Migrating from Qlik to Superset for Australian Government Organisations

Table of Contents

1. Executive Summary
2. Why Government Organisations Are Moving from Qlik to Superset
3. Pre-Migration Assessment and Scoping
4. Governance, Security and Compliance Frameworks
5. Cost Benchmarking and Budget Planning
6. Technical Architecture and Data Integration
7. The Migration Cutover Pattern
8. Post-Migration Optimisation and Handover
9. Common Pitfalls and How to Avoid Them
10. Next Steps and Getting Started

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Executive Summary

Australian government organisations have invested heavily in Qlik for business intelligence and analytics. Over the past three years, however, a shift toward open-source, sovereign-controlled platforms has accelerated. Apache Superset—a modern, lightweight, and cost-efficient alternative—is now the platform of choice for teams modernising their analytics stack.

This guide covers the complete migration journey from Qlik to Superset for government teams. We focus on scoping, governance, cost benchmarks, and the cutover pattern that minimises disruption and maximises adoption.

Key outcomes you can expect:

• Cost reduction: 40–60% per-seat savings by moving from Qlik’s per-user licensing model to Superset’s open-source architecture.
• Faster time-to-insight: Superset dashboards ship in 4–6 weeks versus 12–16 weeks for equivalent Qlik projects.
• Sovereign control: Full data residency in Australian infrastructure (AWS Sydney, Azure Australia, or on-premises).
• Compliance alignment: Native support for Essential Eight controls and audit-readiness frameworks.

If your government team is running Qlik on a per-seat licensing model and seeing adoption plateau at 30–50% of intended users, this playbook is for you.

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Why Government Organisations Are Moving from Qlik to Superset

The Licensing and Cost Pressure

Qlik’s per-user, per-month licensing model creates a hard ceiling on adoption. A typical government agency with 500 intended analytics users faces annual Qlik costs of $600,000–$900,000 (at $100–$150 per user per month, including support and infrastructure). In practice, usage remains at 150–250 active users because budget-holders restrict seat allocation.

Superset’s open-source model eliminates per-user licensing. Deployment costs shift to infrastructure (cloud or on-premises) and internal team capacity. For the same 500-user cohort, Superset infrastructure costs typically range from $80,000–$150,000 annually, with no seat restrictions.

Sovereignty and Data Residency

Following the Privacy Overview guidance from the Office of the Australian Information Commissioner, Australian government agencies increasingly require data residency within Australian borders. Qlik’s SaaS offering, whilst IRAP-assessed, still routes some metadata and telemetry through Qlik’s US infrastructure.

Superset, deployed on Australian cloud infrastructure (AWS Sydney, Azure Australia East, or on-premises), ensures full data sovereignty. This is non-negotiable for defence, health, and national security portfolios.

Vendor Lock-In and Control

Qlik’s proprietary scripting language (QlikView/Qlik Sense) and closed data model create switching costs. Dashboards, data models, and custom extensions are locked within the Qlik ecosystem. Superset uses SQL, Python, and standard web technologies. Dashboards are portable; data models are decoupled from the platform.

For government organisations planning multi-decade analytics strategies, this flexibility is critical.

Modern Developer Experience

Qlik’s UI-driven development model suits business analysts but frustrates engineering teams. Apache Superset Documentation emphasises a code-first, API-driven approach. Dashboards are defined in YAML or JSON, version-controlled in Git, and deployed via CI/CD pipelines—the same patterns used for modern software delivery.

Government teams modernising their tech stacks (moving to Kubernetes, containerisation, and infrastructure-as-code) find Superset’s architecture a natural fit.

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Pre-Migration Assessment and Scoping

Step 1: Inventory Your Qlik Estate

Before committing to a migration, you need a clear picture of what you’re moving. Conduct a comprehensive Qlik audit:

Dashboards and Apps

• Count total Qlik Sense apps and QlikView documents currently in production.
• Categorise by business unit, frequency of use, and user count.
• Identify “zombie” dashboards (last accessed >6 months ago). These often represent 20–30% of the estate and are candidates for retirement, not migration.

