Affiliations: Modern Statistical Methods and Data Science Branch, Statistics Canada, Ottawa, ON, Canada | E-mail: Eric.Rancourt@Canada.ca
Correspondence:
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Corresponding author: Modern Statistical Methods and Data Science Branch, Statistics Canada, Ottawa, ON, Canada. E-mail: Eric.Rancourt@Canada.ca.
Note: [1] Parts of this paper are stemming from presentations the author made at the 2018 IAOS Conference and the 2019 World Statistics Congress.
Abstract: As societies evolve through the data revolution, it is important that National Statistical Offices (NSOs) continue to devote efforts to fine-tune their approaches to maintain the vital trust they need to operate successfully. To do this, they must constantly re-invent themselves to remain relevant to new data needs and keep up with their high-quality standards. With new sources of information surfacing both in the public and private spheres, options are multiplying for NSOs to design new ways to gather and grow the data into information. As this is happening, new practices and new issues are emerging throughout the data life-cycle process. Operating beyond the sample survey paradigm, NSOs see themselves confronted with the need to anchor their new approaches in solidly defined and defendable frameworks. Further, as new data themes, methods and sources are considered, transparency becomes a central issue. Using Statistics Canada’s data life-cycle management model, this paper illustrates how the scientific approach can be leveraged to make transparency more explicit both in projects and management.
Keywords: Business process, data life-cycle, framework, modernization
