM), standard province abbreviations (ON, MB, etc.)
• Include built-in edits to alert when outliers or unexpected entries are made
• Validate aggregated or tabulated data against other sources
• Use a logical, documented naming convention for variables and files
• Document inclusion and exclusion rules, procedures to be followed and quality checks
• Produce output datasets at regular, predictable intervals (the last day of every month, the last day of the year)
• Define and implement a strategy for back-up, storage and retention

Data quality assurance practices for survey data (sample or census)

• Use statistically sound methods for sampling, weighting and estimation
• Ensure all methods are documented and reproducible
• Ensure survey frame is as up to date, complete and as accurate as possible
• Document frame and sample coverage with respect to time period, geographic coverage and population units
• Test questionnaire flow and interpretability
• Choose a collection method appropriate for the target population and the subject matter, given cost considerations and other factors
• Use a quality control technique such as Statistical Process Control^{Definition of Statistical Process Control} to ensure that collected data are accurate
• Make at least one attempt to contact every sampled unit, and track contact attempts
• Use editing resources efficiently and effectively; in other words, make data fit for purpose^{Definition of fit for purpose}, not perfect
• Validate aggregated or tabulated data against other sources

Data quality assurance practices for producing scanned data, satellite data or meter data

• Use a quality control technique such as Acceptance Sampling^{Definition of acceptance sampling} or "ground truthing"^{Definition of ground truthing} to ensure that data are accurate
• Ensure that changes are conveyed to all users e.g., when a new UPC code is introduced for a product

Data quality assurance practices for combining data from different sources

• Ensure that definitions align for: concepts; populations of interest; units of observation; reference periods
• Report all data sources and what contribution they make to the final product
• Analyze non-matching or leftover data to see why they did not match
• Ensure all methods are documented and reproducible

Data quality assurance practices for metadata (documentation)

• Include documentation needs in project planning and resource allocation
• Document as you go; don't leave it all to the end
• Use templates and standard naming conventions
• Describe all concepts: the population covered by the data; any limitations or exceptions in the data; the reference period
• Describe all methods used in sampling, data collection, data entry, editing, combining data from various sources, tabulation
• Describe data security measures
• Describe quality assurance practices followed
• Describe measures to protect against the disclosure of sensitive information
• Provide summary statistics on key variables (mean, median, mode, range, set of valid values)
• Provide a data dictionary or controlled vocabulary set for variables, where appropriate
• Use recognized standard formats wherever possible, i.e., ISO 8601 for dates (YYYYMMDD) and time (HH:MM), standard province abbreviations (ON, MB, etc.)
• Make documentation available to data users
• Use relevant and appropriate labels, keywords and tags so that the data and associated metadata are discoverable electronically
• Track and document updates and revisions

Data quality assurance practices for data security, accessibility and protecting against the disclosure of sensitive information

• In the data production process, restrict access to only those who have appropriate training and authority and a defined need to access the information ("need to know")
• In the data production process, protect security of data in all holdings and all transmissions through encryption and other techniques
• Adopt the "single source of truth" strategy for minimizing duplication of information and effort, in part through efficient database structures
• Use standard formats for names, dates, addresses, and other commonly used variables (international, regional or national standards where appropriate, for example ISO 8601 for dates and time (YYYYMMDD HH:MM using the 24 hour clock)
• Use standard software and file formats for files made available to other users
• Plan and prepare to share datasets at the lowest possible level of granularity (detail)
• Do regular backups
• Define and implement a retention and storage strategy
• Protect against the disclosure of sensitive information (the identity or attributes of any person or business), by masking values^{Definition of masking values} and other techniques

4. Checklists

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Thank-you for reading the toolkit! We want to make it better for you. Please take a moment to let us know which parts of it you find useful, what's missing, how we can make it better. We're also happy to answer your questions. Please send an email to the Statistics Canada Quality Secretariat.

Definition of data producer

The entity responsible for generating, collecting, gathering, processing or manipulating the data, and making it available to others.

Examples of quality assurance practices

Use recognized standards for date and time, such as YYMMDD HH:MM; validate tabulated data against other sources.

Definition of metadata

Metadata are data that describes other data. Metadata summarize basic information about data, making it easier to find and to work with.

Definition of codeset

The set of values that the variable can take on; for example the codeset for a traffic light is red, amber, green.

Definition of Statistical Process Control

A particular type of Quality Control, Statistical Process Control is a way of monitoring the variation in a process and detecting deviations from normal, to be expected variation using control charts.

Definition of fit for purpose

The expression "fit for purpose" or "fit for use" is used to represent the concept of meeting the needs of a client or user of a product, but not exceeding them. An example of a "fit for purpose" commuter vehicle would be a Honda or Mazda, not a Lamborghini.

Definition of Acceptance Sampling

A particular type of Quality Control, acceptance sampling uses statistical sampling to determine whether to accept or reject a production lot of material.

Definition of ground truthing

The process of gathering the proper objective (provable) data as opposed to information provided by inference.

Definition of masking values

Simple methods for protecting against disclosure of sensitive information are rounding, grouping values together or adding random "noise".

More information

Note of appreciation

Canada owes the success of its statistical system to a long-standing partnership between Statistics Canada, the citizens of Canada, its businesses, governments and other institutions. Accurate and timely statistical information could not be produced without their continued co-operation and goodwill.

Standards of service to the public

Statistics Canada is committed to serving its clients in a prompt, reliable and courteous manner. To this end, the Agency has developed standards of service which its employees observe in serving its clients.

Copyright

Published by authority of the Minister responsible for Statistics Canada.

© Her Majesty the Queen in Right of Canada as represented by the Minister of Industry, 2017

All rights reserved. Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue no. 12-606-X

Frequency: Occasional

Ottawa

Day 1: February 24, 2025 from 8:30am to 4:00pm

Attendance

CSAC Members

Dr. Howard Ramos (Chairperson), Catherine Beaudry, David Chaundy, Benoit Dostie, Annette Hester, Anke Kessler, André Loranger, Vinamra Mathur, Stephen Tapp, Rosemary Bender

Statistics Canada

Josée Bégin, Geoff Bowlby, Kathleen Mitchell, Katy Champagne, Jennifer Withington, Eric Rancourt, Étienne Saint-Pierre

Time	Agenda	Presenter(s)
8:30 – 8:50	Members meet-and-greet	All members
8:50 – 9:00	Chairperson introductory remarks	Howard Ramos Chairperson, CSAC
9:00 – 9:55	Process for defining official statistics at Statistics Sweden	Heather Bergdahl Statistics Sweden
9:55 – 10:15	Break
10:15 – 11:00	Chief Statistician updates and hot files	André Loranger Chief Statistician of Canada
11:00 – 12:00	Data linkages and data transfers at Statcan	Presenters (in order) Julio Rosa, Section Manager, Centre for Special Business Projects Winnie Chan, Chief, Social Analysis and Modelling Division Guests Chris Li, Director, Centre for Special Business Projects Li Xue, Director, Social Analysis and Modelling Division Shelly Jeglic, Assistant Director, Data Access Division
12:00 – 13:00	Working lunch – Discussion on the Terms of Reference
13:00 – 14:00	Advancing the Government of Canada’s Data Landscape	Stephen D. Burt Chief Data Officer of Canada, Data & Digital Policy Sector, Office of the Chief Information Officer, Treasury Board Secretariat
14:00 – 14:45	Update on Censuses	Census of Population Patrice Mathieu Director, Census Operations, Census Program Division Census of Agriculture Erin Kumar Assistant Director, Agriculture Division Guests: Augustine Akuoko-Asibey, Director General, Agriculture, Energy and Environment Statistics Cindy St-Germain, Director, Agriculture
14:45 – 15:05	Break
15:05 – 15:55	Roundtable discussion In camera	All members
15:55 – 16:00	Chairperson closing remarks	Howard Ramos Chairperson, CSAC

Day 1 Summary

1. Chairperson introductory remarks - Howard Ramos

Mr. Ramos opened the meeting by welcoming members and Assistant Chief Statisticians (ACS) and provided an overview of the agenda for the day.

2. Process for defining official statistics at Statistics Sweden – Heather Bergdahl

Ms. Bergdahl presented Statistics Sweden’s framework for defining official statistics, grounded in the Official Statistics Act and Ordinance, which require standardized methods, quality documentation, public access, annual evaluations, and gender-based breakdowns.

She outlined relevant government directives, key quality concepts, and criteria for determining official statistics, and addressed implementation challenges. An open Q&A followed. CSAC members asked about the roles of Statistics Sweden and other authorized organizations, focusing on guidance, oversight, and compliance. Ms. Bergdahl clarified that while exceptions may be granted, data quality remains essential.