Data Sources and Connections

• Document all data connectors: SQL Server, Oracle, Salesforce, SAP, custom APIs, file-based sources.
• Record transformation logic embedded in Qlik load scripts (QlikView script or Qlik Sense data model).
• Identify real-time versus batch-loaded data and refresh frequencies.

User Base and Adoption

• Break down active users by role: analysts, business users, executives, developers.
• Measure engagement: average sessions per user, average session duration, dashboard views per month.
• Identify power users who will become Superset champions post-migration.

Custom Extensions and Integrations

• List any custom Qlik extensions, plugins, or API integrations.
• Document any webhook or alert integrations (e.g., email alerts on KPI breaches).
• Assess whether these can be replicated in Superset or need alternative solutions.

Effort Estimate: 2–3 weeks for a government agency with 50–100 Qlik apps.

Step 2: Define Your Superset Architecture

Superset is flexible; it can be deployed in multiple ways. For government organisations, we recommend a phased approach:

Phase 1: Proof of Concept (Weeks 1–4)

• Deploy a single Superset instance on AWS Sydney or Azure Australia East.
• Connect to 2–3 non-sensitive data sources (e.g., HR, finance test data).
• Build 3–5 representative dashboards mirroring existing Qlik apps.
• Test user authentication (LDAP, SAML, OAuth2) against your identity provider.
• Validate performance with 50–100 concurrent users.

Phase 2: Pilot (Weeks 5–12)

• Expand to production data sources (with appropriate access controls).
• Migrate 15–20 high-value Qlik dashboards.
• Onboard 100–150 pilot users from key business units.
• Conduct feedback sessions; iterate on UX and data model design.

Phase 3: Cutover (Weeks 13–16)

• Migrate remaining dashboards in batches (50–100 per week).
• Decommission Qlik environments.
• Support existing users; train new cohorts.

Step 3: Stakeholder Alignment

Migrations fail when stakeholders don’t understand the trade-offs. Secure buy-in early:

• CIOs and security leads: Frame Superset as a sovereign, audit-ready platform aligned with Essential Eight and IRAP principles.
• Finance teams: Present the 40–60% cost-per-user reduction and the elimination of per-seat licensing constraints.
• Business unit heads: Explain faster dashboard delivery (4–6 weeks vs. 12–16 weeks) and broader user access without budget gatekeeping.
• Data and analytics teams: Highlight the modern data stack alignment (dbt, Airflow, Kubernetes) and portability.

Document assumptions and success criteria in a migration charter. Agree on go/no-go gates at each phase.

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Governance, Security and Compliance Frameworks

Data Governance and Access Control

Superset’s role-based access control (RBAC) is simpler than Qlik’s but requires careful configuration. Define your governance model upfront:

Role Hierarchy

• Admin: Full platform access, user management, data source configuration.
• Data Steward: Can create and modify dashboards; manages data dictionary; publishes to production.
• Analyst: Can view and interact with published dashboards; limited ad-hoc query capability.
• Viewer: Read-only access to assigned dashboards.

Data Source Access

• Implement row-level security (RLS) for sensitive datasets (e.g., personnel data, classified information).
• Use database-level permissions to enforce access; don’t rely solely on Superset’s UI-layer controls.
• Document data lineage: which dashboards depend on which data sources, and who owns each.

Compliance and Audit Readiness

Australian government organisations must satisfy multiple compliance frameworks:

IRAP / PROTECTED Alignment

Superset’s open-source codebase is auditable. Deploy on IRAP-aligned infrastructure (AWS GovCloud Australia, Azure Government, or certified on-premises environments). Ensure:

• Encryption in transit (TLS 1.2+) and at rest (AES-256).
• All API traffic is logged and retained for 12 months minimum.
• Access logs include user ID, action, timestamp, and data source.

Privacy Act Compliance

Following OAIC guidance, ensure personal information is:

• Held securely with restricted access (RBAC).
• Not exported to unsecured systems (disable CSV/Excel exports for sensitive dashboards).
• Audited for access (who viewed what, when).

Audit Trail and Logging

Superset logs dashboard views, query execution, and data exports. Configure:

• Centralised logging to AWS CloudWatch, Azure Monitor, or Splunk.
• Retention policies (typically 12–24 months for government).
• Alerting on suspicious activity (e.g., bulk data exports, failed authentication attempts).

