Environmental Scan: Assessing Inclusionary Practice in Canadian Data Services

By Ali Milad

This article provides an overview of key findings from an environmental scan conducted by the We Count team in May 2020. The scan relied on information available through online sources to explore how data ethics are being taught, expressed, and implemented within Canada by three stakeholders in the data ecosystem: Postsecondary Education (PSE) Institutions, Data Service Providers, and AI Firms.

The Data Service Provider portion of the environmental scan identified 27 companies with operations in Canada and aimed to answer the following questions: Who are the most popular data service providers in Canada? What is the main messaging regarding their service approaches? How are they addressing bias management and inclusion?


  • 20 of 27 identified companies are US/EU based with operations in Canada
  • 11 of 27 identified companies reference bias management
  • 5 of 27 identified companies address inclusion or disability

A diverse space

Companies usually offer a combination of services, such as provision, analysis, and software services. As such, companies were categorized into five classes:

  1. Data Exchange (4% of identified companies)
    A marketplace where data sets are purchased from data providers, usually in raw formats. This is where 2nd and 3rd party data are purchased.

    Examples: AWS Data Exchange

  2. Data Provider (11% of identified companies)
    Any company that wishes to sell its data directly or on a data exchange.

    Examples: InfoCanada, Cleanlist, Direct Lead Data

  3. Data & Insight Provider (19% of identified companies)
    Offer 3rd party data sets and help curate and convert them into insights based on client needs.

    Examples: Pelmorex, Experian, AggregateIQ

  4. Data Research & Intelligence Partner (22% of identified companies)
    Conduct their own research, such as surveys and focus groups. May also collect and curate data. Companies in this space usually have a client-facing consultatory practice.

    Examples: Kantar, Ipsos, Vividata

  5. Data Solutions (44% of identified companies)
    Offer a combination of data services, such as a data management platform (DMP), data exchange, data sets, analytic toolkits, data onboarding, and business services.

    Examples: Adobe Audience Manager, Liveramp, Salesforce

It is worth noting that 85% of the identified companies help interpret and make sense of the data they provide (Data & Insight Providers, Data Research & Intelligence Partners, Data Solutions).

Examples of company messaging

Most of website messaging is targeted towards potential clients and focuses on better ROI, conversion, and decision-making:

“Getting the edge on your competition isn’t just about moving ahead. It’s about digging down and making sense of your data.”

— Adobe Audience Manager

“It is our expertise at extracting models and analytical insights from this data that enables us to drive revenue for your business.”

— InfoCanada

“We are on a mission to help organizations leverage the power of our data and identity solutions to deliver innovative products.”

— Liveramp

“We have a deep commitment to evidence-based decision-making for one reason, to help you achieve results.”

— Environics Analytics

Examples that address fairness and inclusion

Nielsen: In a 2019 article, Nielsen CEO David Kenney stated that the company employs trusted and fair data science principles, spending millions to ensure that every person is represented in measurements. Moreover, Nielsen has conducted studies and published reports on consumers with disabilities, some of which are available in Braille.

Ipsos: In an article that details the characteristics and risks of Big Data, Ipsos declared that they are careful not to mistake size for representativeness in data sets. Ipsos also published a study on home access for people with disabilities in the UK.

Salesforce: Salesforce’s dedicated webpage on AI ethics communicates their stance of ensuring that AI is safe and inclusive for all. To achieve this, Salesforce tests models with diverse data sets, and has hired a Chief Ethical and Humane Use Officer to develop a strategic framework for the ethical and humane use of technology.

Neustar: Neustar communicated the findings of a Federal Trade Commission report on inclusion and exclusion of Big Data, and addressed questions that marketers should consider in regards to the representativeness, accuracy, bias, and fairness of the data they use.

Maru/Blue: The company boasts reliable and representative insights through their patient ailment communities, which provide access to information on people with very specific health concerns.


  • Clients often expect help interpreting data; bias awareness is a needed skill for data service providers
  • Most Data Service Provider messaging is focused on appealing to traditional business values (ROI, conversion, better decision-making)
  • Data privacy and consent are the most common ethical concerns addressed; inclusion is rarely mentioned
  • A proactive stance on inclusion and ethics by data service providers can help educate clients on its business values
  • Data Management Platforms that automate data science functions can support inclusion if the platforms are designed inclusively from the start


We are pleased to share the findings of this scan through the following accessible formats:

We have also compiled lists of stakeholders identified during the scan for their proactive stance regarding data ethics and ethical AI: