Migrating from Qlik to Superset for PE Portco Organisations

Table of Contents

• Why PE Portcos Move Away from Qlik
• The Business Case: Cost, Speed, and Flexibility
• Pre-Migration Assessment and Scoping
• Governance and Data Architecture
• Cost Benchmarks and ROI
• The Cutover Pattern: Planning and Execution
• Common Migration Pitfalls and How to Avoid Them
• Post-Migration Optimisation and Scaling
• Next Steps and Getting Started

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Why PE Portcos Move Away from Qlik

Private equity portfolio companies often inherit analytics stacks that made sense five years ago but no longer align with modern operational needs. Qlik has been a workhorse for many mid-market and enterprise organisations, but it carries structural constraints that become increasingly expensive and inflexible as businesses scale.

The core problem is licensing. Qlik operates on a per-user, per-named-user model that penalises broad democratisation. When a PE firm acquires a business with 50 Qlik users and wants to scale the analytics function to 500 users across operational teams, the per-seat cost becomes prohibitive. A single named user licence can cost $1,500–$3,000 annually depending on the module and region, which means adding analytics to 450 new users could cost $675,000–$1.35 million per year.

Beyond cost, Qlik’s architecture makes it difficult to embed analytics into operational workflows. If your portco is building a customer-facing SaaS product or needs embedded dashboards in internal tools, Qlik licensing and deployment models force expensive workarounds. Modern analytics platforms like Apache Superset, by contrast, are open-source and designed for embedding, multi-tenancy, and programmatic access.

The third driver is speed to insight. Qlik’s data model requires upfront schema design and ETL discipline. Superset, when paired with modern data warehouses like Snowflake, Databricks, or ClickHouse, allows analysts and engineers to iterate faster on dashboards and queries without heavyweight data modelling overhead.

For PE firms running operational value creation programmes across their portfolio, migration from Qlik to Superset typically unlocks 30–50% cost savings on analytics licensing within 18 months, whilst simultaneously improving time-to-dashboard and enabling self-service analytics at scale.

The Business Case: Cost, Speed, and Flexibility

Licensing and Total Cost of Ownership

Qlik’s per-user licensing model is straightforward to budget but expensive to scale. A typical mid-market portco with 100–500 employees might have 20–50 Qlik users at an annual cost of $30,000–$150,000. When you add in infrastructure (Qlik Sense cloud or on-premises), support contracts, and occasional consulting for data model changes, total cost of ownership often reaches $200,000–$400,000 per year for a single portfolio company.

Superset, by contrast, is open-source. There are no per-user fees. You pay for infrastructure (cloud compute, database, storage), engineering time to maintain and extend the platform, and optional commercial support if you choose it. For a mid-market portco, a fully managed Superset instance on AWS or Azure typically costs $15,000–$50,000 annually, including infrastructure and a fractional data engineer or analyst to maintain it.

The savings are real, but the comparison is only valid if you account for implementation cost. Migrating from Qlik to Superset is not a simple data export. You need to:

• Audit all existing Qlik applications, dashboards, and data models
• Map Qlik logic to SQL and Superset chart definitions
• Test equivalence between old and new dashboards
• Train users on the new interface and self-service capabilities
• Establish governance and access control policies

A well-scoped migration for a mid-market portco typically costs $80,000–$250,000 in professional services, depending on complexity. If your Qlik spend is $300,000 per year, you break even in 4–8 months and save $150,000–$250,000 annually thereafter.

Speed and Flexibility

Qlik’s strength is in exploratory analysis and ad hoc discovery within pre-built data models. Its weakness is in time-to-production for new dashboards and in embedding analytics into operational systems.

With Superset, a skilled analyst can build a new dashboard in hours, not days. Superset integrates natively with modern data warehouses and can query data directly without intermediate ETL. When paired with Apache Airflow documentation for workflow orchestration, you can automate data preparation and refresh cycles with fine-grained control.

For PE portcos that are modernising their tech stacks—consolidating data sources, building new SaaS products, or integrating acquired companies—Superset’s flexibility and open-source architecture make it far easier to adapt analytics to new business models.

Multi-Tenancy and Embedded Analytics

If your portco operates a B2B SaaS product or needs to embed dashboards into customer-facing applications, Qlik’s licensing and deployment constraints create friction. Each customer instance might require a separate Qlik licence, or you’d need to architect complex workarounds.

Superset was built for embedding. You can instantiate multiple Superset databases and datasets per tenant, control access programmatically via Flask-Login documentation, and serve dashboards to end users without per-seat licensing. This is a game-changer for SaaS businesses and enables analytics-driven customer experiences that would be cost-prohibitive with Qlik.