For government teams navigating these frameworks, working with fractional CTO advisory in Canberra or platform development in Canberra can accelerate compliance sign-off and architecture validation.

Change Management and Governance Boards

Establish a steering committee:

• Executive sponsor: CIO or agency head; owns budget and escalations.
• Technical lead: Owns architecture and cutover execution.
• Data steward: Owns governance, access control, and data quality.
• Business representatives: From key user departments; validate dashboard requirements.

Meet fortnightly during the migration. Review:

• Dashboard migration progress (target: 80% complete by week 12).
• User adoption metrics (target: 60% of pilot users active weekly by week 8).
• Compliance and security sign-offs.
• Budget and timeline adherence.

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Cost Benchmarking and Budget Planning

Qlik vs. Superset: Total Cost of Ownership

Qlik Annual Cost (500-user cohort)

Component	Cost
Per-user licensing (150 active users @ $120/month)	$216,000
Infrastructure (cloud or on-premises)	$60,000
Qlik support and maintenance	$40,000
Internal team (2 FTE @ $150k average)	$300,000
Total	$616,000

Superset Annual Cost (500-user cohort)

Component	Cost
Cloud infrastructure (AWS or Azure)	$120,000
Superset support and maintenance (vendor or in-house)	$30,000
Internal team (2.5 FTE @ $150k average)	$375,000
Data pipeline and ETL (dbt, Airflow)	$50,000
Total	$575,000

Net Savings: $41,000 annually (7%), with higher savings if you can reduce internal team headcount post-stabilisation.

If your Qlik deployment uses 300+ active users, savings exceed 40%. If you’re currently restricting seat allocation due to budget, Superset’s unlimited-user model delivers 60%+ savings by enabling full adoption.

Migration Project Budget

One-time Migration Costs (16-week project)

Phase	Cost
Assessment and scoping (weeks 1–3)	$45,000
PoC and infrastructure setup (weeks 1–4)	$60,000
Pilot and dashboard migration (weeks 5–12)	$120,000
Cutover and go-live support (weeks 13–16)	$80,000
Training and documentation	$30,000
Total	$335,000

This assumes a blended team of 3–4 engineers (internal + contractor support). If you partner with a vendor like PADISO, costs typically range $300,000–$450,000 for a government-grade migration with full compliance validation.

ROI Timeline

• Payback period: 8–12 months (migration cost divided by annual savings).
• 3-year savings: $123,000–$205,000 (after subtracting migration cost).
• Intangible benefits: Faster dashboard delivery, broader user access, vendor independence, and compliance alignment are not quantified here but often justify migration on their own.

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Technical Architecture and Data Integration

Superset Deployment Architecture for Government

Superset is a web application that queries databases directly (or via data warehouses). Unlike Qlik, it doesn’t embed data; it’s a query and visualisation layer.

Recommended Architecture

┌─────────────────────────────────────────────────────┐
│ Data Sources                                        │
│ (SQL Server, Oracle, Postgres, Snowflake, etc.)    │
└────────────────────┬────────────────────────────────┘
                     │
         ┌───────────┴───────────┐
         │                       │
    ┌────▼────┐          ┌──────▼──────┐
    │   ETL   │          │  Data Cache │
    │ (dbt,   │          │  (Redis)    │
    │ Airflow)│          └──────┬──────┘
    └────┬────┘                 │
         │          ┌──────────┬┘
         │          │          │
    ┌────▼──────────▼─────┐    │
    │  Superset Instance   │◄───┘
    │  (AWS Sydney or      │
    │   Azure AU East)     │
    └────┬────────────────┘
         │
    ┌────▼──────────────┐
    │  User Access      │
    │  (LDAP/SAML)      │
    │  (100–500 users)  │
    └───────────────────┘

Key Components

• Data sources: Connect Superset directly to production databases or data warehouses. Avoid copying data into Superset; query at runtime for real-time insights.
• ETL layer: Use dbt or Apache Airflow to orchestrate data transformations (replacing Qlik’s load scripts). This decouples data preparation from visualisation.
• Cache layer: Redis caches frequently queried datasets, reducing database load and improving dashboard performance.
• Superset instance: Deploy on Kubernetes (ECS or AKS) for high availability and auto-scaling.
• Authentication: Integrate with your existing identity provider (Active Directory, Okta, Azure AD) via LDAP or SAML.

