Migrating from Mode to Superset for Australian Government Organisations

Table of Contents

1. Why Government Teams Are Moving from Mode to Superset
2. Understanding the Migration Landscape
3. Scoping Your Migration: The First 30 Days
4. Governance, Compliance, and Audit Readiness
5. Cost Benchmarking and Budget Planning
6. Technical Architecture and Data Integration
7. The Cutover Pattern: Phased Migration Strategy
8. Team Capability and Training
9. Post-Migration Operations and Optimisation
10. Next Steps and Quick Wins

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Why Government Teams Are Moving from Mode to Superset

Australian government organisations—from federal agencies to state-based teams and local councils—are increasingly moving away from per-seat BI tools like Mode Analytics toward open-source alternatives like Apache Superset. The shift reflects a clear pattern: cost control, sovereignty, and operational independence.

Mode’s per-seat licensing model has become untenable for large government teams. A typical federal agency with 200+ analysts, data scientists, and operational staff faces annual Mode costs exceeding $300,000–$500,000 USD. When you convert that to AUD and factor in currency fluctuation, the bill becomes a significant budget line. Superset, by contrast, runs on your own infrastructure—whether that’s on-premises, in a sovereign AWS region, or on Azure Government—with no per-seat fees. The cost differential alone justifies the migration effort.

Beyond cost, Australian government organisations have a mandate around data sovereignty and control. The Digital Service Standard and the Digital Investment Framework both emphasise keeping government data within Australian borders and maintaining operational independence from overseas vendors. Mode, hosted in the US, doesn’t align with that imperative. Superset, deployed on sovereign infrastructure, does.

Third, there’s the governance and audit angle. When you control your BI platform, you control access logs, query history, and data lineage. That matters enormously for compliance audits, freedom-of-information requests, and security posture. Mode’s centralised SaaS model makes it harder to satisfy those requirements.

The migration is not trivial—it requires careful planning, technical expertise, and stakeholder alignment—but it’s absolutely achievable. This guide walks you through the playbook.

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Understanding the Migration Landscape

Why Mode and Superset Are Not Drop-In Replacements

Before you commit to a migration timeline, you need to understand what you’re actually moving. Mode and Superset are both BI platforms, but they approach the problem differently.

Mode is a hosted, SQL-first BI tool designed for analysts. You write SQL queries, Mode renders interactive dashboards, and you share reports via a web interface. It’s cloud-native, low-friction, and requires minimal infrastructure knowledge. The trade-off is that you’re locked into Mode’s hosting, pricing, and feature roadmap.

Superset is an open-source data visualisation and business intelligence platform. It’s self-hosted, Python-based, and highly extensible. You deploy it on your own infrastructure, manage its database, and control its entire lifecycle. Superset can connect to dozens of data sources—PostgreSQL, MySQL, ClickHouse, Snowflake, BigQuery, and more—but you’re responsible for the deployment, security, scaling, and maintenance.

The technical differences matter:

• Query language: Mode uses SQL directly; Superset abstracts SQL through a visual query builder or allows native SQL.
• Hosting: Mode is SaaS; Superset is self-hosted.
• Customisation: Mode has limited customisation; Superset is highly customisable via Python and React.
• Cost model: Mode charges per seat; Superset charges nothing (but you pay for infrastructure).
• Compliance: Mode is US-hosted; Superset runs where you deploy it.

For Australian government teams, these differences are not cosmetic—they’re structural. The migration is not a flip-the-switch exercise; it’s a re-platforming project.

The Real Cost of Staying on Mode

Before you dive into migration planning, quantify the cost of inaction. For a typical Australian government agency:

• Licensing: 200 seats at USD $25–$35 per seat per month = USD $60,000–$84,000 per year (AUD $90,000–$126,000).
• Currency exposure: AUD/USD fluctuation adds 10–15% volatility year-on-year.
• Vendor lock-in: Custom reports, dashboards, and integrations become harder to migrate the longer you stay.
• Data sovereignty risk: Every query is logged offshore; compliance teams flag this in audits.
• Growth friction: Adding new analysts or data scientists means adding new seats and new costs.

