Open Data Users’ Top Priority Datasets on data.gc.ca
by Alice Born (Guest Blogger)
Open data users voted the North American Industry Classification System (NAICS) and the National Occupational Classification (NOC) as their top priorities to be made available via the Open Data Portal, data.gc.ca, and they are now up.
Making these statistical classifications available in .CSV, an open format, benefits the open data community and the general public. Users can now integrate or mash up data with other government data for analysis, or to build applications that use these classifications in a variety of ways.
Read on for more detail about these classifications.
The North American Industry Classification System (NAICS) is one of the best known classification systems. NAICS was first developed at the time of the North American Free Trade Agreement, and continues to be widely used today. It provides a consistent framework for collecting, tabulating, presenting and analyzing industry statistics.
If you want to start a new business, you must register with Canada Revenue Agency and request a business number. As part of that process, you need to scan the NAICS hierarchy for your industry, and figure out which NAICS code is the best match for your business.
Associations want to make sure they are well represented in NAICS to keep a handle on their industry. Labour organizations care because NAICS is used to show the distribution of labour across the economy.
Not every industry is the same in every country. For example, maple syrup production is important to Canada; less so the United States and Mexico. Thus, you will see a little US or CAN or MEX superscript on some industries to denote where the codes differ among the countries. But there are many trilateral classes.
As Director of Standards Division, I am often asked about the International Standard Industry Classification. While Canada, the United States and Mexico may have developed and adopted NAICS for the purposes of improving data comparability, the three countries also consult and contribute to the international classification system. The last few rounds of revisions have brought NAICS closer to the international standard.
The National Occupational Classification is the nationally accepted taxonomy and organizational framework of occupations in the Canadian labour market.
NOC 2011 is the result of extensive occupational research, analysis and consultation conducted across the country. In the decade since the first structural NOC revision in 2001, the labour market has evolved significantly. Technological innovation, further globalization of the economy and restructuring of the workplace have affected many occupations.
Designed to classify occupation information from statistical surveys, NOC is also widely used to compile, analyze and communicate information about occupations. Occupational information is crucial for providing labour market and career intelligence, for skills development, for occupational forecasting, and for analyzing labour supply and demand, employment equity and many other programs and services. It is a standardized framework for organizing the world of work in a manageable, understandable and coherent system.
NOC’s basic principle is to classify the kind of work performed. Occupations are identified and grouped primarily by the work usually performed, as determined by the tasks, duties and responsibilities of the occupation. Multiple factors—the materials processed or used, the industrial processes and equipment used, the degree of responsibility and complexity of work, as well as the products made and services provided—have been used as indicators of the work performed when classifying jobs into occupations and occupations into groups.
The NAICS and the NOC are revised every five years. Standards Division consults widely with Canadians to ensure these periodic revisions keep pace with an evolving industrial structure and changing labour market.
If you find these open data formats of the NAICS and NOC classifications useful, Statistics Canada has other standard classifications that may interest you. As well, over the course of the next year, Statistics Canada will be releasing machine-readable versions of these classifications:
Please feel free to use the comment feature below to send your ideas or questions.
Alice Born, Director