Business or organization information

1. Which of the following categories best describes this business or organization?

  • 1: Government agency
  • 2: Private sector business
  • 3: Non-profit organization serving households or individuals
    e.g., child and youth services, community food services, food bank, women's shelter, community housing services, emergency relief services, religious organization, grant and giving services, social advocacy group, arts and recreation group
  • 4: Non-profit organization serving businesses
    e.g., business association, chamber of commerce, condominium association, environment support or protection services, group benefit carriers (pensions, health, medical)
  • 5: Don't know

2. In what year was this business or organization first established?

  • Year business or organization was first established:
    OR
  • 1: Don't know

3. In the last 12 months, did this business or organization conduct any of the following activities?

Select all that apply.

  • 1: Export goods or services outside of Canada
  • 2: Have operations outside of Canada
    What measures has this business or organization undertaken to respect human rights in its operations outside of Canada?
    Select all that apply.
    • 1: Hired an external advisor
    • 2: Has an internal advisor
    • 3: Adopted a policy
    • 4: Conducted internal consultations
    • 5: Conducted external consultations
    • 6: Undertook a human rights impact assessment or other due diligence exercise
    • 7: Conducted regular audits and evaluations
    • 8: Implemented specific programs internally
    • 9: Implemented specific programs externally
    • 10: Other
      Specify other:
      OR
    • 11: None of the above
      OR
    • 12: Don't know
  • 3: Make investments outside of Canada
  • 4: Sell goods to businesses or organizations in Canada who then resold them outside of Canada
  • 5: Import goods or services from outside of Canada
    Include both intermediate and final goods.
  • 6: Relocate any business or organizational activities or employees from another country into Canada
  • 7: Engage in other international business or organizational activities
    OR
  • 8: None of the above

Business or organization obstacles

4. Over the last three months, which of the following are obstacles for this business or organization?

Select all that apply.

  • 1: Shortage of labour force
  • 2: Recruiting and retaining skilled employees
  • 3: Shortage of space or equipment
  • 4: Financial constraints
  • 5: Insufficient demand for goods or services offered
  • 6: Fluctuations in consumer demand
  • 7: Obtaining financing
  • 8: Government regulations
  • 9: Rising cost of inputs
    An input is an economic resource used in a firm's production process.
    e.g., labour, capital, energy and raw materials
  • 10: Increasing competition
  • 11: Challenges related to exporting goods and services
  • 12: Corporate tax rate
  • 13: Maintaining sufficient cash flow or managing debt
  • 14: Broadband access
  • 15: Other
    Specify other:
    OR
  • 16: None of the above

Teleworking and working remotely

5. During the 2020 calendar year, is teleworking or working remotely a possibility for any employees of this business or organization?

  • 1: Yes
    • On August 31st, 2020, what percentage of the workforce was teleworking or working remotely?
      Provide your best estimate rounded to the nearest percentage.
      • Percentage:
        OR
      • 1: Don't know
    • Once the COVID-19 pandemic is over, what percentage of the workforce is anticipated to continue to primarily telework or work remotely
      Provide your best estimate rounded to the nearest percentage.
      • Percentage:
        OR
      • 1: Don't know
  • 2: No

Remote sales

6. Over the next three months, is the extent to which this business or organization uses any of the following methods for providing good or services to customers or users expected to change?

If method is used only for advertising good or services, select "Method not used".

a. Business's own website

  • 1: Increase
  • 2: Stay the same
  • 3: Decrease
  • 4: Method not used by this business

b. Business's own app

  • 1: Increase
  • 2: Stay the same
  • 3: Decrease
  • 4: Method not used by this business

c. Social media account

  • 1: Increase
  • 2: Stay the same
  • 3: Decrease
  • 4: Method not used by this business

d. E-mail

  • 1: Increase
  • 2: Stay the same
  • 3: Decrease
  • 4: Method not used by this business

e. Telephone

  • 1: Increase
  • 2: Stay the same
  • 3: Decrease
  • 4: Method not used by this business

f. External website, platform, app, or online marketplace not owned by the business or organization
An online platform is an online marketplace that places one party in touch with another, such as buyers and sellers. e.g., eBay, Craigslist, Amazon Marketplace, Airbnb and Uber. The online system may be entirely self-controlled or it may allow third-party apps to connect via the platform's programming interface.

  • 1: Increase
  • 2: Stay the same
  • 3: Decrease
  • 4: Method not used by this business

Business or organization status

7. How did the COVID-19 pandemic affect the status of this business or organization?

Select all that apply.

  • 1: This business or organization shut down temporarily but has since reopened
  • 2: This business or organization shut down temporarily and remains shut down
  • 3: This business or organization has remained partially operational
    e.g., reduced hours, reduced services
  • 4: This business or organization has remained fully operational

Current or planned measures

8. Due to the COVID-19 pandemic, what actions or measures does this business or organization have currently in place or plans to implement?

Select all that apply.

  • 1: Restriction on the number of people allowed into the businesses space at one time
    Include restrictions on access to the workplace.
  • 2: Rental or acquisition of more physical space for the business or organization
  • 3: Expansion of business or organization into existing outdoor space
  • 4: Addition of signage or floor markers to promote physical distancing
  • 5: Modification of the office space
  • 6: Adding plexiglass or sneeze guards
  • 7: Reduction of business hours
  • 8: Screen employees upon entry into the workplace for a fever, cough, or other signs of illness
  • 9: Screen customers upon entry into the workplace for a fever, cough, or other signs of illness
  • 10: Insist that employees displaying any signs of illness stay home
  • 11: Request that customers displaying any signs of illness do not enter
  • 12: Provide hand sanitizer to employees and customers
  • 13: Provide facemasks, gloves, or other personal protective equipment to employees
  • 14: Provide facemasks, gloves, or other personal protective equipment to customers
  • 15: More janitorial staff
  • 16: Frequent cleaning of high-touch areas or surfaces
  • 17: Fill positions with less skilled workers
  • 18: Subcontract out more work
  • 19: Other
    Specify other:
    OR
  • 20: No measures implemented

9. In many locations, schools and child care facilities are not fully operational. Which of the following options are provided or could possibly be provided to parents employed by this business or organization?

Select all that apply.

  • 1: Allow parents to telework or work remotely
  • 2: Allow parents to change their schedules
  • 3: Assign parents alternate tasks that can be done outside of normal business hours
  • 4: Create weekend, evening, or overnight shifts to provide more flexibility for parents
  • 5: Allow parents to switch to part-time status on a temporary or limited basis
  • 6: Offer an extended leave of absence with reduced or no pay
  • 7: Allow parents to bring children to work
  • 8: Other
    Specify other:
    OR
  • 9: Not considering any special accommodation for parents
    OR
  • 10: Not applicable — schools and child care facilities in our area are expected to have a normal schedule
    OR
  • 11: Not applicable — all work can be performed on a flexible schedule

Layoffs

10. Since the start of the COVID-19 pandemic, did this business or organization lay off any of its workforce?

Include employees that have been laid off and have since been rehired.