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Pre-Migration Assessment and Scoping

Before you commit to a migration timeline, you need a clear inventory of what you’re moving and why. This phase typically takes 2–4 weeks and sets the foundation for realistic budgets and timelines.

Audit Your Qlik Estate

Start by cataloguing every Qlik application, dashboard, report, and data source currently in use. Many organisations discover that 30–50% of their Qlik assets are either obsolete, rarely used, or redundant. This is your first opportunity to simplify.

For each Qlik application, document:

• Purpose and users: Who relies on this dashboard? Is it operational (used daily) or strategic (monthly reporting)?
• Data sources: Which databases, data warehouses, or ETL systems feed this application?
• Refresh frequency: Is this real-time, hourly, daily, or weekly?
• Interactivity and complexity: How many fields, selections, and calculated dimensions does the data model contain?
• Access control: Who has permission to view, edit, or administer this application?

Use this audit to classify applications into tiers:

• Tier 1 (Critical): Operational dashboards used daily by 10+ users. Must migrate with high fidelity.
• Tier 2 (Important): Strategic or departmental dashboards used weekly by 5–10 users. Migrate with good fidelity; some UI differences acceptable.
• Tier 3 (Legacy): Rarely used or redundant dashboards. Candidate for retirement or simplified replacement.

Typically, 20–30% of a Qlik estate is Tier 1, 40–50% is Tier 2, and 20–30% is Tier 3. You may choose to retire Tier 3 entirely, saving migration effort.

Define Success Criteria

Before migration begins, align stakeholders on what success looks like. For PE portcos, this usually means:

• Cost reduction: Target 30–50% reduction in analytics licensing and infrastructure costs within 12 months.
• Time to dashboard: New dashboards built in < 5 business days (vs. 2–3 weeks in Qlik).
• User adoption: 80%+ of previous Qlik users actively using Superset within 3 months.
• Data freshness: Maintain or improve on current refresh SLAs (e.g., 4-hour refresh for operational dashboards).
• Compliance: Maintain or improve on current audit readiness and access control.

Document these criteria in a migration charter signed by the CFO, CTO, and business unit heads. This becomes your north star when trade-offs arise.

Assess Your Data Architecture

Superset is a visualisation and query layer. It assumes you have a modern data warehouse or data lake beneath it. If your Qlik setup is directly connected to operational databases (ERP, CRM, legacy systems), you’ll need to build or improve your data warehouse layer as part of the migration.

Asess:

• Current data warehouse: Do you have a Snowflake, BigQuery, Databricks, or similar? If not, budget for a new data warehouse project.
• ETL and data pipelines: How are data sources currently extracted, transformed, and loaded? Can these be automated with Apache Airflow documentation, or do you have manual processes?
• Data quality and lineage: How do you currently track data quality and lineage? Superset has limited built-in data governance; you may need to implement dbt, Dataedo, or similar tools.
• Query performance: How do your current data sources perform under typical query loads? Superset can expose performance bottlenecks if your underlying data layer isn’t optimised.

If you don’t have a modern data warehouse, the migration becomes a dual project: build the warehouse and migrate analytics simultaneously. This adds 8–12 weeks to the timeline and $50,000–$150,000 to the budget, but it’s often the right long-term investment.

For PE portcos with multiple acquired companies, this is an opportunity to consolidate data sources and build a unified analytics layer. Rather than migrating Qlik to Superset in isolation, consider a broader platform development in Australia or platform development in United States initiative that includes data warehouse consolidation, ETL automation, and analytics modernisation as a single programme.

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Governance and Data Architecture

Access Control and Authentication

Qlik has role-based access control (RBAC) built into the platform. Users authenticate via LDAP, SAML, or local credentials, and permissions cascade from application to sheet to object level.

Superset also supports RBAC, but it works differently. Superset manages access at the database, dataset, and dashboard level. You can integrate Superset with your existing identity provider (Active Directory, Okta, Auth0) via SAML or OAuth, and you can define fine-grained permissions using Superset’s role and permission system.

During migration, you’ll need to:

1. Map Qlik roles to Superset roles: Document every Qlik security role and define an equivalent in Superset.
2. Audit user access: Pull a report of every Qlik user and their permissions. This is often messy; use it as an opportunity to clean up access.
3. Implement row-level security (RLS): If your Qlik applications use section access or row-level filters based on user attributes, you’ll need to implement RLS in Superset. This is typically done via SQLAlchemy documentation custom SQL or Superset’s native RLS feature.
4. Test authentication flows: Ensure your identity provider integration works before cutover. Test single sign-on (SSO), role sync, and permission inheritance.

