Short description of Statcan's new Data Science Accelerator, a data science team specializing in the implementation into production of ML models.

1. There’s no denying that the past two years have been
booming with innovations in data science, artificial
intelligence (AI) and machine learning (ML). In the private
sector, significant investments were made in driverless
vehicles, augmented reality applications, and smart cities.
At Statistics Canada, scanner data from major retailers
were implemented into the Consumer Price Index (CPI)
and the Monthly Retail Trade Survey programs. And, in
2017, the Economic Statistics Field initiated something
completely new at the agency: a Data Science Accelerator
(DSA). It’s mission? To do just as its name suggests:
propel data science forward.
Enterprise
Statistics Division
propelling data
science forward
By Meagan Sylvester, Editor
Structured as a“hub and spoke,”the DSA can be thought
of as an internal start-up that supports other start-ups—
taking some ideas from the private sector and applying
them here.
Canada’s leadership role
While StatCan may be new to the exploration of AI,
Canada has long been a leader in this field, particularly in
Montréal and Toronto—two of Canada’s most active AI
cities. In 2017, these centres were gaining momentum and
becoming hubs of AI research and development.
“The name was selected for a very specific reason: the DSA
has trained data scientists, with expertise in big data, ML
and AI, which isn’t generally the case across most divisions
at StatCan,”said Sevgui Erman, Assistant Director of the
Enterprise Statistics Division.
“The vision is to have more AI experts throughout the
agency, which could mean training our own staff or
bringing in new people. There’s potential to drastically
change the way we produce information for Canadians,”
she added.
The DSA is pioneering new ways of working using
advanced technologies to deliver quick results, build
capacity, provide advice, and coordinate data science
projects across the agency.
Enhancing our processes
Internally, part of the DSA’s mission is to build data
science capacity by solving concrete problems, which,
in some cases, means modernizing and automating
our processes. AI, however, will not replace human
expertise—it will enhance it, speeding up our processes,
increasing our capacity to respond to users, and
improving our reach and relevance to Canadians.
“In Montréal, an entire AI ecosystem has been created
at the Université de Montréal and McGill,”said Ms.
Erman.“Today, the city has the largest concentration
of AI researchers in the world with large government
investments, so it was the right time for us to get started.”
And we have.Taking this leadership role even further,
Statistics Canada has joined forces with the Privy
Council Office andTreasury Board Secretariat to co-
lead the development of a Public Service Data Strategy.
The strategy, being spearheaded by the agency’s
Modernization Secretariat, aims to do everything it can to
increase the use of data, and make it easier for people to
use statistical information to design programs and policies.
“It can be onerous to do record linkage, so if there’s
anything we can do to lighten the load and build more
capacity, we’re interested,”said Richard Evans, Director
General of the Industry Statistics Branch.“Today, we’re
in a position to go one step further by applying more
sophisticated models to our methods. It’s an important
blending of the old and new.”
Living proof
To date, nearly 30 use-cases for data science have been
identified. As just one example, the DSA is working on an
Agriculture Crop Yield Estimates project to improve its
timeliness around field crop data and seasonal estimates.
Rather than sending surveys to farmers, the unit is
exploring the possibility of combining satellite images
with administrative sources and AI to predict what the
farmers are growing—all within the same season.
“There’s a tremendous opportunity with these big data
sources: they could be available instantaneously,”Mr. Evans
added.“Traditionally, we would mail the questionnaire and
give the respondent a week or two to reply. If this data were
on the cloud, we could jump on them immediately and
publish a result as quickly as possible.”

2. “There are days when it’s a bit rough going. Those first
winters were terrible, but look at what we’ve become,”
Mr. Evans said.“This time, it won’t take 300 years. With
the right IT, the best people and leaner processes,
StatCan will definitely be the place to be for data science
and AI within the federal family.”
Note from the Data Science Accelerator
The success of these projects relies on strong
collaboration with our Methodology and IT partners.
Bringing ML models to production requires active
participation from all stakeholders.The DSA projects
are multi-disciplinary and are designed to build
data science capacity within the agency.We have
been enjoying great support from all that have been
involved, and on our end, we strive to share with other
teams any advancements we make.
The DSA would like to thank their subject matter
partners—whose engagement and creative
ideas are the fuel for their projects—and senior
management, for their continued support. Ms.
Erman would also like to thank the entire DSA
team, who are the true drivers of these AI projects.
The DSA is already using the cloud’s massive IT
infrastructure to move the crop yield project forward,
thanks to the efforts of StatCan’s IT team.
The future of data science at StatCan
Looking ahead, the team views the development
of this important field as one similar to the story of
Canada—a country with humble beginnings, but
one that has thrived and prospered.
Source: Statistics Canada, Modernization Bulletin, December 2018