For a 300-person government team, Mode costs can exceed AUD $200,000 per year. Superset, hosted on sovereign AWS or Azure, might cost AUD $20,000–$40,000 per year in infrastructure and operational overhead. The payback period is typically 12–18 months.

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Scoping Your Migration: The First 30 Days

Step 1: Inventory Your Mode Estate

Your first task is to understand what you’re moving. Create a comprehensive inventory of:

1. Reports and dashboards: Count them, document owners, and identify usage patterns (who views what, how often).
2. Data sources: List all databases, APIs, and third-party systems connected to Mode.
3. Users and permissions: Map role-based access control (RBAC) and document team structures.
4. Custom code: Identify any custom SQL, Python, or JavaScript embedded in Mode.
5. Integrations: Document any downstream systems that consume Mode reports (email, Slack, API calls, etc.).
6. SLAs and uptime requirements: Understand which reports are mission-critical and what downtime is acceptable.

A typical government agency might have:

• 150–300 active dashboards
• 8–15 connected data sources
• 200–400 named users
• 50–100 reports on scheduled distribution
• 20–30 custom SQL queries or transformations

Document all of this in a spreadsheet. Include columns for:

• Dashboard/report name
• Owner (team, person)
• Data source(s)
• User count
• Update frequency
• Criticality (mission-critical, important, nice-to-have)
• Complexity (simple SQL, complex joins, custom code)
• Downstream dependencies

This inventory becomes your migration roadmap.

Step 2: Assess Data Architecture

Superset’s performance and reliability depend entirely on your data architecture. Before you migrate, you need to understand your current data setup and plan the target state.

Questions to answer:

• Where does your data live? (On-premises data warehouse, AWS Redshift, Azure Synapse, Snowflake, etc.)
• How fresh does it need to be? (Real-time, hourly, daily, weekly)
• What’s your current data refresh cadence?
• Do you have a data lake or data warehouse, or are you querying operational databases directly?
• How much data are we talking about? (GB, TB, PB?)
• What’s the query complexity? (Simple aggregations or complex multi-table joins?)

For Australian government teams, consider also:

• Sovereignty: Is your data currently in Australian data centres? (Most government data should be.)
• Classification: Is any data PROTECTED, OFFICIAL, or UNCLASSIFIED? This affects where Superset can run.
• Integration patterns: How does data flow from source systems into your BI platform?

Superset performs best when backed by a modern data warehouse—ClickHouse, Snowflake, or BigQuery. If you’re currently querying operational databases directly through Mode, you’ll want to architect a data warehouse or data lake first. This is a separate workstream but critical to migration success.

Step 3: Define Success Criteria

Before you start migrating, agree on what success looks like. Typical success criteria for government teams:

• Cost reduction: Achieve 60–70% cost reduction vs. Mode within 18 months.
• Time to value: Migrate 80% of critical dashboards within 12 weeks.
• Data sovereignty: 100% of data remains in Australian data centres.
• Uptime: Achieve 99.5% availability for production dashboards.
• User adoption: 90% of users actively using Superset within 6 months.
• Compliance: Pass security audit and satisfy SOC 2 / ISO 27001 requirements.
• Performance: Dashboard load times under 3 seconds for 95th percentile queries.

Document these criteria and share them with stakeholders. They become your north star throughout the migration.

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Governance, Compliance, and Audit Readiness

Aligning with Government Standards

Australian government organisations operate under strict governance frameworks. Your Superset deployment must align with these standards.

The Digital Service Standard requires government services to be secure, accessible, and user-centred. For a BI platform, this means:

• Security: Implement authentication (SSO via Active Directory), encryption in transit and at rest, and audit logging.
• Accessibility: Ensure dashboards meet WCAG 2.1 AA standards for colour contrast, keyboard navigation, and screen-reader compatibility.
• User-centredness: Design dashboards with end users, not just for technical analysts.

The Digital Investment Framework requires documented business cases, risk assessments, and value realisation planning. For your Superset migration, this means:

• Business case: Document cost savings, risk reduction, and capability uplift.
• Risk register: Identify technical, organisational, and compliance risks; document mitigation strategies.
• Benefits realisation plan: Define how you’ll measure and track benefits post-migration.