For PE portcos with multiple portfolio companies, consider a federated governance model: each portfolio company has its own Superset instance or dataset namespace, but they share a common authentication layer and governance framework. This allows each company autonomy whilst maintaining group-level audit and compliance.

Data Lineage and Metadata Management

Qlik’s data model is self-contained; it’s often difficult to trace data lineage back to source systems. Superset doesn’t solve this problem on its own—it’s a visualisation tool, not a data governance platform.

To build proper data lineage and metadata management, integrate Superset with a modern data cataloguing tool. Options include:

• dbt (data build tool): If your data warehouse uses dbt for transformation logic, dbt’s metadata can be integrated into Superset via dbt Cloud or custom connectors.
• Apache Atlas or similar: For organisations that need comprehensive data governance, Apache Atlas provides lineage tracking, metadata management, and access control.
• Databricks Lineage: If you’re using Databricks as your data warehouse, Databricks Unity Catalog provides lineage and governance natively.

For a PE portco migration, recommend starting with dbt if your team is building new data pipelines, or with Databricks Lineage if you’re consolidating multiple acquired companies onto Databricks. These integrations typically add 2–4 weeks to the implementation timeline but pay dividends in governance and compliance.

Data Refresh and SLA Management

Qlik typically uses scheduled reloads managed through Qlik’s scheduler or external tools like Talend or Informatica.

Superset doesn’t manage data refresh directly—the underlying database or data warehouse does. You’ll need to:

1. Migrate refresh logic to your data warehouse: If you’re using Snowflake or BigQuery, define refresh schedules in those platforms. If you’re using Databricks, use Databricks Jobs or Delta Live Tables.
2. Automate with Apache Airflow: For complex multi-step ETL, use Apache Airflow documentation to orchestrate data preparation, transformations, and refresh cycles. Airflow integrates well with modern data warehouses and can trigger Superset cache invalidation on completion.
3. Define and monitor SLAs: Document refresh SLAs for each dataset (e.g., “operational dashboards refresh every 4 hours, strategic reports refresh daily”). Use Airflow monitoring and alerting to ensure SLAs are met.

For PE portcos, this is an opportunity to centralise and automate ETL across the portfolio. Rather than each company maintaining its own Qlik reload schedules, build a shared Airflow instance that manages data pipelines for all portfolio companies. This reduces operational overhead and improves consistency.

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Cost Benchmarks and ROI

Migration Costs

A typical Qlik-to-Superset migration for a mid-market portco breaks down as follows:

Component	Low Estimate	High Estimate	Notes
Assessment & Scoping	$8,000	$15,000	2–4 weeks, 1 senior engineer
Data Architecture & Warehouse	$0	$100,000	Only if building new warehouse
Dashboard & Report Migration	$30,000	$120,000	Depends on # of Tier 1 & 2 dashboards
Governance & Access Control	$10,000	$30,000	Identity integration, RLS setup
Testing & QA	$8,000	$25,000	Equivalence testing, user acceptance
Training & Documentation	$5,000	$15,000	User training, runbooks, knowledge base
Contingency (10–15%)	$6,000	$30,000	Scope creep, complexity surprises
Total	$67,000	$335,000	Typical range: $120,000–$200,000

The wide range reflects the complexity of your Qlik estate and the maturity of your target data architecture. A portco with 20 simple dashboards and an existing data warehouse might migrate for $80,000. A portco with 100 complex dashboards, custom code, and no data warehouse might spend $300,000+.

Annual Operating Costs

Once migrated, annual costs for Superset typically include:

Component	Annual Cost	Notes
Cloud Infrastructure (Compute, Storage, Database)	$15,000–$50,000	Depends on query volume, data size, and cloud provider
Data Warehouse	$20,000–$100,000	Snowflake, BigQuery, Databricks, ClickHouse, etc.
Fractional Data Engineer / Analyst	$40,000–$80,000	0.5–1 FTE to maintain dashboards, pipelines, and user support
Commercial Support (Optional)	$0–$20,000	Optional; Superset community support is free
Total	$75,000–$250,000	Typical mid-market: $120,000–$180,000

Compare this to your current Qlik spend. If you’re paying $300,000+ annually on Qlik licensing alone, the ROI is compelling:

• Migration cost: $150,000 (mid-range estimate)
• Year 1 savings: $300,000 (Qlik) – $150,000 (Superset) = $150,000
• Payback period: 1 year
• Year 2+ savings: $150,000+ annually

Over a 3-year horizon, a $150,000 migration investment generates $450,000+ in savings, a 3x return. For PE firms, this is attractive: the migration is capital-efficient and delivers measurable cost reduction within the first year.