An employee is someone who would be issued a T4 from this business or organization. This excludes business owners, contract workers and other personnel who would not be issued a T4.

  • 1: Yes
    • What percentage of the workforce was laid off?
      Provide your best estimate rounded to the nearest percentage.
      • Percentage:
      • 1: Don't know
    • Of the workforce laid off by this business or organization, what percentage has since been rehired?
      If no staff has been rehired, please enter "0".
      Provide your best estimate rounded to the nearest percentage.
      • Percentage:
      • 1: Don't know
  • 2: No
  • 3: This business or organization has no staff
  • 5: Don't know

August 2020 revenues compared with August 2019

11. Compared to August 2019, how did the revenues of this business or organization change in August 2020?

Include grants.

  • 1: Revenues were higher in August 2020
    By what percentage were revenues higher?
    When precise figures are not available, please provide your best estimate.
    • 1% to less than 10%
    • 10% to less than 20%
    • 20% to less than 30%
    • 30% to less than 40%
    • 40% to less than 50%
    • 50% or more
  • 2: Revenues were lower in August 2020
    By what percentage were revenues lower?
    When precise figures are not available, please provide your best estimate.
    • 1% to less than 10%
    • 10% to less than 20%
    • 20% to less than 30%
    • 30% to less than 40%
    • 40% to less than 50%
    • 50% or more
  • 3: Revenues have stayed the same
  • 4: Not applicable
    e.g., started operating after August 31st, 2019

August 2020 expenses compared with August 2019

12. Compared to August 2019, how did the expenses of this business or organization change in August 2020?

Exclude wages and salaries.

  • 1: Expenses were higher in August 2020
    By what percentage were expenses higher?
    When precise figures are not available, please provide your best estimate.
    • 1% to less than 10%
    • 10% to less than 20%
    • 20% to less than 30%
    • 30% to less than 40%
    • 40% to less than 50%
    • 50% or more
  • 2: Expenses were lower in August 2020
    By what percentage were expenses lower?
    When precise figures are not available, please provide your best estimate.
    • 1% to less than 10%
    • 10% to less than 20%
    • 20% to less than 30%
    • 30% to less than 40%
    • 40% to less than 50%
    • 50% or more
  • 3: Expenses have stayed the same
  • 4: Not applicable
    e.g., started operating after August 31st, 2019

Impact on expenditures

13. For each of the following, indicate whether this business or organization has increased or decreased expenditures as a result of COVID-19.

a. Sanitization and cleaning

  • 1: Increased
  • 2: No change
  • 3: Decreased
  • 4: Does not have this expense
  • 5: Do not know

b. Repair and maintenance

  • 1: Increased
  • 2: No change
  • 3: Decreased
  • 4: Does not have this expense
  • 5: Do not know

c. Personal protective equipment and supplies

  • 1: Increased
  • 2: No change
  • 3: Decreased
  • 4: Does not have this expense
  • 5: Do not know

d. Rent

  • 1: Increased
  • 2: No change
  • 3: Decreased
  • 4: Does not have this expense
  • 5: Do not know

e. Technology and equipment for teleworking
e.g., laptops, office chairs

  • 1: Increased
  • 2: No change
  • 3: Decreased
  • 4: Does not have this expense
  • 5: Do not know

f. Research and development

  • 1: Increased
  • 2: No change
  • 3: Decreased
  • 4: Does not have this expense
  • 5: Do not know

g. Research and development personnel

  • 1: Increased
  • 2: No change
  • 3: Decreased
  • 4: Does not have this expense
  • 5: Do not know

Funding or credit

14. Due to COVID-19, was funding or credit for this business or organization approved or received from any of the following sources?

Select all that apply.

  • 1: Canada Emergency Business Account (CEBA)
    e.g., loan of up to $40,000 for eligible small businesses and non-profits
  • 2: Temporary 10% Wage Subsidy
  • 3: Canada Emergency Wage Subsidy (CEWS)
  • 4: Canada Emergency Commercial Rent Assistance (CECRA)
  • 5: Export Development Canada (EDC) Small and Medium-sized Enterprise Loan and Guarantee program
  • 6: Business Development Bank of Canada (BDC) Co-Lending Program for Small and Medium-sized Enterprises
  • 7: Innovation Assistance Program
  • 8: Regional Relief and Recovery Fund
  • 9: Provincial, Territorial or Municipal government programs
  • 10: Grant or loan funding from philanthropic or mutual-aid sources
  • 11: Financial institution
    e.g., term loan or line of credit
  • 12: Loan from family or friends
  • 13: Other
    Specify other approved source of funding or credit:
    OR
  • 14: None of the above

Flow condition: If "None of the above" is selected in Q14, go to Q15, otherwise skip to Q16.

15. For which of the following reasons has this business or organization not accessed any funding or credit due to COVID-19?

Select all that apply.

  • 1: Eligibility requirements
  • 2: Public perception
  • 3: Application requirements or complexity
  • 4: Lack of awareness
  • 5: Funding or credit not needed
  • 6: Other
    Specify other:

Liquidity

16. Does this business or organization have the cash or liquid assets required to operate?

  • 1: Yes
  • 3: No
    Will this business or organization be able to acquire the cash or liquid assets required?
    • 1: Yes
    • 3: No
    • 5: Don't know
  • 5: Don't know

Debt

17. Does this business or organization have the ability to take on more debt?

  • 1: Yes
  • 2: No
  • 3: Don't know

Business space

18. Does this business or organization own or rent/lease space?

If there are multiple locations, report for the largest location based on square footage.

  • 1: Own
    Does this business or organization intend to:
    • Move to another location
    • Sublet space to others
    • Fully maintain occupancy of current location
  • 2: Rent or lease
    Does this business or organization intend to:
    • Maintain its full lease
    • Break its lease
    • Sublet space to others
  • 3: Neither

Personal protective equipment or supplies

19. Does business or organization expect to experience difficulty procuring any of the following personal protective equipment or supplies?

