AODA IASR Data Collection Process: From Fragmentation to Accountability

Background

Led the redesign of an AODA IASR data collection process across 10 departments at OCAD University, aligned with the biannual public sector reporting cycle.

The goal was to consolidate data collection, improve transparency, and clarify accountability, while minimizing disruption to departmental workflows.

Overview

Accessibility compliance in large institutions involves multiple departments, distributed ownership, and complex reporting requirements.

Prior to this work, data collection was fragmented, inconsistent, and difficult to track. Contributors faced unclear responsibilities, scattered requests, and inefficient workflows.

The redesigned process introduced a structured system that simplifies data collection, strengthens accountability, and supports reliable reporting.

👉 Problem: A single system contained all requirements for all departments, creating information overload and making it difficult for contributors to identify what applied to them. At the same time, shared access introduced risks to data integrity, accountability, and traceability.

👉 Solution: AODA Data Collection System Redesign. Redesigned the process into a centralized, structured workflow that improves clarity, reduces cognitive load, and enables consistent data collection across departments.

System

Centralized Database
A Microsoft Lists system managed centrally to track submissions, monitor progress, and identify gaps.

Structured Communication Flow
Clear guidance emails and supporting documents defined responsibilities, timelines, and expectations.

Standardized Surveys
Forms captured consistent, structured responses across departments.

Pre-Survey Preparation
PDF versions enabled contributors to review and prepare responses before submission.

Sign-Off & Accountability
Formal acknowledgements ensured leaders confirmed the accuracy and completeness of their submissions.

Reason Why

Reduce Cognitive Load
Providing only relevant requirements per department made the process easier to navigate and complete.

Improve Data Integrity
Centralized control reduced risks of accidental edits, inconsistencies, and data loss.

Strengthen Accountability
Defined ownership and sign-offs made responsibility visible and traceable.

Thinking Process

1Research &Information Architecture

2Process Design & Structuring

3Validation & Iteration

Research & Information Architecture
Mapped the end-to-end workflow and identified key friction points: unclear ownership, confusing survey structure, and lack of traceability.
Process Design & Structuring
Reorganized the system to centralize data collection while tailoring inputs per department. Defined clear steps, roles, and communication flows.
Validation & Iteration
Tested the workflow internally and refined communication materials, structure, and data tracking to ensure clarity and usability.

Impact

Responses collected from all departments with minimal duplication
Clear ownership and accountability through structured sign-offs
Reduced confusion and improved communication across stakeholders
Established a repeatable, traceable process for future reporting cycles

Inclusive Design: Prioritizing Accessibility in Process Design

Alternative Formats: PDF and printable materials supported different working styles
Clear Language: Guidance reduced ambiguity and improved completion rates
Structured Workflow: Simplified steps made the process easier to follow
Flexible Interaction: Contributors could prepare responses offline before submitting

⭐ Driving Scalable Accessibility Governance with DAG

DAG (Distributed Accessibility Governance) is a lightweight MVP developed as a personal project, building on the AODA reporting system work completed at OCAD University. It takes the core learnings from that experience and translates them into a structured, scalable solution.

The MVP was built using generative AI tools (Lovable) through a rapid, “vibe coding” approach, allowing for quick prototyping and iteration.

The tool is designed to address common challenges across public sector organizations—fragmented data collection, unclear ownership, and inconsistent reporting. By standardizing workflows, clarifying accountability, and centralizing evidence, DAG enables more efficient and reliable AODA compliance management.

It’s a practical step toward scaling accessibility governance beyond a single institution.

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