Cost Optimisation Tactics

To maximise ROI, implement these cost control measures:

1. Right-size your data warehouse: Don’t overbuild. Start with a small Snowflake or Databricks cluster and scale as query volume grows. Many portcos can start with < $5,000/month in warehouse costs.
2. Retire redundant dashboards: Use your Tier 3 audit to decommission dashboards no one uses. This reduces maintenance burden and simplifies the migration scope.
3. Automate data refresh: Use serverless or scheduled compute (e.g., Snowflake Tasks, Databricks Jobs) rather than always-on infrastructure.
4. Leverage open-source tools: Superset is free. Use dbt for transformations (free tier available). Use Apache Airflow for orchestration (self-hosted or Astronomer). Minimise proprietary tools.
5. Centralise infrastructure for the portfolio: If you have multiple portfolio companies, consider a shared Superset instance with multi-tenancy, a shared data warehouse, and shared Airflow. This amortises costs across the portfolio.

For PE firms running 10+ portfolio companies, a shared analytics platform can reduce per-company costs by 40–60% compared to standalone implementations.

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The Cutover Pattern: Planning and Execution

The cutover—the transition from Qlik to Superset—is the highest-risk phase. A poorly executed cutover can disrupt business operations, lose stakeholder confidence, and create technical debt. Here’s a proven pattern used by PE-backed companies and enterprise teams.

Phase 1: Pilot (Weeks 1–4)

Start with a small, self-contained Tier 1 dashboard that is critical but relatively simple. This might be a daily operational dashboard used by the finance team or a sales dashboard used by the VP of Sales.

Goals:

• Prove the technical approach (data sources, refresh, access control)
• Build confidence with a key stakeholder group
• Identify and resolve integration issues before full migration
• Establish the team’s rhythm and processes

Deliverables:

• Superset instance deployed and configured
• Pilot dashboard built, tested, and live
• Access control and authentication working
• Data refresh working on schedule
• User training completed; pilot users actively using Superset

Success criteria:

• Pilot users prefer Superset to Qlik (or are neutral)
• No data quality issues
• Refresh SLA met
• Zero unplanned downtime

If the pilot succeeds, you’ve validated the approach and built momentum. If it struggles, you’ve discovered issues early when they’re cheap to fix.

Phase 2: Wave 1 Migration (Weeks 5–12)

Once the pilot is stable, migrate the remaining Tier 1 dashboards and the highest-priority Tier 2 dashboards. This typically represents 40–60% of your dashboard portfolio but 80%+ of actual usage.

Execution approach:

1. Parallel run: Run Qlik and Superset dashboards side-by-side for 2–4 weeks. Users see both versions and can compare. This builds confidence and catches discrepancies.
2. Staged cutover: Cut users over to Superset by department or function, not all at once. This allows you to provide focused support and manage change.
3. Dashboard equivalence testing: Before users see a Superset dashboard, QA team compares it side-by-side with the Qlik original. Verify all metrics, filters, and interactivity match.
4. User training: Conduct 30-minute hands-on training sessions for each user group. Focus on what’s different (interface, self-service capabilities) and what’s the same (data, metrics, refresh schedule).

Timeline:

• Week 5: Build and test first 10 dashboards
• Week 6: Launch parallel run; begin user training
• Week 7–8: Cutover first user group; build next batch of dashboards
• Week 9–10: Cutover second user group; build Tier 2 dashboards
• Week 11–12: Cutover remaining Tier 1 and priority Tier 2 dashboards; stabilise

Success criteria:

• 90%+ of Tier 1 dashboards live and stable
• 80%+ of users actively using Superset
• < 5% of issues are data quality or equivalence problems
• Qlik usage declining (but not yet shut down)

Phase 3: Wave 2 and Cleanup (Weeks 13–20)

Migrate remaining Tier 2 dashboards and retire Tier 3 dashboards. Focus on automation and self-service enablement.

Activities:

1. Migrate remaining dashboards: Use lessons learned from Wave 1 to accelerate. Most Tier 2 dashboards should take 2–3 days each.
2. Enable self-service: Train power users on Superset’s SQL Lab and dashboard creation tools. Empower them to build new dashboards without engineering support.
3. Retire Tier 3 dashboards: Formally decommission Tier 3 dashboards. Archive the Qlik applications. This reduces operational noise and clarifies the new baseline.
4. Optimise performance: Monitor Superset and data warehouse query performance. Optimise slow queries, add caching, tune refresh schedules.
5. Decommission Qlik: Once all dashboards are stable in Superset and users are confident, decommission Qlik. Cancel licences and infrastructure.