a. Masks

  • 1: Significant difficulty
  • 2: Some difficulty
  • 3: No difficulty
  • 4: Not needed

b. Eye protection

  • 1: Significant difficulty
  • 2: Some difficulty
  • 3: No difficulty
  • 4: Not needed

c. Face shields
e.g., visors

  • 1: Significant difficulty
  • 2: Some difficulty
  • 3: No difficulty
  • 4: Not needed

d. Gloves

  • 1: Significant difficulty
  • 2: Some difficulty
  • 3: No difficulty
  • 4: Not needed

e. Gowns

  • 1: Significant difficulty
  • 2: Some difficulty
  • 3: No difficulty
  • 4: Not needed

f. Cleaning products

  • 1: Significant difficulty
  • 2: Some difficulty
  • 3: No difficulty
  • 4: Not needed

g. Disinfecting wipes

  • 1: Significant difficulty
  • 2: Some difficulty
  • 3: No difficulty
  • 4: Not needed

h. Hand sanitizer

  • 1: Significant difficulty
  • 2: Some difficulty
  • 3: No difficulty
  • 4: Not needed

i. Plexiglass or sneeze guards

  • 1: Significant difficulty
  • 2: Some difficulty
  • 3: No difficulty
  • 4: Not needed

j. COVID-19 testing kits

  • 1: Significant difficulty
  • 2: Some difficulty
  • 3: No difficulty
  • 4: Not needed

k. Thermometers

  • 1: Significant difficulty
  • 2: Some difficulty
  • 3: No difficulty
  • 4: Not needed

l. Other

  • 1: Significant difficulty
  • 2: Some difficulty
  • 3: No difficulty
  • 4: Not needed

Flow condition: If there are any responses of "significant difficulty" or "some difficulty" in Q19, go to Q20. Otherwise, skip Q20 and go to Q21.

20. Indicate why this business or organization expects to have difficulty procuring personal protective equipment or supplies.

Select all that apply.

  • 1: Do not know where to procure personal protective equipment or supplies from
  • 2: Normal source of personal protective equipment or supplies is unable to meet demand
  • 3: Cost of personal protective equipment or supplies is too high
  • 4: Cannot source enough personal protective equipment or supplies to meet consumption
  • 5: Other
    Specify other:

Measures permanently adopted

21. Using a scale from 1 to 5, where 1 means "very unlikely" and 5 means "very likely", how likely is this business or organization to permanently adopt each of the following measures once the COVID-19 pandemic is over?

An employee is someone who would be issued a T4 from this business or organization.

a. Offer more employees the possibility of teleworking or working remotely

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

b. Require more employees to telework or work remotely

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

c. Require employees to come back to on-site work

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

d. Increased IT infrastructure to support teleworking

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

e. Make investments to increase the security of telework systems

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

f. Automate certain tasks
e.g., through the use of robots or computer algorithms

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

g. Adopt shiftwork to increase the distance between employees

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

h. Modify the work space to increase the distance between employees

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

i. Diversify supply chains within Canada

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

j. Diversify supply chains outside Canada

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

k. Reduce hiring of temporary foreign workers

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

l. Increase online sales capacity

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

m. Increase contactless delivery or pickup options

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

n. Reduce the physical space used by this business or organization

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

o. Increase the physical space used by this business or organization

  • 1: 1 – very unlikely
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – very likely
  • 6: Not relevant

Future operations

22. Using a scale from 1 to 5, where 1 means "strongly agree" and 5 means "strongly disagree", indicate how well each of the following statements apply to this business or organization.

a. Before the pandemic, the general outlook for this business or organization was positive

  • 1: 1 – strongly agree
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – strongly disagree
  • 6: Don't know

b. This business or organization is actively considering bankruptcy or closing as a result of COVID-19

  • 1: 1 – strongly agree
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – strongly disagree
  • 6: Don't know

c. This business or organization is prepared financially for a possible second wave of COVID-19

  • 1: 1 – strongly agree
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – strongly disagree
  • 6: Don't know

d. The revenue of this business or organization over the next 3 months will be higher than it was over the last 3 months

  • 1: 1 – strongly agree
  • 2: 2
  • 3: 3
  • 4: 4
  • 5: 5 – strongly disagree
  • 6: Don't know

23. How long can this business or organization continue to operate at its current level of revenue and expenditures before having to consider staffing actions, closure or bankruptcy?

  • 1: Less than 1 month
  • 2: 1 month to less than 3 months
  • 3: 3 months to less than 6 months
  • 4: 6 months to less than 12 months
  • 5: 12 months or more
  • 6: Don't know

Expectations over the next 3 months

24. Over the next 3 months, does this business or organization expect the prices it charges to increase, stay about the same, or decrease?

  • 1: Increase
  • 2: Stay about the same
  • 3: Decrease
  • 4: Don't know
  • 5: Not applicable

25. Over the next 3 months, does this business or organization expect its overall number of employees to increase, stay about the same, or decrease?

  • 1: Increase
  • 2: Stay about the same
  • 3: Decrease
  • 4: Don't know

Flow condition: If the business or organization is a government agency or non-profit organization, skip to Q28. Otherwise, go to Q26.

Expectations for the next year

26. In the next year, are there any plans to expand this business or organization or acquire or invest in other businesses or organizations?

  • 1: Yes
    Does this business or organization plan to:
    Select all that apply.
    • 1: Expand current location of this business or organization
    • 2: Expand this business or organization to other locations
    • 3: Acquire other businesses or organizations or franchises
    • 4: Invest in other businesses or organizations
  • 2: No
  • 3: Don't know

27. In the next year, are there any plans to transfer, sell or close this business or organization?

  • 1: Yes
    Does this business or organization plan to:
    • 1: Transfer to family members without money changing hands
    • 2: Sell to family members
    • 3: Sell to employees
    • 4: Sell to external parties
    • 5: Close the business or organization
    • 6: Don't know
  • 3: No
  • 5: Don't know

Ownership

(i) The groups identified within the following questions are included in order to gain a better understanding of the impact of COVID-19 on businesses or organizations owned by members of various communities across Canada.

28. What percentage of this business or organization is owned by women?

Provide your best estimate rounded to the nearest percentage.

  • Percentage:
    OR
  • 1: Don't know

29. What percentage of this business or organization is owned by First Nations, Métis or Inuit peoples?

Provide your best estimate rounded to the nearest percentage.

  • Percentage:
    OR
  • 1: Don't know

30. What percentage of this business or organization is owned by immigrants to Canada?

Provide your best estimate rounded to the nearest percentage.

  • Percentage:
    OR
  • 1: Don't know

31. What percentage of this business or organization is owned by persons with a disability?

Provide your best estimate rounded to the nearest percentage.

  • Percentage:
    OR
  • 1: Don't know

32. What percentage of this business or organization is owned by LGBTQ2 individuals?

The term LGBTQ2 refers to persons who identify as lesbian, gay, bisexual, transgender, queer and/or two-spirited.

Provide your best estimate rounded to the nearest percentage.

  • Percentage:
    OR
  • 1: Don't know

33. What percentage of this business or organization is owned by members of visible minorities?

A member of a visible minority in Canada may be defined as someone (other than an Indigenous person) who is non-white in colour or race, regardless of place of birth.

Provide your best estimate rounded to the nearest percentage.

  • Percentage:
    OR
  • 1: Don't know

Flow condition: If more than 50% of this business or organization is owned by members of visible minorities, go to Q34. Otherwise, go to "Contact person".

34. It was indicated that a percentage of this business or organization is owned by members of visible minorities. Please select the categories that describe the owner or owners.

Select all that apply.

