Product Experience
Every role. One platform.
From front-counter staff handling resident requests to the CAO reviewing council-ready reports — every role has a purpose-built journey. Explore how Civic Open Data Portal works for your team.
Watch the 3-Minute Demo
See Civic Open Data Portal handle a complete resident service request — from intake through resolution and council reporting.
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Explore the Interface
Click through the actual Civic Open Data Portal interface. Navigate between the dashboard, resident profiles, service requests, and reports to see how everything connects.
Role-Based Journeys
One Platform, Every Perspective
Select a role to explore their complete journey through Civic CRM — from day-one onboarding to daily workflows and strategic outcomes.
Data Officer / Open Data Coordinator
From Raw Data to Public Dataset
Follow the complete lifecycle of a dataset — from source system extraction through transformation, anonymization, quality validation, metadata enrichment, and multi-format publication. Zero PII risk, full audit compliance, DCAT-compliant metadata at every step.
Ingest
Data arrives
Data flows into the platform from source systems — automated pipelines from databases, API endpoints, and file drops, or manual drag-and-drop upload of CSV, Excel, JSON, XML, GeoJSON, and Shapefile formats.
The Pipeline Builder (spec 2.2) configures automated extraction from source municipal systems: database queries (PostgreSQL, SQL Server, Oracle), REST/SOAP API endpoints, file system directories, and FTP/SFTP drops. Manual upload supports drag-and-drop for ad-hoc datasets with preview of the first 100 rows before proceeding. Source connections are credentialed with encrypted storage. Every ingest event is logged with source, timestamp, record count, and file hash.
Transform
Clean & anonymize
Transformation rules clean the data — removing duplicates, standardizing formats, handling missing values — then anonymization strips PII per MFIPPA with re-identification risk scoring and geographic aggregation.
The Transformation Engine (spec 2.3) applies configurable rules: column mapping, data type conversion, value formatting, filtering, aggregation, and calculated fields. The Anonymization Module detects PII fields automatically (name, address, phone, email, SIN patterns) and applies removal, generalization (exact age → range), suppression (values with <5 records), and geographic aggregation (address → neighbourhood). Re-identification risk is scored using k-anonymity and l-diversity metrics. Every transformation step is logged for compliance audit.
Validate
Quality assured
Pre-publication validation checks schema compliance, data types, value ranges, and historical comparison — flagging significant changes from previous publication. AI quality scoring generates a report card with completeness, accuracy, and consistency metrics.
The Pre-Publication Validator (spec 2.4) runs schema compliance checks against the data dictionary, verifies data types, validates value ranges, and compares record counts against previous publication (flagging >20% changes for review). The AI Quality Assessor (spec 8.1) scores completeness, accuracy, timeliness, and consistency with improvement recommendations. A quality report card is auto-generated for display alongside the published dataset. The privacy scan confirms no residual PII survived the anonymization process.
Enrich
Metadata complete
DCAT-compliant metadata is enriched: title, description, keywords, publisher, licence, update frequency, temporal/spatial coverage, and quality notes. A field-level data dictionary defines every column with type, description, and allowed values.
The DCAT Metadata Editor (spec 1.2) guides the data officer through comprehensive metadata entry with field validation, keyword auto-suggest, and completeness scoring. The Data Dictionary Builder (spec 1.2) defines field name, data type, description, allowed values, and sample data for every column — auto-detecting types from the dataset. Metadata quality scoring ensures publication meets the configured minimum threshold (default: 90% completeness). Bilingual metadata (en/fr) is supported and enforced where applicable.
Approve
Privacy reviewed
The publishing workflow routes the dataset through privacy review — MFIPPA checklist, re-identification risk assessment, and sign-off — before final approval by the open data coordinator.
The Publishing Workflow Engine (spec 5.1) routes the dataset through the configured approval chain: data owner (department) → open data coordinator → privacy reviewer → final approval. The Privacy Assessment Engine (spec 5.2) presents a structured checklist: does the dataset contain personal information? Is anonymization sufficient? Re-identification risk score acceptable? The reviewer signs off with identity logging. Approval decisions are recorded in the immutable audit trail. Rejection returns to the data owner with specific feedback for remediation.
Publish
Live & measurable
Dataset published simultaneously in CSV, JSON, XML, GeoJSON, and Excel with persistent URLs. API endpoints auto-generated. Subscribers notified. Usage analytics begin tracking downloads, API calls, and community engagement.
