Open Data Professional Certification
Certification Criteria
Point-based Evaluation & Portfolio Review
The working group is proposing a point- and portfolio-based approach to evaluating applicants for certification. The idea of an exam has also been discussed, and is something that we need feedback on from the broader community about.
Point values based on student activity hours, or course contact hours would be assigned to the various types of educational experiences including formal, accredited institutions, or informal courses, conferences and webinars. Each level of certification will require different point minimums in the prescribed knowledge domains.
Point values would also be assigned to employment experience, non-employment experience, and possibly others that could be documented.
Additionally, applicants’ contributions to the broader open data community would be recognized through a large variety of opportunities and activities including conference participation, authoring courses and publications, student project involvement, mentorship, and even being an application reviewer for this certification program. Through this endeavour we also hope to develop an online community of certified professionals to exchange ideas and knowledge with each other, through which there could be some credit given for participation.
A code of ethics will be developed with the community that applicants would be required to agree to and uphold, as well as possibly a letter or reference for some levels of certification.
Education
Formal
Informal
Conferences/seminars/webinars
Experience
Employment (for some levels)
Projects
Other
Contributions to the Open Data Field/Volunteer Projects
Conference volunteer, organizer, presenter, panel participant, webinar presenter, etc.
Publications/articles
Student project host/advisor
Mentor
In-kind consultation
Certification application reviewer
Participation in Open Data Community of Practice
Code of Ethics Sign-off
Required for levels 2 to 4
Reference letter (for some levels)
Required for levels 2 to 4
Other (?)
Certification Levels
The working group is proposing a series of certification levels, with increasingly more stringent requirements.
Level 1 - Certified Open Data Explorer - “CODE”
The first level we envision is a low-barrier entry level called “Explorer”. This level is intended for users and proponents of open data that may not have any professional experience. It gets them in the door and started on a path to more advanced certification.
The applicant will be required to complete a series of basic online training modules and successfully pass a quiz to show a basic level of understanding of open data, and some experience working with it.
Low barrier entry as an open data proponent/user, no professional experience required (students, casual users, community group participants/leaders).
Basic training modules + quiz.
Experience downloading, analyzing, working with open data.
Becomes a member of the community of practice.
Membership in both CODS & GOOD.
Recognition of basic level of understanding and experience with open data.
Level 2 - Certified Open Data Practitioner - “CODP”
This level would require everything from the Level 1, plus a certain amount of education and experience points in the Open Data Fundamentals knowledge domain.
This level would require at least one year of employment and/or other equivalent project work experience in open data, as well as applicant agreement and sign-off on the code of ethics.
There is the possibility for an applicant to skip the Explorer level with the right demonstrated experience and education.
Explorer level plus…
Education: Open Data Fundamentals
Experience: 1 year or more employment or other project work (paid or volunteer)
Code of Ethics Sign-off
“Springboard” option based on project experience (subject to review panel approval)
Level 3 - Certified Open Data Architect- “CODA”
This level would entail all of the requirements of Level 2 plus education and experience in Data Governance and Policy, Data Management and Technology, and Open Data Standards and Interoperability knowledge domains.
Applicants of this level would require two or more years of equivalent professional experience, and an employer or certified member reference letter.
All of Practitioner level plus…
Education:
Data Governance and Policy
Data Management and Technology
Open Data Standards and Interoperability
Experience: 2+ years professional equivalent
Code of Ethics Sign-off
Employer or other open data professional reference letter
“Springboard” based on project experience (subject to review panel approval)
Level 4 - Certified Open Data Manager - “CODM”
This level of certification is intended for professionals that manage medium to large scale open data programmes. Applicants could possess the experience and education required for any of the previous levels, however they may desire to practise in the “management” side as opposed to a technical lead role.
An applicant to this level would need all of the requirements of Level 3 plus minimum points in Open Data Leadership and Advocacy, and Open Data Project and Program Management knowledge domains.