Security, Audit, and Compliance

Government organisations face regular security audits. Your Superset deployment must be audit-ready from day one.

Key audit requirements:

1. Authentication and authorisation: Implement multi-factor authentication (MFA) and role-based access control (RBAC). Users should authenticate via your government Active Directory or Azure AD.
2. Audit logging: Every query, dashboard view, and administrative action must be logged. Logs must be immutable and retained for 7+ years.
3. Data encryption: Encrypt data in transit (TLS 1.2+) and at rest (AES-256).
4. Network isolation: Superset should run in a private subnet, accessible only via VPN or bastion host.
5. Vulnerability management: Regular patching, dependency scanning, and penetration testing.
6. Incident response: Document how you’ll respond to security incidents, data breaches, or platform outages.
7. Data residency: All data and logs must remain in Australian data centres (AWS ap-southeast-2, Azure Australia East, etc.).

Many government teams use Vanta to automate compliance monitoring. Vanta integrates with AWS, Azure, and on-premises infrastructure to continuously monitor security controls, generate audit reports, and flag non-compliance. If your organisation is pursuing SOC 2 Type II or ISO 27001 certification, Vanta can accelerate the process significantly.

PADISO’s Security Audit service specifically helps Australian organisations get audit-ready via Vanta, reducing the time from months to weeks. For government teams migrating to Superset, this is often a critical workstream.

Data Governance and Lineage

Superset must integrate with your data governance framework. This includes:

• Data dictionary: Document all tables, columns, and business definitions.
• Data lineage: Track how data flows from source systems through transformations into dashboards.
• Access control: Implement fine-grained permissions (row-level security, column-level security).
• Data quality: Monitor data freshness, completeness, and accuracy.

Superset has native support for data lineage (via metadata API) and RBAC, but you’ll need to configure these carefully. Consider also integrating with a data cataloguing tool like Alation or Collibra if you have large, complex data estates.

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Cost Benchmarking and Budget Planning

Mode Costs: The Baseline

Let’s establish the baseline. A typical Australian government organisation with 250 Mode users incurs:

• Licensing: 250 seats × USD $30/seat/month = USD $90,000/year (AUD ~$135,000)
• Professional services: Annual consulting, custom development = AUD $20,000–$50,000
• Integration costs: API calls, data connectors, middleware = AUD $10,000–$30,000
• Total annual cost: AUD $165,000–$215,000

This cost grows with headcount. Each new analyst adds USD $360/year in licensing alone.

Superset Costs: Infrastructure, Operations, and Talent

Superset is free, but hosting it costs money. Here’s a realistic cost model for a government-grade deployment:

Infrastructure (AWS ap-southeast-2):

• Compute: ECS/Fargate cluster (2–4 vCPU, 8–16 GB RAM) = AUD $200–$400/month
• Database: RDS PostgreSQL (multi-AZ, 100 GB storage) = AUD $300–$500/month
• Data warehouse: ClickHouse cluster (3 nodes, 50 TB storage) = AUD $1,500–$3,000/month (optional; depends on data size)
• Networking: VPC, NAT gateway, VPN = AUD $100–$200/month
• Backup and DR: S3 storage, snapshots = AUD $100–$200/month
• Monitoring and logging: CloudWatch, log aggregation = AUD $100–$200/month

Subtotal infrastructure: AUD $2,400–$4,500/month (AUD $28,800–$54,000/year)

Operations and talent:

• Platform engineering: 1 FTE (0.5–1.0 engineer) to manage Superset, infrastructure, and integrations = AUD $100,000–$150,000/year
• Data engineering: 0.5–1.0 FTE to manage data pipelines, warehouse, and quality = AUD $80,000–$120,000/year
• Training and change management: AUD $20,000–$40,000 (one-time)

Subtotal operations: AUD $200,000–$310,000/year

Total Superset cost: AUD $228,800–$364,000/year

At first glance, this looks expensive. But here’s the reality:

1. You already have infrastructure and engineers: Most government organisations already run data warehouses and have platform engineers. The incremental cost of Superset is much lower than the full cost shown above.
2. Superset scales without per-seat cost: Whether you have 100 or 1,000 users, your infrastructure cost stays roughly the same.
3. Payback period: If Mode costs AUD $165,000–$215,000/year and Superset costs AUD $50,000–$100,000/year (incremental), payback happens in 12–18 months.
4. Long-term savings: After year 2, you’re saving AUD $100,000–$150,000 per year indefinitely.