3. Chief Statistician updates and hot files – André Loranger

Chief Statistician André Loranger briefed CSAC members on key priorities and challenges, including the agency’s financial position, 2026 Census planning, Canada–U.S. data relations, the role of artificial intelligence, and upcoming initiatives. He concluded by providing a summary of his recent international engagements.

Members asked about Statistics Canada’s authority during government prorogation. Mr. Loranger addressed these questions, reaffirming the agency’s international leadership and the importance of sustaining it through responsible budgeting.

4. Data linkages and data transfers at Statcan – Julio Rosa, Winnie Chan

Mr. Rosa and Ms. Chan presented on Data Linkage and Integration, outlining key drivers and two primary environments: the Business Linkable File Environment (B-LFE) and the Social Data Linkage Environment (SDLE), along with supporting analytical platforms.

CSAC members inquired about interconnections between the environments and recommended considering provincial models. The presenters confirmed ongoing collaboration with provinces and reaffirmed Statistics Canada’s responsibility under the Statistics Act to ensure strong data governance.

5. Working Lunch – CSAC Member Discussion on the Terms of Reference

The Canadian Statistics Advisory Council (CSAC) met to discuss its future composition and structure, including the anticipated departure of three members and potential vacancy in the Chair position in 2025. Members reviewed the current Terms of Reference, considering possible amendments to better reflect the Council’s evolving needs.

6. Advancing the Government of Canada’s Data Landscape – Stephen Burt

Mr. Burt presented Canada’s policy, legislative, and digital landscape, with a focus on the 2023–2026 Data Strategy, including year one achievements and year two priorities.

CSAC members raised questions about short-term challenges and long-term cultural change. Mr. Burt emphasized the need for a more unified approach across departments. The discussion also addressed the role of a national statistical system, the value of data reference standards, and potential legislative adjustments linked to budget and government-wide support.

7. Update on Censuses – Patrice Mathieu and Erin Kumar

Mr. Mathieu and Ms. Kumar provided an update on the Censuses. Topics included the start of operations, communication strategy, reducing respondent burden, and promoting job opportunities. They also outlined the content determination process, with tentative approval expected in May 2025, pending Cabinet approval.

The discussion explored potential automation options, such as notification systems and chatbots. The presenters also shared future strategies, including analysis to support recruitment and data collection.

8. Chairperson closing remarks – Howard Ramos

Mr. Ramos provided closing remarks for the day.

Day 2: February 25, 2025 from 8:50am to 12:30pm

Attendance

CSAC Members

Dr. Howard Ramos (Chairperson), Catherine Beaudry, David Chaundy, Benoit Dostie, Annette Hester, Anke Kessler, André Loranger, Vinamra Mathur, Stephen Tapp, Rosemary Bender

Statistics Canada

Geoff Bowlby, Katy Champagne, Jennifer Withington, Eric Rancourt, Étienne Saint-Pierre

Day 2 Summary

1. Chairperson Opening Remarks – Howard Ramos

Mr. Ramos welcomed members back and provided an overview of the agenda for the day.

2. Introduction to the regulation of UK Official Statistics – Helen Miller-Bakewell and Penny Babb

Ms. Miller-Bakewell, joined by Ms. Babb presented on the regulation of UK official statistics. They outlined the structure of the UK Statistical System, highlighting the role of the Office for Statistics Regulation, and described the 2007 introduction of official statistics and the accreditation process based on the principles of Trustworthiness, Quality, and Value (TQV).

CSAC members asked about the impact of the official statistics framework on public trust and sought clarification on the relationship between the National Statistician and the UK Statistics Authority (UKSA).

3. Using citizen data to report on the Sustainable Development Goals - Cara Williams and Catherine Michaud

Ms. Williams and Ms. Michaud presented the 17 Sustainable Development Goals (SDGs) and potential role of citizen-generated data in addressing current data gaps. They discussed the origins of such data, the role of national statistical offices (NSOs), reporting capabilities, and considerations around data quality.

CSAC members raised questions about data quality evaluation and fit-for-use thresholds and sought further clarity on how Statistics Canada might use this data.

4. Chairperson closing remarks – Howard Ramos

Mr. Ramos thanked CSAC members, including the Chief Statistician, the Assistant Chief Statisticians and their teams and the CSAC Secretariat for their support.