Aspiring Certified Open Data Managers would require four or more years of professional equivalent experience in the open data field.
All of Specialist plus…
Education:
Open Data Leadership and Advocacy
Open Data Project and Program Management
Experience: 4+ years professional equivalent
Code of Ethics Sign-off
Employer or other open data professional reference letter
“Springboard” based on project experience (subject to review panel approval)
Re-certification
The working group is considering a re-certification requirement every 2 to 3 years, with continuing education, experience, and volunteering activities.
Certification Costs
The working group is proposing the following costs for certification:
Level 1 (Explorer) - $120
Upgrade to Level 2 - $280
Upgrade to Level 3 - $380
Upgrade to Level 4 - $480
Level 2 (Practitioner) - $400
Upgrade to Level 3 - $100
Upgrade to Level 4 - $200
Level 3 (Architect) - $500
Upgrade to Level 4 - $100
Level 4 (Manager) - $600
Feedback
Community Feedback Results
Coming soon…
Working Group
The Open Data Certification Program Working Group members are:
Eric Carr, County of Dufferin
Eugene Chen, Chair of CODS
Paul Connor, Executive Director of CODS
Ben Dick, City of Ottawa
Kevin Farrugia, Hamilton Police Service
Jury Konga, Director, GO Open Data Association
Jamie Leitch, Board Chair GO Open Data Association
Steven Way, Mohawk College
Contact
For questions or comments please contact:
Paul Connor
Working Group Co-Chair
Executive Director
Canadian Open Data Society
admin@opendatasociety.ca
Jamie Leitch
Working Group Co-Chair
Board Chair
GO Open Data Association"
chair@go-opendata.ca
Overview
The Canadian Open Data Society (CODS) and the GO Open Data Association (GOOD) are excited to launch a new certification program for current and aspiring open data professionals in Canada and beyond!
This innovative program is designed to validate essential skills and experience, giving practitioners, their employers, and peers confidence that they’re equipped to make impactful contributions in open data initiatives.
With an accessible structure and rigorous standards, the certification provides real value, helping open data programs achieve success and benefit everyone.
Throughout 2023 and early 2024, GOOD has hosted community consultation sessions across Canada, gathering valuable insights from open data experts. These sessions have helped shape the program, reinforcing its importance and aligning it with the needs of today’s open data community.
Why open data?
Open data is ready to take its place as a vital check and balance in our society, a Sixth Estate if you will, alongside our judiciary (Supreme Court/Federal Court), legislatures (Parliament), executives (King-in-Council), news media (a.k.a the Fourth Estate), and non-mainstream viewpoints carried on blogs and social media (a.k.a. The Fifth Estate). It is capable of truly levelling the playing field among governments and their citizens, companies and their consumers, charities and their clients, and among researchers and volunteers everywhere.
Open Data is woven into our lives already via weather forecasts, electoral results analyses, crime reports, and charitable activities, as well as transit schedules embedded into online mapping applications. But a universe of actionable insights remains untapped in the data generated daily by fields as diverse as genealogy, scientific observations, public records, and program impact measurements. Publications and usage of, and insights from Open Data have serious untapped potential to transform the world, offering unlimited possibilities for social, economic and civic development.
Why create a certification programme for open data practitioners?
There are a number of reasons to create such a programme, and certify open data practitioners who publish and/or apply open data, were identified by community members, including:
It would validate that an individual has the requisite skills and knowledge to perform high-quality and high-impact work related to open data:
It can promote trust among organisations employing certified practitioners that they have an understanding of a particular aspect of open data publication and/or applications, whether it's Freedom of Information & Privacy, data ethics, change management etc.
It would provide a mechanism to generate trust in the processes used and quality of the data made available to end users:
Principles of open data would need to be followed in the release and use of open data can be assured by holding certified practitioners to a code of conduct requirement of certification.
It would increase the confidence of funding bodies that may underwrite programs for the publication, sharing and application of open data.