  • 1: South Asian
    e.g., East Indian, Pakistani, Sri Lankan
  • 2: Chinese
  • 3: Black
  • 4: Filipino
  • 5: Latin American
  • 6: Arab
  • 7: Southeast Asian
    e.g., Vietnamese, Cambodian, Laotian, Thai
  • 8: West Asian
    e.g., Afghan, Iranian
  • 9: Korean
  • 10: Japanese
  • 11: Other group
    Specify other group:
    OR
  • 12: Prefer not to say

Canadian Centre for Energy Information external stakeholder meeting - May 28, 2020

Meeting summary: Key points and action items

External stakeholders

  • Annette Hester, the Hester View, Canadian Statistics Advisory Council
  • Allan Fogwill, Canadian Energy Research Institute
  • Bradford Griffin, Canadian Energy and Emissions Data Centre
  • Louis Beaumier, Institut de l'énergie Trottier
  • Ben Brunnen and Krista Nelson, Canadian Association of Petroleum Producers

Purpose

Meeting with potential participants of the Canadian Centre for Energy Information (CCEI) External Advisory Body to discuss CCEI program objectives and the role of a supporting advisory body in meeting those goals.

Theme 1: Governance/External Advisory Committee role

Participants were generally comfortable with the proposed CCEI External Advisory Committee Terms of Reference, but wanted to have collective discussion on the mechanics of the advisory committee (e.g. how to work as a team, outputs/deliverables, how to reach consensus, engagement with external parties).

Action item

  • Include a discussion on governance/committee responsibilities during the inaugural July 2020 advisory committee meeting.

Theme 2: Trust/Transparency and importance of communication/Engagement

Participants expressed need to manage expectations through open and transparent engagement with stakeholders and the public. This included being transparent on advice received from the Advisory Committee, decision making/priority setting by the FPT Deputy-level Steering Committee, documenting how the CCEI is responding, and consider how to share information with the public (i.e. 'Trust Centre', CCEI website or other tools).

Participants also noted the importance of maintaining CCEI as 'policy neutral', both real and perceived, to ensure trust and integrity of the program.

Action items

  • Present engagement plan and communications plan at July 2020 External Advisory Committee meeting to discuss approach to obtain feedback from stakeholders and communicating with the public on program 'plans' (e.g. publishing the 5-year plan on CCEI Website to share with the public, ensuring effective communications on initial website content and direction of the program, etc…)
  • As the program moves forward and obtains advice from the committee and stakeholders informing CCEI's decision making – consider how to ensure transparency regarding advice received (high-level) and actions/decisions taken by the Centre with the general public.
  • Engage with Communications Team at StatsCan on sharing advice received systematically, and having representative from Communications join meetings moving forward to explore.
  • Ensuring/maintaining robust conversation on priority setting and data gaps as a key function of the committee (see Theme 4)

Theme 3: Data from external sources – CCEI website

Participants were pleased with the extensive availability of data from federal government, provinces, territories, academia, international institutions, etc… that the initial website would be offering later this summer.

They advised StatsCan provide 'caveats' on data 'ownership' regarding data on the website from other sources (e.g. footnote that data was from external source – to protect integrity of StatsCan and CCEI for data sources were quality has not been confirmed).

Action Item

  • Explore with IT and dissemination team possibility of some type of caveat and/or clarity on source of data obtained through CCEI search engine – to flag those coming from external sources.

Theme 4: Initial priority data gaps participants raised

Participants identified the following preliminary 'data priorities'/'issues' as part of the conversation – as well as need for CCEI to maintain 'policy neutrality' – again within the context of protecting the integrity of the program:

  • More timely data
  • Innovation and commercialization of new technologies (e.g. carbon capture and storage, battery storage technology, etc…)
  • Cost of energy and implications for interprovincial/international trade
  • Retail and wholesale energy costs and impact on energy poverty/affordability
  • Evolution of energy demand
  • Establishing consistency in data – through established and agreed upon protocols of data quality

These will be added to other initial data gaps/priorities CCEI has been gathering from stakeholders in previous engagement to date.

Action item

  • CCEI team to compile a preliminary list of priorities based on previous engagement with stakeholders and early feedback from advisory body to initiate an early discussion at the July 2020 meeting (and follow-up discussion in September 2020).

Forward agenda: Inaugural CCEI External Advisory Committee meeting in late July

  • Finalizing Terms of Reference and Discussion on Committee Roles/Deliverables
  • CCEI Presentation and discussion on Engagement and Communications Strategies
  • CCEI Presentation and discussion on Early Priorities (as we see them) and feedback from Committee

Response Rate Sawmills, production of wood chips by Geography Quantities produced

Table 2: CV's Sawmills, production of wood chips by Geography
Quantities produced (thousands of oven dried metric tons)
Geography Month
201901 201902 201903 201904 201905 201906 201907 201908 201909 201910 201911 201912
Canada 0.86 0.87 0.86 0.84 0.87 0.88 0.83 0.82 0.82 0.81 0.81 0.76
Newfoundland and Labrador 0.97 0.97 0.97 0.97 0.96 0.97 0.96 0.86 0.83 0.86 0.84 0.96
Prince Edward Island 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Nova Scotia 0.76 0.78 0.61 0.76 0.79 0.79 0.97 0.97 0.81 0.65 0.66 0.38
New Brunswick 0.97 0.97 0.85 0.85 0.86 0.86 0.86 0.78 0.76 0.80 0.46 0.69
Quebec 0.87 0.88 0.87 0.80 0.85 0.82 0.78 0.77 0.74 0.74 0.74 0.53
Ontario 0.74 0.73 0.79 0.76 0.73 0.78 0.78 0.75 0.73 0.72 0.74 0.71
Manitoba 0.97 0.96 0.95 0.95 0.93 0.98 0.97 0.96 0.97 0.88 0.96 0.96
Saskatchewan 0.99 0.81 0.99 0.99 1.00 0.99 0.78 0.80 0.80 0.82 0.79 0.81
Alberta 0.95 0.95 0.94 0.95 0.94 0.94 0.85 0.85 0.84 0.84 0.93 0.92
British Columbia 0.83 0.85 0.86 0.85 0.89 0.92 0.87 0.87 0.92 0.91 0.91 0.93
British Columbia Coast 0.94 0.94 0.95 0.94 0.93 0.94 0.92 0.89 0.93 0.92 0.93 0.92
British Columbia Interior 0.81 0.84 0.84 0.83 0.88 0.92 0.85 0.87 0.92 0.90 0.91 0.94
Northern Interior, British Columbia 0.80 0.89 0.81 0.81 0.85 0.92 0.83 0.87 0.93 0.93 0.93 0.98
Southern Interior, British Columbia 0.81 0.78 0.89 0.86 0.92 0.91 0.87 0.87 0.91 0.86 0.89 0.88

Canadian Research and Development Classification (CRDC) 2020 Version 1.0

Release date: October 5, 2020

Status

This standard was approved as a recommended standard on May 26, 2020.