Cost Benchmarking by Government Tier

Costs vary by organisation size:

Small agency (50 Mode users):

• Mode cost: AUD $75,000/year
• Superset cost (incremental): AUD $30,000–$50,000/year
• Payback: 12–18 months

Medium agency (200 Mode users):

• Mode cost: AUD $300,000/year
• Superset cost (incremental): AUD $50,000–$80,000/year
• Payback: 9–12 months

Large agency (500+ Mode users):

• Mode cost: AUD $750,000+/year
• Superset cost (incremental): AUD $80,000–$120,000/year
• Payback: 6–9 months

Budget planning tip: If you don’t have in-house platform engineering, consider engaging a fractional CTO or platform engineering partner. PADISO’s Fractional CTO & CTO Advisory in Canberra and Platform Development in Canberra specifically support government teams in Canberra, helping with sovereign architecture, procurement, and IRAP-aware decisions. Similar support is available across Australian cities via PADISO’s Platform Development in Australia offering.

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Technical Architecture and Data Integration

Designing Your Superset Architecture

Superset’s architecture is straightforward but must be designed carefully for government compliance and performance.

Core components:

1. Superset application server: Python Flask app running on ECS/Fargate or on-premises.
2. Metadata database: PostgreSQL storing dashboard definitions, user permissions, and query history.
3. Message queue: Celery + Redis for asynchronous task processing (report scheduling, caching).
4. Data warehouse: ClickHouse, Snowflake, BigQuery, or Redshift for analytics queries.
5. Authentication: Active Directory / Azure AD for SSO.
6. Logging and monitoring: CloudWatch, ELK, or Splunk for audit trails and observability.

Deployment options for Australian government:

• AWS ap-southeast-2 (Sydney): Most common; supports PROTECTED data if you use AWS GovCloud equivalent.
• Azure Australia East (Canberra): Good for teams already on Azure; supports PROTECTED via Azure Government.
• On-premises: Some defence and security agencies require on-premises deployment. Superset runs fine on Linux servers in your data centre.

Network architecture:

Government users (VPN/Bastion)
    ↓
Application Load Balancer (ALB)
    ↓
Superset ECS cluster (private subnet)
    ↓
RDS PostgreSQL (private subnet)
    ↓
Data warehouse (ClickHouse / Snowflake / Redshift)
    ↓
Source systems (HR, Finance, Operations, etc.)

All traffic is encrypted (TLS 1.2+). Superset runs in a private subnet, accessible only via VPN or bastion host. Database credentials are managed via AWS Secrets Manager or HashiCorp Vault.

Data Integration and ETL

Superset is a presentation layer; the real work happens upstream in your data pipeline.

Typical data flow:

1. Source systems: HR, Finance, Operations, etc. generate transactional data.
2. ETL/ELT pipeline: Apache Airflow, dbt, or cloud-native tools (AWS Glue, Azure Data Factory) transform and load data into your warehouse.
3. Data warehouse: ClickHouse, Snowflake, or similar; stores clean, aggregated data optimised for analytics.
4. Superset: Queries the warehouse, renders dashboards, caches results.

For Australian government teams, the ETL layer is often the biggest migration effort. If you’re currently using Mode with direct database connections, you’ll need to build (or migrate to) a proper data warehouse. This is a separate project but essential for Superset success.

Recommended data warehouse for government teams:

• ClickHouse: Open-source, high-performance OLAP database. Excellent for government use cases (cost control, sovereignty, performance). Deployed on AWS ap-southeast-2 or on-premises.
• Snowflake: Managed, cloud-native warehouse. Good if you want to outsource infrastructure; less control over data sovereignty.
• AWS Redshift: AWS-native warehouse. Good if you’re already on AWS; tight integration with Superset.