It would validate that an organisation which is mindful of the certification of its open data practitioners is working towards being more open and working with other organisations and citizens / consumers / stakeholders.
It can raise mainstream awareness of open data among organisations and the general public and the certification programme may thereby be established as a leader in this field, with greater value and positive impact realised i.e. to solve problems like climate change.
Standardised certification knowledge requirements can provide a roadmap for other organisations outside of government to provide open data, thereby increasing the total stock and value of open data globally.
It could also provide a cause-motivated on-ramp for non-technical and/or unskilled persons to gain data literacy and other valuable competencies with which they can secure employment and/or pursue public service.
It would provide successive goals for practitioners to strive for and bolster their continued interest in the subject.
It would validate leading practices, standardisation and quality of open data, increasing efficiency and interoperability in the field.
It would set a foundation for the expansion of open data as not just a (leading) practice but a field of study in its own right.
It would create a network that can be easily tapped into for practices, standards, practitioners and examples within the broader open data community.
It would attract new voices to the open data space.
Project Work Plan
Draft Code of Ethics Framework for Certified Open Data Practitioners/Professionals/Managers
As a Certified Open Data Practitioner/Professional/Manager, I acknowledge that my actions have a profound impact on the data landscape, society, and the organizations I serve. I commit to the highest ethical standards in my professional conduct, guided by the following principles:
1. Integrity and Honesty
Act with integrity and honesty in all professional relationships with open data, ensuring that it is handled, analyzed, and presented, accurately, truthfully and without manipulation.
Communicate any assumptions, limitations, or uncertainties that may affect the interpretation of the data.
Avoid conflicts of interest that could impair my objectivity or professional judgement with respect to my work with open data.
2. Transparency and Accountability
Foster transparency in all aspects of data management, including the collection, processing, and dissemination of data.
Utilize tools such as clear and accessible documentation that enables others to understand the sources, methodologies, and decisions behind the data I manage or present.
Be accountable for my actions and decisions, acknowledging and correcting, as soon as reasonably possible, any errors or omissions.
3. Respect for Privacy and Confidentiality
Ensure that the collection, use, and handling of personal and sensitive data strictly comply with relevant privacy laws, regulations, ethical standards, and policies.
Implement measures to protect personal information from unauthorized access or disclosure and seek consent where necessary for the use of such information.
4. Professional Competence
Commit to maintaining a high level of professional competence by regularly seeking out education and development opportunities that align with the Knowledge Domains.
Engage with the broader open data community to stay updated on new technologies, practices, and legal requirements, ensuring that one remains a trusted source of expertise in the field.
Take responsibility for the accuracy and quality of my work, striving for excellence in all my data-related activities.
5. Fairness and Inclusivity
Promote equality and inclusivity in access to open data, ensuring that data is shared and presented in ways that are useful and understandable for all communities.
Strive to reduce data inequalities, particularly addressing the needs of underrepresented or marginalized groups.
Advocate for removing barriers to access and actively work to minimize any biases or exclusions in the datasets I handle.
Recognize and uphold data sovereignty for Indigenous communities.
6. Ethical Data Use
Encourage and demonstrate responsible and ethical use of open data, with attention to avoiding misuse, harm, or the promotion of discriminatory outcomes.
Consider the impacts of publishing data, weighing the potential benefits and risks.
Refrain from supporting or participating in any use of data that is illegal, violates human rights, encourages unethical behaviour, or compromises public safety.
7. Environmental and Social Responsibility
Consider the environmental and social consequences of the data projects I engage in
Acknowledge the importance of open data in addressing urgent societal challenges.
Be an advocate for the responsible and ethical use of data.
8. Obligations to Society
Recognize that data is a public good and will use my expertise to benefit society, ensuring that, for instance, open data supports transparency, accountability, and informed decision-making.
Use open data to empower individuals and communities, fostering public trust and enhancing democratic participation.
Advocate for ethical and equitable open data policies that promote the well-being of all citizens and contribute to positive societal change.