CRDC 2020 Version 1.0

The Canadian Research and Development Classification (CRDC) was developed conjointly by the Social Sciences and Humanities Research Council of Canada (SSHRC), the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canada Foundation for Innovation (CFI), the Canadian Institutes of Health Research (CIHR), and Statistics Canada which is the custodian. This shared standard classification, inspired by the Frascati Model 2015 of the Organisation for Economic Co-operation and Development (OECD), will be used by the federal granting agencies and Statistics Canada to collect and disseminate data related to research and development in Canada. The CRDC first official version is the 2020 version 1.0 and it is composed of 3 main pieces: the type of activity or TOA (with 3 categories), the field of research or FOR (with 1663 fields at the lowest level) and socioeconomic objective or SEO (with 85 main groups at the lowest level).

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Estimation of excess mortality

Introduction

There are a number of indicators that are useful for monitoring the evolution and the impact of a pandemic like COVID-19 in terms of fatalities. Excess mortality is considered a better indicator for monitoring the scale of the pandemic and making comparisons.Footnote 1Footnote 2 Excess mortality refers to the "mortality above what would be expected based on the non-crisis mortality rate in the population of interest."Footnote 3 Excess mortality also encompasses collateral impacts of the pandemic, such as deaths occurring because of the overwhelming of the health care system, or deaths avoided due to decreased air pollution or traffic.Footnote 4Footnote 5Footnote 6

Estimating excess deaths

There are a number of challenges associated with measures of excess deaths. The most important challenge is to properly estimate some level of expected deaths that would occur in non-Covid19 context as a comparison basis for the current counts of deaths.Footnote 1 Indeed, death is a statistically rare event, and important variations may be observed from year to year in the annual counts of deaths, in particular in the less populated provinces and in the territories. Moreover, yearly counts of deaths may be affected by changes in the composition of the population, in regard to age more particularly, and changes in mortality rates (e.g. improvement of mortality).

A second challenge is the difficulty to collect timely counts of deaths. In Canada, death data are collected by the provincial and territorial vital statistical offices. The capacity to provide death data to Statistics Canada in a timely manner varies greatly.Footnote 7 Moreover, it is possible that the pandemic imposes a burden on health care and other institutions that disturb the data collection process, although it could instead add pressure for accelerated collection. The incomplete coverage of the numbers of deaths makes it difficult to draw any conclusions on the extent of excess deaths in Canada that could be caused by the COVID-19 pandemic.

Beginning on May 13, 2020, Statistics Canada has been releasing provisional counts of excess deaths for 2020.Footnote 8 Although the data were published for transparency and as information to be tracked and updated regularly, the uncertainty associated with the baseline expected death counts and the incomplete coverage of the numbers of deaths made it difficult to draw conclusions on the extent of excess deaths in Canada that could be caused by the COVID-19 pandemic. Statistical models are used to obtain estimated death counts adjusted for incompleteness and to estimate baseline non-Covid mortality. Estimates of excess deaths are obtained by comparing adjusted counts with modeled baseline mortality for all weeks in 2020 up to July 4. A description of the models is provided in the next section.

Methodology

This section describes the distinct models used for estimation of baseline mortality and adjustment of observed death counts.

Estimating expected mortality

The model used to estimate the expected number of deaths is based on a quasi-Poisson regression model fit to weekly death count data. Adapted from an infectious disease detection algorithm developed by Farrington et al.,Footnote 9 which has been largely utilized in the context of mortality surveillance in recent yearsFootnote 10. Later modifications to the algorithm, originally implemented by Noufaily et al.Footnote 11 and further expanded by Salmon et al.,Footnote 12 that aim at addressing certain limitations of the model were also adopted in this implementation.

The model was implemented in the R programming language with the use of the surveillance package,Footnote 12 and was applied to weeklyFootnote 13 death counts (all-cause) spanning a selected reference period of approximately four years (2016-2019). Historical counts are a combination of published death data from the Canadian Vital Statistics Death Database (2016-2018) and provisional death counts (2019) coming from the National Routing System (NRS). Estimates of expected deaths are produced for all weeks of 2020 up until the week ending July 4, 2020.

An overdispersed Poisson generalized linear model with a linear time trend and a seasonal factor is fit to the data. The seasonal component aims to represent the expected pattern across weeks that repeats from year-to-year, and consists of a zero-order spline term with 11 knots, representing 10 distinct periods within a given year.Footnote 14 The 10 periods are split between a single 7-week period corresponding to the current week being estimated and the 3 preceding and subsequent weeks, and 9 other 5-week periods corresponding to the rest of the year.

The model can be expressed using the following log-linear configuration:

log μt=α+βt+γc(t)

where μt is the expectation of the count in week t, β is the coefficient corresponding to the linear time trend, and γc(t) the seasonal factor for week t, with c(t) indicating the period in the year that week t belongs to. Footnote 12

The quasi-Poisson model relaxes the Poisson assumption that the variance must equal the mean. Instead, E(yt)=μt, and Var(yt)=ϕtμt, where the overdispersion parameter ϕ is estimated from the model using the formula:

ϕ^=max1n-p∑i=1nwi(yi-μ^i)2μ^i,1

where n is the number of weeks used in the baseline, and p the number of parameters in the model. A value of ϕ=1 implies no overdispersion (regular Poisson model), and ϕ<1 implies underdispersion (a rare occurrence, hence the condition on ϕ^). A weight w is assigned to each of the historical observations, based on the value of its standard deviation in an unweighted model. This reduces the influence of potential outliers on the estimation of the expected counts and corresponding prediction interval.

Finally, a 95% prediction interval is computed for the expected count in week t by assuming that the count follows a negative binomial distribution with mean μt and size parameter set to μt⁄(ϕt-1).

Adjusting death counts for incompleteness

Analysis of death by date (or week) of death is inevitably distorted by delays in reporting. This necessitates appropriate correction of the observed data to estimate the number of death that have occurred but not yet reported. The data received by Statistics Canada via the NRS contain information of day of death, date of report and some demographic information (e.g. age and sex).