Data Integration: Migrating from Qlik Load Scripts

Qlik’s load scripts (QlikView/Qlik Sense) embed transformation logic. Superset doesn’t have an equivalent; instead, you move this logic to a dedicated ETL layer.

Migration Pattern

1. Extract: Identify all Qlik load script logic (joins, aggregations, calculations, data quality checks).
2. Translate: Rewrite in dbt (for SQL transformations) or Python (for complex logic).
3. Test: Validate that output matches the original Qlik load.
4. Deploy: Run ETL via Airflow on a schedule (e.g., nightly or hourly).
5. Monitor: Alert on failures; log execution time and row counts.

Example: Qlik to dbt Migration

Qlik script (QlikView):

Sales:
LOAD
  OrderID,
  CustomerID,
  Amount,
  Date
FROM Sales.qvd;

SalesAgg:
LOAD
  CustomerID,
  Sum(Amount) as TotalSales,
  Count(OrderID) as OrderCount
RESIDENT Sales
GROUP BY CustomerID;

Equivalent dbt model:

-- models/sales_agg.sql
WITH sales AS (
  SELECT
    order_id,
    customer_id,
    amount,
    date
  FROM {{ source('raw', 'sales') }}
)
SELECT
  customer_id,
  SUM(amount) AS total_sales,
  COUNT(order_id) AS order_count
FROM sales
GROUP BY customer_id

This approach is more maintainable, testable, and integrates with modern data stack tools.

Performance Tuning for Large Datasets

Superset performs well with datasets up to 10–50 million rows (depending on query complexity). For larger datasets:

• Pre-aggregate: Use dbt to create summary tables (e.g., daily sales by region) instead of querying raw transactions.
• Materialise views: In your database, create indexed views for frequently queried patterns.
• Use data warehouses: Superset works well with Snowflake, BigQuery, or Redshift, which scale to billions of rows.
• Implement caching: Configure Redis to cache dashboard queries; set TTL (time-to-live) based on data freshness requirements.

For government teams deploying Superset with large datasets, platform development in Sydney or platform development in Australia teams can help optimise data pipelines and Superset configuration.

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The Migration Cutover Pattern

Phase 1: Proof of Concept (Weeks 1–4)

Week 1: Infrastructure Setup

• Provision AWS or Azure infrastructure (Superset instance, RDS/Postgres, Redis).
• Configure networking, security groups, and SSL certificates.
• Set up monitoring and logging (CloudWatch, Azure Monitor).
• Deploy Superset via Docker or Kubernetes.

Effort: 1 engineer, 40 hours.

Week 2: Data Source Connectivity

• Connect Superset to 2–3 non-sensitive data sources (e.g., HR, finance test databases).
• Test query performance; optimise as needed.
• Document connection strings and credentials (store in AWS Secrets Manager or Azure Key Vault).

Effort: 1 engineer, 30 hours.

Week 3–4: Dashboard Build and Testing

• Identify 3–5 representative Qlik dashboards (high-value, diverse data sources).
• Rebuild in Superset, replicating layout and interactivity.
• Conduct user testing with 20–30 pilot users.
• Iterate based on feedback; document UX differences (Superset’s interactivity model differs from Qlik’s).

Effort: 2 engineers, 60 hours.

PoC Deliverables

• Superset instance accessible to pilot users.
• 3–5 production-ready dashboards.
• Documentation: data sources, dashboard definitions, user access guide.
• Sign-off from CIO and business stakeholders: “Superset is fit for purpose; proceed to pilot.”

Phase 2: Pilot (Weeks 5–12)

Week 5–6: Expand Data Sources

• Connect all production data sources (with appropriate access controls).
• Implement row-level security (RLS) for sensitive datasets.
• Set up data refresh schedules (e.g., nightly for transactional data, hourly for operational metrics).

Effort: 1 engineer, 40 hours.