ClickHouse is increasingly popular in Australian government because it’s open-source, runs on sovereign infrastructure, and costs significantly less than Snowflake or Redshift at scale.

Connecting Superset to Your Data Sources

Superset can connect to dozens of data sources. Configuration depends on your architecture:

Direct connections (not recommended for production):

• Superset → PostgreSQL, MySQL, Oracle (operational databases)
• Simple but risky; exposes production databases to query load and security risk

Warehouse-mediated connections (recommended):

• Superset → Data warehouse (ClickHouse, Snowflake, Redshift)
• Data warehouse ← ETL pipeline ← Source systems
• Decouples analytics from operational systems; better performance and security

For government teams, warehouse-mediated is essential. It also supports row-level security (RLS), where different users see different data based on their role.

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The Cutover Pattern: Phased Migration Strategy

Phase 1: Pilot (Weeks 1–4)

Objective: Validate Superset in a controlled environment; build team confidence.

Activities:

1. Deploy Superset in a development environment (AWS ap-southeast-2 or on-premises).
2. Connect to one data source: Start with a single, non-critical database or warehouse table.
3. Recreate 5–10 Mode dashboards in Superset. Choose simple ones first (no custom code).
4. Invite 10–20 pilot users: Mix of analysts, business users, and IT staff.
5. Gather feedback: Run weekly feedback sessions; document gaps and feature requests.
6. Document the process: Create runbooks for dashboard creation, user provisioning, and troubleshooting.

Success criteria for pilot:

• Superset is stable (99% uptime).
• Pilot users can create dashboards without engineering support.
• Dashboard load times are acceptable (< 3 seconds).
• No critical bugs or security issues.

Typical pilot cost: AUD $15,000–$30,000 (engineering time + infrastructure).

Phase 2: Early Adoption (Weeks 5–12)

Objective: Migrate 30–50% of Mode dashboards; expand user base to 50–100 people.

Activities:

1. Migrate high-value dashboards: Focus on dashboards with high usage or business impact.
2. Implement authentication: Connect to Active Directory / Azure AD; set up SSO.
3. Establish governance: Define dashboard ownership, naming conventions, and access control.
4. Automate scheduling: Set up scheduled report delivery (email, Slack, API).
5. Train users: Run hands-on workshops; create video tutorials.
6. Monitor performance: Track dashboard load times, query performance, and user adoption.

Migration approach:

• Parallel running: Keep Mode running; gradually migrate users to Superset.
• User cohorts: Migrate by team or business unit, not all at once.
• Fallback plan: If Superset has issues, users can fall back to Mode.

Typical early adoption cost: AUD $40,000–$80,000 (engineering, training, infrastructure).

Phase 3: Full Migration (Weeks 13–24)

Objective: Migrate remaining 50–70% of Mode dashboards; reach 80%+ user adoption.

Activities:

1. Migrate remaining dashboards: Including complex ones with custom SQL or integrations.
2. Decommission Mode: Cancel Mode subscription; redirect users to Superset.
3. Optimise performance: Fine-tune queries, caching, and infrastructure.
4. Establish operations: Define SLAs, on-call rotations, and incident response.
5. Archive Mode data: Export historical reports and metadata for compliance.

Risk mitigation:

• Staggered cutover: Don’t migrate all dashboards on the same day. Spread cutover over 4–6 weeks.
• Monitoring: Have engineering on standby during cutover; monitor error rates and performance.
• Rollback plan: Keep Mode running for 2–4 weeks post-migration in case of critical issues.

Typical full migration cost: AUD $60,000–$120,000 (engineering, infrastructure, contingency).

Phase 4: Optimisation (Weeks 25–52)

Objective: Optimise performance, reduce costs, and build advanced capabilities.

Activities:

1. Performance tuning: Optimise slow queries; implement caching strategies.
2. Cost optimisation: Right-size infrastructure; eliminate unused resources.
3. Advanced analytics: Implement ML-based insights, anomaly detection, alerting.
4. Self-service BI: Enable business users to create dashboards without analyst support.
5. Continuous improvement: Monthly retrospectives; roadmap for new features.

Typical optimisation cost: AUD $30,000–$60,000 (ongoing engineering).