Reporting delays are susceptible to change over time, and this is all the more true in a time of a pandemic. For this reason, the model estimates adjustment factors that are based on recent data, and uses different period for weeks that are during the pandemic and those preceding it. Weekly counts of deaths that occurred between December 29, 2019 and March 22, 2020 were adjusted based on the distribution of reporting delays estimated from death records received prior to March 22, 2020. Deaths counts for weeks between March 22 and July 4 were adjusted based on reporting delays observed between March 22 and August 7. In some jurisdictions, the level of data completeness of death records can be very low for the most recent weeks. Weekly adjusted counts are provided only for weeks where the estimated coverage rates satisfy a minimum threshold.Footnote 15

The method used for adjusting observed death counts was originally developed by Brookmeyer and DamianoFootnote 16 to model daily counts. It was adapted here to work on a weekly scale. The model was implemented in R programming language using code sent by the authors.Footnote 17 The number of deaths occurring on week t and reported in week t+d (i.e. with a delay of d weeks), Ytd, is modeled using the following Poisson regression model:

LOG(E(Ytd))=αt+βd

where αt represents the log-transformed preliminary reported count in week t, and βd is the term representing the adjustment for underreporting. Note that the right side of the equation is in a log scale so the underreporting adjustment can be seen as a multiplicative adjustment in the original scale. The adjusted number of deaths occurring in week t is then the observed deaths count divided by the estimated probability that the lag on the death being reported is less or equal to a maximum of x weeks, with x+t being the last observable week to report deaths, i.e. x is the maximum delay time in the dataset minus the week of the death t:

Adjusted number of deaths (t)=∑d=0dmax-tYtdP(delay≤dmax-t|time=t, delay≤dmax)

Estimating excess mortality

To calculate the weekly number of excess deaths, the baseline number of deaths in the absence of the pathogen (COVID-19) is subtracted from the observed (and adjusted for reporting delay) number of deaths for the period of interest. The method involves the following steps:

  • Quasi Poisson models are fit to the weekly death counts at the provincial and territorial level from January 1st 2016 to January 1st 2020 to obtain a baseline measure of the expected mortality.
  • Baseline deaths are projected for year 2020 until July 4.
  • Adjust for reporting delays the weekly counts of deaths that occurred from December 29, 2019 to March 22, 2020 based on the distribution of reporting delays estimated from death records received prior to March 22, 2020.
  • Adjust for reporting delays the weekly counts of deaths that occurred from the week ending March 22 to July 4 based on reporting delays observed between March 22 and August 7.
  • Apply an additional correction to the counts and prediction intervals for the period for March 22 to July 4. This correction factor is the ratio of the adjusted count for the week of March 22 based on the distribution of reporting delays estimated from death records received prior to March 22, 2020 to the unadjusted count for the same week.
  • Excess mortality is defined as the adjusted observed mortality minus the baseline for the period of interest.

The 95% prediction intervals surrounding estimates of excess deaths were computed by combining the variances from the two models. An empirical distribution of excess deaths is calculated by randomly pairing 10,000 estimates (replicates) from each model, as per the bootstrap method. The bounds of the prediction intervals represent quantiles of the empirical distribution. The method assumes independence between the two processes that are weekly mortality and collection of death records, but makes no assumption about the statistical distribution of excess deaths.

Validation

The computation of excess mortality requires the estimation of two greatly uncertain processes: how many deaths there should be in a given week, and how many deaths occurred that were not yet recorded at the time of estimation. The use of modelling for estimation of excess deaths aims at improving estimation but also, importantly, to reflect the uncertainty of these processes.

Validation of the models tend to show that they perform well in many regards. Expected counts tend to mimic the seasonal patterns typically observed during a year and follow the increase observed in past years (mainly due to population growth, particularly at old ages). However, because they captured over periods comprising several weeks, these seasonal patterns tend to be smoothed to some extent. For example, the application of time series models to weekly counts tend to produce more defined peaks, in particular in the month of January (likely due to influenza outbreaks). Another limitation has to do with the way prediction intervals are computed. In the model for estimating expected counts, the death counts are assumed to follow a negative binomial distribution, which is well adapted for modelling discrete counts data susceptible to present overdispersion. However, the bounds of the prediction intervals are defined as the quantiles of the negative binomial distribution, and thus do not reflect the variance due to parameter estimation. A better statistical representation would also account for uncertainty in parameter estimation.

The model for adjusting death counts was designed mainly for its capacity to capture recent trends in reporting delays. Experimentation with different time periods suggest that indeed, there have been changes in the pace at which deaths are registered in the provincial and territorial vital statistics database, at least in some provinces. However, the model assumes that there are no changes within the reference period considered. This is not guaranteed, in particular in a time of pandemic. Another limitation is that with the reference period is too short for capturing adequately potential seasonal patterns. The application of times series models to the data reveals the presence of some seasonal patterns in the coverage rates for some lags (number of days between date of death and report date). It is assumed that biases due to changes in reporting patterns are more important than those due to seasonality. Likewise, potential patterns of underreporting related to some specific days of the week, such as Sundays or holidays, were not considered.

Statistics Canada will continue to refine the methodology in an effort to better inform Canadians of the effects of the COVID-19 pandemic.

Response Rate for Sawmills, production of lumber (softwood and hardwood) by Geography

Table 1: Response Rate for Sawmills, production of lumber (softwood and hardwood) by Geography
Quantities produced (M.ft. b.m)
Geography Month
201901 201902 201903 201904 201905 201906 201907 201908 201909 201910 201911 201912
Canada 0.87 0.89 0.87 0.86 0.89 0.90 0.90 0.87 0.87 0.87 0.85 0.83
Newfoundland and Labrador 0.97 0.97 0.97 0.97 0.96 0.97 0.96 0.90 0.87 0.88 0.88 0.96
Prince Edward Island 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00
Nova Scotia 0.75 0.76 0.59 0.76 0.76 0.76 0.96 0.97 0.76 0.57 0.59 0.32
New Brunswick 0.95 0.96 0.92 0.93 0.93 0.93 0.93 0.89 0.88 0.91 0.54 0.80
Quebec 0.91 0.91 0.89 0.83 0.88 0.88 0.87 0.83 0.79 0.86 0.83 0.66
Ontario 0.80 0.79 0.78 0.79 0.75 0.79 0.79 0.78 0.77 0.76 0.80 0.77
Manitoba 0.89 0.81 0.80 0.81 0.81 0.86 0.89 0.89 0.88 0.83 0.85 0.87
Saskatchewan 0.99 0.75 0.99 0.99 1.00 0.99 0.71 0.66 0.73 0.73 0.73 0.74
Alberta 0.92 0.95 0.94 0.95 0.94 0.94 0.94 0.94 0.93 0.93 0.93 0.90
British Columbia 0.85 0.87 0.85 0.85 0.90 0.91 0.92 0.87 0.92 0.91 0.92 0.95
British Columbia Coast 0.95 0.91 0.91 0.91 0.94 0.95 0.93 0.87 0.94 0.92 0.93 0.92
British Columbia Interior 0.83 0.86 0.84 0.84 0.89 0.91 0.91 0.87 0.92 0.91 0.92 0.95
Northern Interior, British Columbia 0.88 0.88 0.80 0.80 0.86 0.92 0.92 0.89 0.92 0.93 0.93 0.98
Southern Interior, British Columbia 0.79 0.84 0.89 0.88 0.93 0.90 0.91 0.86 0.92 0.89 0.91 0.92

Data Visualization: An introduction

Catalogue number: 892000062020014

Release date: September 23, 2020 Updated: December 21, 2022

This video addresses the data visualization competency. By the end of this video, you should have a deeper understanding of data visualization and how it can be used to present data in an interesting and aesthetically pleasing way.