Week 7–10: Dashboard Migration

• Batch 1 (week 7): Migrate 5 high-priority dashboards.
• Batch 2 (week 8): Migrate 5 medium-priority dashboards.
• Batch 3 (week 9): Migrate 5 low-priority dashboards.
• Batch 4 (week 10): Build new dashboards identified during pilot (typically 3–5).

For each dashboard:

• Analyse Qlik source (layout, data model, calculations).
• Rebuild in Superset (typically 6–10 hours per dashboard).
• Validate against original (visual comparison, row counts, KPI values).
• User acceptance testing (UAT) with business owner.

Effort: 2 engineers, 120 hours.

Week 11–12: Pilot Stabilisation

• Onboard 100–150 pilot users; provide training (group sessions + ad-hoc support).
• Monitor adoption (target: 60% of pilot users active weekly by end of week 12).
• Gather feedback; prioritise fixes and enhancements.
• Measure performance: average dashboard load time <3 seconds, 95th percentile <8 seconds.

Effort: 1 engineer + 1 trainer, 60 hours.

Pilot Deliverables

• 20 production-ready dashboards.
• Pilot user base: 100–150 active users.
• Adoption metrics: >60% weekly active users.
• Compliance sign-off: security and privacy reviews complete.
• Training materials: user guide, video tutorials, FAQ.

Phase 3: Cutover (Weeks 13–16)

Week 13: Final Qlik Audit

• Identify any remaining Qlik dashboards (zombies excluded).
• Prioritise by user count and business criticality.
• Estimate effort for each; allocate to batches.

Week 14–15: Batch Migration

• Migrate 50–100 dashboards per week.
• Run Qlik and Superset in parallel (dual reporting).
• Monitor data consistency; flag discrepancies.
• Conduct spot-check UAT with business users.

Week 16: Go-Live

• Disable Qlik access (or set to read-only).
• Announce Superset as the primary analytics platform.
• Provide 24/7 support for first week (handle escalations, performance tuning).
• Begin Qlik decommissioning (backup data, archive configurations).

Effort: 3 engineers, 100 hours.

Cutover Deliverables

• All dashboards migrated to Superset.
• Qlik access disabled; data archived.
• Full user base (500+) transitioned; training complete.
• Runbook: how to operate Superset, troubleshoot, scale.
• Post-go-live support plan (2–4 weeks of daily check-ins).

Risk Mitigation During Cutover

Rollback Plan

If critical issues arise during cutover, revert to Qlik within 24 hours. Keep Qlik infrastructure running for 2 weeks post-go-live; don’t decommission prematurely.

Performance Baseline

Before cutover, establish Superset performance baseline:

• Average dashboard load time: <3 seconds.
• Concurrent user capacity: 200+ users without degradation.
• Query timeout: 30 seconds (adjust based on data source).

If cutover testing shows Superset doesn’t meet baseline, delay go-live; investigate and optimise.

User Support

During cutover, provide:

• Dedicated Slack channel for support questions.
• Daily standups with business representatives.
• Known-issues log (updated twice daily).
• Escalation path to technical leadership.

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Post-Migration Optimisation and Handover

Week 1–2 Post-Go-Live: Stabilisation

Monitor and Tune

• Watch Superset logs for errors, slow queries, and authentication failures.
• Identify the top 10 slowest dashboards; optimise queries or pre-aggregate data.
• Adjust cache TTL based on usage patterns (e.g., increase for infrequently-accessed dashboards).
• Scale infrastructure if CPU or memory approaches 80% utilisation.

User Support

• Respond to support tickets within 2 hours.
• Conduct follow-up training for users struggling with the new interface.
• Collect feedback on missing features or data sources; prioritise backlog.

Week 3–4 Post-Go-Live: Optimisation

Performance Tuning

• Review slow-query logs; add database indexes or materialised views.
• Implement query caching for frequently-accessed dashboards (e.g., executive dashboards queried 100+ times daily).
• Consider upgrading infrastructure (larger RDS instance, more Redis memory) if utilisation remains high.

Feature Enablement

• Enable alerts: notify users when KPIs breach thresholds (e.g., “Revenue drops below $1M”).
• Set up scheduled reports: email dashboards to stakeholders on a schedule (daily, weekly).
• Implement custom SQL queries for analysts; document best practices.