Acknowledge my responsibility to listen to feedback and criticisms, and be responsive to concerns, adapting my practices to better align with societal needs and values when possible.
9. Obligations to Employers and Clients
Use my skills to the best of my ability to support the mission and goals, of my employer or client, while upholding the principles of transparency and ethical responsibility in the dissemination of data
Maintain confidentiality and protect proprietary or sensitive information, except when disclosure is legally required or ethically justified to protect the public.
Provide honest, independent, objective analysis and advice, ensuring that my professional recommendations are based on sound data and best practices, free from bias or undue influence.
10. Obligations to Open Data Professionals and the profession
Respect other certified open data professionals, valuing their expertise, perspectives, and contributions to the field.
Acknowledge and give credit to the work of other open data professionals, refraining from misrepresentation, plagiarism, or misappropriation of their ideas, data, or methods.
Support the professional growth of my peers by offering guidance, mentorship, and constructive feedback when appropriate, contributing to the continuous improvement of the broader open data community.
Hold paramount the ethical standards of the profession, leading by example and advocating for the responsible and ethical use of open data within the broader community.
11. Respect for Intellectual Property
Respect the intellectual property rights of others, ensuring that data, methodologies, and tools are properly attributed.
Advocate for the use of open licenses where appropriate, balancing the rights of creators with the need for public access and reuse.
Comply with all applicable intellectual property laws, and refrain from unauthorized use or reproduction of data and resources owned by others.
12. Collaboration and Knowledge Sharing
Actively contribute to the open data community, sharing knowledge, insights, and best practices to advance the collective understanding of open data principles.
Foster an environment of collaboration and mutual support among certified open data professionals, recognizing that advancing the field requires shared knowledge and diverse contributions.
Engage with stakeholders across sectors, recognizing the importance of cross-disciplinary collaboration in advancing the field of open data.
13. Legal and Ethical Compliance
Comply with all relevant laws, regulations, and professional standards governing open data and data management in my jurisdiction.
Engage in ethical decision-making, even in the absence of clear legal guidelines, always prioritizing the public interest and the integrity of my profession.
Report unethical or illegal data practices, whether observed in my work or the work of others, and advocate for corrective action.
Knowledge Domains
What are knowledge domains?
Knowledge domains are broader topics or subject areas and areas of expertise that a community of professionals agree someone should have to effectively work in the field of open data. They include the technical, ethical, policy, and governance core competencies relevant to the open data space.
Where do they come from?
Knowledge domains are developed and validated by the broader open data community, those people that work with, create, and publish open data. Best practices across all sectors of open data help to inform the knowledge domains, and together, form the idea of a “body of knowledge” for open data practitioners and users.
Why are knowledge domains important for Open Data Certification?
An agreed set of domains provides a standard framework for consistent skills and knowledge, which helps to ensure a high quality of practice across all sectors. Knowledge domains can help professionals in the field identify areas for growth and advancement, and ensure future endeavours are aligned with best practices. They demonstrate to the public a commitment to remain relevant in the changing space. Formal knowledge domains can provide greater assurances that the certified professionals meet a peer-recognized and expected standard of expertise.
How are knowledge domains applied to certification?
Applicants for certification will be required to demonstrate how their education and practical experience aligns with the knowledge domain. Candidates would not necessarily have to have experience in all sub-domains, but there will be minimum requirements and the experience they do have would have to align with the domains.
These knowledge domains would be refined and updated as the field of open data evolves to include emerging skills, technologies, and practices.
The broader open data community would be encouraged to provide feedback and help shape these domains to ensure they remain relevant. As our community advances, so would the body of knowledge that defines an “open data professional”.
Proposed Knowledge Domains
AI Disclosure
This content was created with the assistance of artificial intelligence (AI) tools, including ChatGPT. AI assisted in creating this content, and with writing style and grammar. All final content was reviewed, modified, and approved by human contributors.