We will go over when it should be used, and give you some examples of the different types of data visualization techniques that exist.

Data journey step
Tell the Story
Data competency
  • Data visualisation
  • Storytelling
Audience
Basic
Suggested prerequisites
N/A
Length
10:54
Cost
Free

Watch the video

Data Visualization: An introduction - Transcript

(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "Data Visualization: An introduction")

Data Visualization: An introduction

Welcome to part one of a multi part series on data visualization. This video will provide an introductory overview of data visualization, and how to use it to tell your story.

Learning goals

This video addresses the data visualization competency. By the end of this video, you should have a deeper understanding of data visualization, and how it can be used to present data in an interesting and aesthetically pleasing way.

We will go over when it should be used, and we will give you some examples of the different types of data visualization techniques that exist.

Steps of the data journey

This diagram is a visual representation of the data journey. From collecting the data to cleaning, exploring, describing and understanding the data, to analyzing the data and lastly to communicating with others the story the data tell.

Step 4: Tell the story

Data visualization can occur at different steps of the data journey, depending on what you're using it for. In this video, we'll be focusing primarily on how to present data in a way that helps tell the story.

Data Visualization

(Diagram of the Steps of the data journey: Step 1 - define, find, gather; Step 2 - explore, clean, describe; Step 3 - analyze, model; Step 4 - tell the story. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)

Data visualization is the graphical representation of information and data. It is a combination of art and science as it uses tools such as charts, graphs and maps to make trends and patterns that might be hidden in a large data set much easier to understand.

Why use data visualization?

But how does data visualization make trends and patterns easier to understand?

Vision is such an important part of how we experience the world. Perhaps because it's how we've always survived. How we've found food, avoided threats, created art that preserves our culture and histories. And since the brain absorbs and processes visual information faster than any other stimuli, presenting information through graphics can be incredibly effective.

So it only makes sense that as technology has evolved, so would the way we present information we're trying to share with the world.

Presenting data

(4 images where, starting from the left, an apple pie, cherry pie, blueberry pie and "other" pie are sorted with a squinting face with tongue out emoji as a 5th image on the far right.)

For example, think about the following question: What is the most popular kind of pie? If you really wanted to know the most popular type of pie in your hometown, you might decide to conduct a survey.

This survey would ask everyone in town what kind of pie is their favourite. Apple? Cherry? Blueberry? Some other flavour? And finally, an option for people who really just don't like pie at all. Once you've acquired your data, there's several ways to communicate the results.

Option 1: Text

The first option is text. You could consider creating a written report describing the figures that read something like "of the 100 people surveyed, 40 preferred apple pie. 30 preferred blueberry and 20 preferred cherry. Additionally, five people chose a flavor other than those in the list, and five said they didn't like pie at all."

Option 2: Table

(Image of a table where the left and right columns lists the different pie flavours and the count of respondents preferring said flavour, respectively: "apple = 40";"bleuberry = 30";"Cherry = 20";"Other = 5";"I don't like pie = 5";"Total = 100".)

In this situation, where we're just trying to find out the most popular pie flavor. We might decide that reading a full analysis of the results is unnecessary.

This is where the option to receive the exact same results in a table, could be preferable. When reading a table, it's all about the numbers. Here we can clearly see that most people prefer apple pie without having to take the time to read through a lot of text.

So, a good thing to note here is that when you're trying to compare more than two numbers, you will probably want to look into presenting your data in a more visual way, rather than textual.

Option 3: Visual

(A series of images with 4 apple pies; 3 bleuberry pies; 2 cherry pies & half a pie for those who like other pies and the other half for those who do not like pies.)

A third way to present the results of our pie survey is without many words or numbers at all. Option three is where data visualization comes in. From this picture it's instantly clear that apple pie is the most popular.

Types of data visualization

(Simplified image of a series of different types of data visualizations: (left) Graphics; Charts; Maps; Tables; Pictographs; Infographics; Dashboards (Right).)

There are many different ways of presenting data visually, such as, graphs, charts, maps, tables pictographs, infographs and dashboards. On the next few slides will look at what each one is best at showing.

Scatter plot

(Text on screen: Showing relationship between two things)

(Image of a Scatter plot on display with the titltle on top:"Total revenue from of ice-cream sales, 2019 ($CAD)".The vertical(y) and horizontal(x) axis represent the proportion of the revenue ($CAN) and temperture (Celsius), respectively.)

A scatter plot is great for showing the relationship between two values. In this graph we can clearly see the relationship between temperature on the horizontal axis and ice cream sales on the vertical axis. We can see how ice cream revenues increased with increasing temperatures.

Line graph

(Text on screen: Showing trends through time)

(Image of a line graph on display with the titltle on top:"Canada's official poverty line".The vertical(y) and horizontal(x) axis represent the proportion of the population (%) and year (year), respectively.)

A line graph is a good way to show how something changes over time. This one shows how Canada's official poverty line has been declining in recent years from 12.1% in 2015 to 8.7% in 2018.

Bar Chart

(Text on screen: Showing a comparison between several things)

(Image of a bar chart on display with the titltle on top:"Cannabis use in the past three months by age, Canada - Fourth quarter 2019".The vertical(y) and horizontal(x) axis represent the proportion of cannabis users (%) and age group (year), respectively. The left most bar to the right most bar, represent the age groups: "15 to 24"; "25 to 34"; "35 to 44"; "45 to 54"; "55 to 64" and "65 and over".)

A bar chart is better when you want to compare different groups of things. Here we compare the use of cannabis among Canadians by age group. The chart clearly shows that cannabis use is higher among those in the younger age groups compared with older age groups.

Pie chart

(Text on screen: Showing the composition of a whole)

(Image of a circular pie chart tittled on the top: Six provinces cultivated "vinifera and french hybrid" grapes for winemaking in 2018. The pie chart is composed of 3 asymetric slices.)

A pie chart is the perfect tool for showing the composition of a whole, or the distribution of something. Here, we see that in 2018 Ontario produced more grapes for winemaking than all the other provinces combined.

Maps

(Text on Screen: Putting data into geographical context)

(Image of the map of Canada where each province has a different gradient of bleu representing the unemployment rate where the darker bleus represent a higher unemployment rate in percentage points. Dark regions are areas with no data.)

Here is an example of a map being used as data visualization. It shows how the job vacancy rate differs across provinces. The job vacancy rate for each province in Canada is indicated by the shading on the map.

Tables

(Text on Screen: Used to show many categories, and provide more detail and precision than many other data visualization methods)

(Image of a table where the left most column represents the age group; the middle and right major columns represent "All families with children" and "Total children in all families", respectively. Both major columns contain sub columns representing the years 2015; 2016 and 2017.)