Ongoing Operations: Handover to Internal Team

After 4 weeks, transition Superset operations to your internal team. Provide:

Documentation

• Architecture runbook: How Superset is deployed, scaled, and monitored.
• Dashboard catalogue: List of all dashboards, owners, data sources, refresh schedules.
• Data dictionary: Definitions of key metrics, dimensions, and data lineage.
• Troubleshooting guide: Common issues and resolutions.
• Disaster recovery plan: How to restore from backups; RTO/RPO targets.

Knowledge Transfer

• 2–3 days of hands-on training for your ops team (infrastructure, monitoring, troubleshooting).
• 1–2 days for your analytics team (dashboard creation, data source management, SQL optimisation).
• Quarterly office hours (first 3 months) for escalations and architectural questions.

SLA and Support Model

Define internal SLAs:

Issue Type	Response Time	Resolution Time
Critical (platform down)	15 min	2 hours
High (dashboard unavailable)	1 hour	4 hours
Medium (performance degradation)	4 hours	1 day
Low (feature request)	1 day	2 weeks

If you lack internal capacity, consider a fractional CTO advisory arrangement in Canberra or Melbourne to provide ongoing technical leadership and escalation support.

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Common Pitfalls and How to Avoid Them

Pitfall 1: Underestimating Data Transformation Effort

Problem: Teams assume Superset can replicate Qlik’s embedded transformations via UI configuration. In reality, complex Qlik load scripts (joins, aggregations, custom calculations) require rebuilding in dbt or Python.

Solution:

• Audit all Qlik load scripts early (week 1–2).
• Allocate 1–2 engineers to build an ETL layer (dbt + Airflow) in parallel with dashboard migration.
• Budget 4–6 weeks for ETL development; don’t assume it’s a 1–2 week effort.

Pitfall 2: Ignoring Performance at Scale

Problem: PoC dashboards perform well with test data (100K rows), but production dashboards (10M+ rows) time out or consume excessive resources.

Solution:

• Load realistic data volumes into PoC environment (weeks 2–3).
• Conduct load testing: simulate 50–100 concurrent users; measure response times.
• Identify slow queries early; optimise database indexes or pre-aggregate data.
• Set query timeout to a reasonable value (30–60 seconds); alert on timeouts.

Pitfall 3: Weak Governance and Access Control

Problem: Post-migration, Superset becomes a free-for-all. Users export sensitive data; dashboards are created without oversight; data lineage is lost.

Solution:

• Define governance upfront (roles, access levels, approval workflows).
• Implement RBAC strictly: viewers can’t export; analysts can’t modify production dashboards.
• Use a data catalogue (e.g., Apache Atlas, Collibra) to track lineage and ownership.
• Audit dashboard creation; require business owner sign-off before production deployment.

Pitfall 4: Insufficient User Training

Problem: Users find Superset’s interface unfamiliar (compared to Qlik). Adoption stalls; teams revert to Qlik or bypass analytics entirely.

Solution:

• Invest in training early (weeks 7–8 of pilot).
• Offer multiple formats: group workshops, video tutorials, one-on-one coaching.
• Create a user community: monthly “analytics office hours” for questions and feedback.
• Highlight early wins: showcase dashboards that deliver new insights or solve business problems.

Pitfall 5: Not Planning for Post-Migration Support

Problem: Migration team leaves after go-live; internal team lacks knowledge to operate Superset. Escalations pile up; platform stability degrades.

Solution:

• Plan knowledge transfer during weeks 13–16 (not after).
• Ensure internal team shadows the migration team for 2–4 weeks post-go-live.
• Document everything: architecture, runbooks, troubleshooting guides, SLAs.
• Maintain a vendor relationship (or fractional CTO advisory) for escalations and strategic guidance.

Pitfall 6: Migrating Zombie Dashboards

Problem: 20–30% of Qlik dashboards are unused (last accessed >6 months ago). Migrating them wastes effort and clutters the Superset environment.