The Multi-Format Publisher (spec 2.5) generates all output formats simultaneously with persistent URLs that survive updates. API endpoints are auto-generated with interactive Swagger documentation. The Subscription Engine notifies all subscribers via email. Usage analytics begin tracking: downloads by format, API calls by endpoint, unique users, and geographic distribution. The dataset appears in the public catalogue with its quality report card, data dictionary, and preview capability. The Data Quality Monitor (spec 5.5) begins freshness tracking against the configured update schedule.
Publishing Journey
0 / 6
Step 01
Ingest
Data arrives
Data flows into the platform from source systems — automated pipelines from databases, API endpoints, and file drops, or manual drag-and-drop upload of CSV, Excel, JSON, XML, GeoJSON, and Shapefile formats.
The Pipeline Builder (spec 2.2) configures automated extraction from source municipal systems: database queries (PostgreSQL, SQL Server, Oracle), REST/SOAP API endpoints, file system directories, and FTP/SFTP drops. Manual upload supports drag-and-drop for ad-hoc datasets with preview of the first 100 rows before proceeding. Source connections are credentialed with encrypted storage. Every ingest event is logged with source, timestamp, record count, and file hash.
Step 02
Transform
Clean & anonymize
Transformation rules clean the data — removing duplicates, standardizing formats, handling missing values — then anonymization strips PII per MFIPPA with re-identification risk scoring and geographic aggregation.
The Transformation Engine (spec 2.3) applies configurable rules: column mapping, data type conversion, value formatting, filtering, aggregation, and calculated fields. The Anonymization Module detects PII fields automatically (name, address, phone, email, SIN patterns) and applies removal, generalization (exact age → range), suppression (values with <5 records), and geographic aggregation (address → neighbourhood). Re-identification risk is scored using k-anonymity and l-diversity metrics. Every transformation step is logged for compliance audit.
Step 03
Validate
Quality assured
Pre-publication validation checks schema compliance, data types, value ranges, and historical comparison — flagging significant changes from previous publication. AI quality scoring generates a report card with completeness, accuracy, and consistency metrics.
The Pre-Publication Validator (spec 2.4) runs schema compliance checks against the data dictionary, verifies data types, validates value ranges, and compares record counts against previous publication (flagging >20% changes for review). The AI Quality Assessor (spec 8.1) scores completeness, accuracy, timeliness, and consistency with improvement recommendations. A quality report card is auto-generated for display alongside the published dataset. The privacy scan confirms no residual PII survived the anonymization process.
Step 04
Enrich
Metadata complete
DCAT-compliant metadata is enriched: title, description, keywords, publisher, licence, update frequency, temporal/spatial coverage, and quality notes. A field-level data dictionary defines every column with type, description, and allowed values.
The DCAT Metadata Editor (spec 1.2) guides the data officer through comprehensive metadata entry with field validation, keyword auto-suggest, and completeness scoring. The Data Dictionary Builder (spec 1.2) defines field name, data type, description, allowed values, and sample data for every column — auto-detecting types from the dataset. Metadata quality scoring ensures publication meets the configured minimum threshold (default: 90% completeness). Bilingual metadata (en/fr) is supported and enforced where applicable.
Step 05
Approve
Privacy reviewed
The publishing workflow routes the dataset through privacy review — MFIPPA checklist, re-identification risk assessment, and sign-off — before final approval by the open data coordinator.
The Publishing Workflow Engine (spec 5.1) routes the dataset through the configured approval chain: data owner (department) → open data coordinator → privacy reviewer → final approval. The Privacy Assessment Engine (spec 5.2) presents a structured checklist: does the dataset contain personal information? Is anonymization sufficient? Re-identification risk score acceptable? The reviewer signs off with identity logging. Approval decisions are recorded in the immutable audit trail. Rejection returns to the data owner with specific feedback for remediation.
Step 06
Publish
Live & measurable
Dataset published simultaneously in CSV, JSON, XML, GeoJSON, and Excel with persistent URLs. API endpoints auto-generated. Subscribers notified. Usage analytics begin tracking downloads, API calls, and community engagement.
The Multi-Format Publisher (spec 2.5) generates all output formats simultaneously with persistent URLs that survive updates. API endpoints are auto-generated with interactive Swagger documentation. The Subscription Engine notifies all subscribers via email. Usage analytics begin tracking: downloads by format, API calls by endpoint, unique users, and geographic distribution. The dataset appears in the public catalogue with its quality report card, data dictionary, and preview capability. The Data Quality Monitor (spec 5.5) begins freshness tracking against the configured update schedule.
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See Civic Open Data Portal in your environment
Schedule a personalized walkthrough with our municipal solutions team. We’ll configure a demo environment to match your municipality’s structure.