Total Migration Timeline and Cost

• Duration: 6–12 months (depending on complexity)
• Total cost: AUD $145,000–$290,000 (engineering, infrastructure, training)
• Payback: 12–18 months
• Post-migration savings: AUD $100,000–$150,000 per year

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Team Capability and Training

Building Internal Capability

Superset’s success depends on your team’s ability to operate it. You need:

1. Platform engineers (1–2 FTE): Deploy, maintain, and scale Superset. Manage infrastructure, security, and integrations.
2. Data engineers (1–2 FTE): Build and maintain data pipelines, warehouse, and data quality.
3. Analytics engineers (1–3 FTE): Create reusable data models, document metrics, and support analysts.
4. Business analysts (varies): Create dashboards, answer business questions, manage stakeholder expectations.

Hiring vs. outsourcing:

• Hiring: Takes 3–6 months; builds long-term capability; more expensive upfront.
• Outsourcing: Faster to market; lower upfront cost; risk of knowledge loss.
• Hybrid: Hire 1 FTE platform engineer; outsource platform setup and initial migration to a specialist firm.

Many Australian government teams use a hybrid approach. They hire a permanent platform engineer (who understands government procurement, security, and culture) and engage an external partner like PADISO for the migration and initial setup. PADISO’s Fractional CTO & CTO Advisory in Canberra and Platform Development in Canberra specifically support this model, providing fractional technical leadership while government teams build permanent capability.

Training and Change Management

A Superset migration is not just technical; it’s organisational. Users need to learn new tools and workflows.

Training plan:

1. Executive briefing (30 min): Why we’re migrating; benefits; timeline.
2. Analyst workshop (2 hours): Creating dashboards, writing SQL, managing permissions.
3. Business user workshop (1 hour): Viewing dashboards, filtering data, exporting reports.
4. Admin training (4 hours): User provisioning, access control, monitoring.
5. Self-service resources: Video tutorials, runbooks, FAQ, Slack channel for support.

Change management:

• Sponsor: Executive champion (e.g., CFO, CIO) who communicates the vision and benefits.
• Steering committee: Representatives from key business units; meets monthly to review progress.
• Early adopters: 20–30 power users who champion Superset; provide peer support.
• Communication cadence: Weekly updates during migration; monthly after cutover.

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Post-Migration Operations and Optimisation

Establishing Operations and SLAs

Once Superset is live, you need operational discipline.

Define SLAs:

• Availability: 99.5% uptime (43 minutes downtime per month)
• Performance: 95th percentile dashboard load time < 3 seconds
• Data freshness: Dashboards updated within 4 hours of source data changes
• Support response: P1 (critical) issues resolved within 2 hours; P2 (major) within 8 hours

Operational runbooks:

• Incident response (dashboard down, slow queries, data issues)
• Backup and disaster recovery
• User provisioning and offboarding
• Dependency updates and patching
• Capacity planning and scaling

Monitoring and Observability

You can’t optimise what you don’t measure. Implement comprehensive monitoring:

1. Application metrics: Requests per second, error rate, response time, active users.
2. Infrastructure metrics: CPU, memory, disk, network utilisation.
3. Database metrics: Query latency, slow queries, cache hit rate, connections.
4. Business metrics: Dashboard views, user adoption, data freshness.
5. Audit logs: All queries, dashboard changes, user actions.

Use CloudWatch (AWS) or Azure Monitor (Azure) for infrastructure; use Superset’s native metrics API for application metrics. Consider a dedicated observability platform like Datadog or New Relic if you have complex infrastructure.

Optimisation Opportunities

Post-migration, focus on:

1. Query optimisation: Identify slow queries; add indexes; rewrite inefficient SQL.
2. Caching: Implement query caching (Redis) and dashboard caching to reduce database load.
3. Data model optimisation: Denormalise tables for common queries; pre-aggregate data.
4. Infrastructure right-sizing: Monitor utilisation; scale down unused resources.
5. Self-service BI: Enable business users to create dashboards without analyst support; reduces bottleneck.