Solution:

• Audit Qlik usage (week 1–2); identify dashboards with <5 views per month.
• Archive these; don’t migrate.
• If a user requests one later, rebuild it on-demand (typically 2–4 hours).
• This frees 20–30% of migration effort for higher-value work.

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Next Steps and Getting Started

If your government organisation is ready to migrate from Qlik to Superset, here’s how to begin:

1. Conduct an Initial Assessment (Week 1)

Inventory your Qlik estate. Answer these questions:

• How many Qlik apps/dashboards do you operate?
• What’s your current user base and adoption rate?
• Which data sources do you connect to?
• What’s your annual Qlik spend (licensing + infrastructure + team)?

This assessment takes 1–2 weeks and costs $10,000–$15,000. It’s the foundation for your migration plan.

2. Define Your Target Architecture

Decide:

• Will you deploy Superset on AWS, Azure, or on-premises?
• How will you handle authentication (LDAP, SAML, OAuth2)?
• What’s your data refresh strategy (nightly, hourly, real-time)?
• Who owns governance and access control?

This typically requires 1–2 days of architecture workshops with your CIO, security lead, and data steward.

3. Build a Business Case

Quantify:

• Cost savings (licensing, infrastructure, team efficiency).
• Time-to-value (faster dashboard delivery, broader user access).
• Risk mitigation (vendor independence, compliance alignment).
• Strategic alignment (modern data stack, cloud-native architecture).

Present to your executive sponsor and finance team. Secure budget approval before committing to migration.

4. Partner with Experts

Consider engaging a vendor or fractional CTO team to guide the migration. Look for partners with:

• Government and compliance experience (IRAP, Essential Eight, Privacy Act).
• Superset and modern data stack expertise.
• Sydney or Australian presence (for on-site support and cultural alignment).

PADISO offers platform development services across Australia, with specialisation in government and public-sector teams. We’ve helped 50+ organisations modernise their analytics stacks and achieve compliance. Our services include Superset deployment, data pipeline engineering, and fractional CTO advisory to guide your technical strategy.

Alternatively, review case studies from comparable organisations to see real outcomes.

5. Start with a Proof of Concept

Don’t commit to a full migration immediately. Run a 4-week PoC:

• Deploy Superset on test infrastructure.
• Migrate 3–5 representative dashboards.
• Engage 20–30 pilot users; gather feedback.
• Validate performance, security, and compliance.

This reduces risk and builds internal confidence before full cutover.

6. Plan Your Cutover Timeline

Once PoC is successful, commit to a 12–16 week migration:

• Weeks 1–4: PoC and infrastructure.
• Weeks 5–12: Pilot and dashboard migration.
• Weeks 13–16: Cutover and go-live.

Build in buffer time for unexpected issues (typically 10–15% of project duration).

7. Secure Sponsorship and Governance

Establish a steering committee with:

• Executive sponsor (CIO or agency head).
• Technical lead (your architect or fractional CTO).
• Data steward (owner of governance and access control).
• Business representatives (from key user departments).

Meet fortnightly to review progress, address blockers, and adjust timeline as needed.

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Conclusion

Migrating from Qlik to Superset is a strategic move for Australian government organisations. It unlocks cost savings (40–60% per-user reduction), accelerates time-to-insight (4–6 weeks vs. 12–16 weeks), and aligns with sovereign, compliance-first infrastructure principles.

The playbook outlined here—scoping, governance, cost benchmarking, and cutover pattern—reflects 50+ successful migrations across government, finance, and enterprise sectors. Key success factors are:

1. Upfront assessment: Understand your Qlik estate and target architecture before committing.
2. Phased approach: PoC → Pilot → Cutover reduces risk and builds confidence.
3. Governance and compliance: Bake in RBAC, audit trails, and compliance frameworks from day one.
4. Expert partnership: Engage vendors or fractional CTOs with government and Superset experience.
5. User-centric delivery: Invest in training, support, and feedback loops; adoption is the true measure of success.

If you’re ready to start, reach out to PADISO’s platform development team or book a discovery call with a fractional CTO in your city (Sydney, Melbourne, Canberra, Brisbane, Adelaide, Darwin). We’ll help you assess your current state, build a business case, and execute a migration that delivers real results.