Tables are used to show many categories and provide more detail and precision than many other data visualization methods. In this table we see the number of families with children compared to the total number of children in all families for different age ranges of children.

Pictographs

(Text on Screen: Simple but instantly interpretable)

((Reuse of the pie survey) A series of images with 4 apple pies; 3 bleuberry pies; 2 cherry pies & half a pie for those who like other pies and the other half for those who do not like pies.)

This data visualization from the pie example is a pictograph. A pictograph is the representation of data using images. This is one of the simplest ways to represent statistical data. The popularity of different kinds of pie is represented by the number of pies. In this pictograph, each pie represents 10 individuals. While a pictograph has very low precision, our brains interpret the message instantly.

Infographics

(Text on Screen: Used to tell a comprehensive data story)

(An image containing an infograph Titled: "Family matters - information on the splitting of householde tasks. Who does what ?". Infograph contains facts and conclusions on the subject mater.)

An infographic is several data visualizations put together to tell a more comprehensive data story. Typically, an infographic portrays the state of something at a particular point in time. Like a poster.

In this example, several data points are put together to tell a story about who does the chores in a family. From this infographic we learn that some chores are done equally by men and women, like dishes, shopping and organizing the social life. While laundry and meal prep are more likely to be done by women, outdoor work is most likely done by men.

Finally, the infographic reveals that the distribution of tasks depends on who's in the labor force at the time.

Dashboards

(Text on Screen: Used to inform business decisions and are updated at regular intervals)

(An image containing a dashboard where tables, charts and graphics to display several issues related to human resources)

A dashboard is several data visualizations put together, often to inform business decisions. Dashboards are usually updated regularly and show changes over time. The colour, size, and position of the individual graphics are used strategically to focus attention on different aspects.

This dashboard for example uses tables, charts and graphics to display information to manage human resources.

How to choose the right visualization

The right visualization depends on several factors.

What type of data do you have? Are their relationships in the data? Or are they changing over time? Are you making comparisons or showing the composition of something? And who's your audience? What story do you want to tell them? Are differences by geographic region important to them? How much precision do they want or need? Is your audience making business decisions based on the information you're sharing? Or, is it simply to inform?

On the previous slides you saw some different types of data visualizations and what each one can be used for.

Recap of Key points

(Text on Screen: Data visualization is the graphical representation of information and data.; Vision is an important part of how we experience the world.; There are many different ways of presenting data visually.)

In this video, you learned that data visualization is the graphical representation of information and data.

A picture truly is worth 1000 words. Just make sure you choose the right picture to accurately represent your data and effectively get your message across. Watch for more videos in this series featuring good practices for data visualization.

(The Canada Wordmark appears.)

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The Data Journey: What you need to know for successful navigation

Catalogue number: 892000062020007

Release date: September 23, 2020 Updated: October 22, 2021

In this video you will learn about the steps and activities in the data journey, as well as the foundation supporting it.

No previous knowledge is required. The data journey represents the key stages of the data process. The journey is not necessarily linear. It is intended to represent the different steps and activities that could be undertaken to produce meaningful information from data. Not everyone who uses data will do all of these steps.

No previous knowledge is necessary.

Data journey step
Foundation
Data competency
  • Data discovery
  • Data management and organization
Audience
Basic
Suggested prerequisites
N/A
Length
04:37
Cost
Free

Watch the video

The Data Journey: What you need to know for successful navigation - Transcript

(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "The Data Journey: What you need to know for successful navigation")

Data 101: Data Journey

The training videos in this series are organized around a data journey. This video tells you what you need to know for successful navigation.

Learning goals

In this video you'll learn about the steps and activities in the data journey as well as the foundation supporting it.

No previous knowledge is required.

Steps of a data journey

(Diagram of the Steps of the data journey: Step 1 - define, find, gather; Step 2 - explore, clean, describe; Step 3 - analyze, model; Step 4 - tell the story. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)

The data journey represents the key stages of the data process. The journey is not necessarily linear. It is intended to represent the different steps and activities that could be undertaken to produce meaningful information from data.

Not everyone who uses data will do all of these steps, for example. You might already have gathered and cleaned data ready for analysis. Therefore you might only need to do the last two steps.

Step 1: Define, find and gather

(Diagram of the Steps of the data journey with an emphasis on "Define, find, gather".)

(Text on screen: Showing relationship between two things)

The first step is to define the question you need to answer or data gap you need to fill. Next is to find the right data to answer that question, or fill that data gap. If such data doesn't exist, you may need to figure out a way to gather it, like through a new survey, for example. In this first step you will use one or more of the following competencies: data discover, data gathering and/or data management and organization.

Step 2: Explore, clean and describe

(Diagram of the Steps of the data journey with an emphasis on "Explore, clean, describe".)

Once you have defined the need and found the data, the next thing is to get to know it. If you're already familiar with the data, then you might know what to expect. On the other hand, if the data is new to you, then you should spend some time exploring the formats variables and looking for errors and missing values. It may be necessary to clean the data before using it for analysis. It is important to document what you found and what you did to clean the data.

The product at the end of this step is data ready for analysis. In this step you will use one or more of the following competencies: data cleaning and or data exploration.

Step 3: Analyse and model

(Diagram of the Steps of the data journey with an emphasis on "Analyze, model".)

If you were doing analysis to describe a phenomenon, draw conclusions about a population or make predictions about future events, then your data journey continues. The purpose of doing analysis and modeling is to use statistical techniques to turn the data into information to provide meaningful insights that address your previously determined information needs. In this step, you'll use one or more of the following competencies: data analysis, data modeling and/or evaluating decisions based on data.

Step 4: Tell the story

(Diagram of the Steps of the data journey with an emphasis on "Tell the story".)

The statistical information that comes from analysis and modeling is easier to digest if it is presented in some sort of story. It could be a research paper, an infographic, a briefing for management, or some combination of these and other data presentation methods. In this step, you'll use one or more of the following competencies: data interpretation, data visualization and/or storytelling.

Build your data journey on a solid foundation

(Diagram of the Steps of the data journey. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)

In order to successfully follow the steps of the data journey, it is essential to build your work on a solid foundation of stewardship, metadata, standards and quality.

Stewardship encompases all activities to govern, safeguard and protect data.

Metadata should describe all the processing and manipulation that the data has undergone.

Standard methods, practices and classifications should be applied throughout.

Quality should be proactively managed throughout the process and relevant quality indicators should accompany all deliverables.

Recap of key points

The data journey steps are: defined, find, gather; explore clean, describe; analyzing, model, and tell the story. Not everyone who uses data will do all these steps themselves. For example, you might get already gathered and clean data ready for analysis. The data journey is supported throughout by a foundation of stewardship, metadata, standards and quality.

Further learning

You are welcome to watch the videos in any order you choose. If you're not sure where to go next, we recommend Types of Data and Gathering Data.

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