Expected post-migration improvements:

• 40–60% reduction in query latency (via optimisation and caching)
• 30–50% reduction in infrastructure costs (via right-sizing)
• 2–3x increase in dashboard creation velocity (via self-service)
• 80%+ user adoption within 6 months

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Next Steps and Quick Wins

Immediate Actions (Next 30 Days)

1. Secure executive sponsorship: Get CFO, CIO, or agency head to endorse the migration.
2. Inventory Mode estate: Document all dashboards, users, and data sources (use the template from Section 3).
3. Assess data architecture: Understand your current data warehouse or plan to build one.
4. Define success criteria: Agree on cost targets, timeline, and user adoption goals.
5. Identify pilot users: Recruit 10–20 early adopters from key business units.
6. Estimate budget: Use the cost benchmarks from Section 5 to create a business case.
7. Engage a partner (optional): If you lack internal platform engineering expertise, reach out to PADISO or a similar firm. PADISO’s Platform Development in Canberra and Fractional CTO & CTO Advisory in Canberra help government teams scope and execute migrations.

Quick Wins to Build Momentum

1. Migrate one critical dashboard (Week 2–3): Choose a high-visibility dashboard used by executives. Migrate it to Superset; compare performance and UX.
2. Calculate cost savings (Week 3): Quantify Mode licensing costs vs. Superset infrastructure costs. Share the numbers with leadership.
3. Run a pilot (Week 4–8): Deploy Superset in dev; invite 20 pilot users; gather feedback.
4. Publish a case study: Document the pilot results; share learnings with the broader organisation.
5. Establish a steering committee (Week 5): Monthly meetings to review progress, remove blockers, and maintain momentum.

Longer-Term Roadmap

Beyond the migration, consider:

1. Advanced analytics: Implement anomaly detection, forecasting, and ML-powered insights.
2. Data governance: Build a data catalogue (Alation, Collibra); establish data quality metrics.
3. Self-service BI: Enable business users to create dashboards via a low-code interface.
4. Real-time analytics: Migrate from batch ETL to real-time streaming (Kafka, Kinesis) for live dashboards.
5. Embedded analytics: Embed Superset dashboards in business applications for better UX.

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Conclusion: Why This Matters for Australian Government

Migrating from Mode to Superset is not just a cost-saving exercise. It’s about reclaiming control of your data infrastructure, aligning with government policy on data sovereignty, and building long-term operational independence.

The Australian Government Design System and Digital Service Standard both emphasise keeping government data within Australian borders and building services that are secure, accessible, and user-centred. Superset, deployed on sovereign AWS or Azure infrastructure, ticks all those boxes.

The financial case is compelling: typical government agencies save AUD $100,000–$200,000 per year post-migration. The operational case is equally strong: you own your BI platform, control access logs, and can satisfy security audits without relying on overseas vendors.

The migration is not trivial—it requires careful planning, technical expertise, and stakeholder alignment—but it’s absolutely achievable. Follow the playbook in this guide:

1. Scope carefully (30 days): Inventory Mode, assess data architecture, define success criteria.
2. Plan governance (ongoing): Align with Digital Service Standard and Digital Investment Framework; plan for audit readiness.
3. Budget realistically (12–24 months): Expect total migration costs of AUD $150,000–$300,000; payback in 12–18 months.
4. Design architecture (4–8 weeks): Plan Superset deployment, data warehouse, and integrations.
5. Migrate in phases (6–12 months): Pilot → early adoption → full migration → optimisation.
6. Build capability (ongoing): Hire platform engineers; train analysts; establish operations.
7. Optimise continuously (post-migration): Monitor performance; reduce costs; enable self-service.

If you’re a government team considering this migration, start with the 30-day scope. Document your Mode estate, assess your data architecture, and calculate the financial case. Then, decide whether to build capability internally or partner with a specialist firm.

For teams in Canberra, PADISO’s Fractional CTO & CTO Advisory and Platform Development teams have deep experience with government migrations, sovereign architecture, and IRAP-aware decisions. Similar support is available across Australia via PADISO’s Platform Development in Australia and Fractional CTO services in Sydney, Melbourne, Brisbane, Adelaide, and Darwin.

The path from Mode to Superset is clear. The question is not whether to migrate, but when—and with what support. Start today.