Timeline:

• Week 13–16: Migrate remaining Tier 2 dashboards
• Week 17–18: Retire Tier 3 dashboards; decommission Qlik
• Week 19–20: Optimise performance; stabilise; plan for scale

Success criteria:

• 100% of Tier 1 and Tier 2 dashboards live and stable
• Qlik decommissioned; no users accessing it
• Superset adoption 85%+
• Self-service dashboard creation happening
• Total cost of ownership reduced 30–50% vs. Qlik

Phase 4: Optimisation and Scale (Weeks 21+)

Once the migration is complete, focus on optimisation, scaling, and unlocking new capabilities.

Activities:

1. Performance tuning: Use Superset and database monitoring to identify slow queries. Optimise data models, add indices, implement caching strategies.
2. Advanced analytics: Introduce forecasting, cohort analysis, and other advanced analytics capabilities that were difficult in Qlik.
3. Embedded analytics: If your portco operates a SaaS product, embed Superset dashboards into your application. This was difficult with Qlik; now it’s straightforward.
4. Expand self-service: Train more users on SQL Lab. Build a library of reusable SQL templates and datasets. Enable business users to answer their own questions.
5. Cross-portfolio analytics: If you have multiple portfolio companies, consolidate data and build cross-company dashboards (e.g., comparing performance across portfolio).

This phase has no fixed end date; it’s ongoing. The goal is to shift analytics from a cost centre (maintaining dashboards) to a value centre (enabling data-driven decisions and new revenue opportunities).

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Common Migration Pitfalls and How to Avoid Them

Pitfall 1: Underestimating Data Architecture Gaps

Problem: Your Qlik setup connects directly to operational databases (ERP, CRM, legacy systems). You assume Superset will do the same. But Superset queries are slower and less predictable against transactional databases.

Solution: Build a proper data warehouse or data lake before migration. Use Trino documentation or similar tools to federate access across multiple sources if a full warehouse is infeasible. Budget 8–12 weeks and $50,000–$150,000 for this if you don’t have one.

Pitfall 2: Scope Creep

Problem: During the audit phase, you discover 100 Qlik dashboards instead of 50. You also discover custom Qlik extensions and complex data models that will take months to replicate. The migration timeline balloons.

Solution: Use your Tier 1/2/3 classification ruthlessly. Migrate Tier 1 and high-value Tier 2 dashboards only. Retire Tier 3. For complex Qlik extensions, evaluate whether the same capability is needed in Superset or whether you can simplify the user experience.

Pitfall 3: Insufficient User Training and Change Management

Problem: Users are trained once, then left to figure out Superset on their own. They get frustrated, revert to Qlik, and the migration stalls.

Solution: Invest in change management. Conduct multiple training sessions (initial, follow-up, advanced). Create a “Superset champions” group in each department who become power users and peer support. Provide a help desk or Slack channel for questions. Celebrate early wins.

Pitfall 4: Ignoring Access Control and Compliance

Problem: You migrate dashboards without properly implementing row-level security (RLS) or access control. Users see data they shouldn’t, or compliance auditors flag the gap.

Solution: Implement RLS before cutover. Test it thoroughly. For regulated industries (finance, healthcare), document your access control model and get compliance approval before migration. Use tools like Apache Superset Documentation – Introduction to understand Superset’s RLS capabilities.

Pitfall 5: Not Optimising the Data Warehouse

Problem: You migrate to Superset but your data warehouse is slow or expensive. Users complain about dashboard load times. Costs spiral.

Solution: Profile your data warehouse before migration. Identify slow queries. Optimise data models, add indices, and implement materialized views or caching. Use tools like Databricks Blog to stay current on best practices for your specific data platform.

Pitfall 6: Losing Institutional Knowledge

Problem: Your Qlik data model contains business logic and calculations that no one has documented. When you migrate, you lose context. Dashboards look the same but calculations are subtly wrong.

Solution: During the audit phase, interview every Qlik power user and document the business logic behind each dashboard. Create a data dictionary that maps Qlik calculated fields to SQL expressions in Superset. This takes time but prevents costly mistakes.

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Post-Migration Optimisation and Scaling

Consolidating Multiple Portfolio Companies

If you’re managing multiple portfolio companies, post-migration is the ideal time to consolidate their analytics infrastructure. Rather than each company running its own Superset instance, consider a shared platform with multi-tenancy.

Benefits:

• Reduced infrastructure costs (one Superset instance instead of five)
• Reduced operational overhead (one team manages all instances)
• Ability to build cross-portfolio dashboards and benchmarking
• Easier to implement consistent governance and compliance

Implementation:

1. Shared Superset instance: Deploy a single Superset instance with row-level security (RLS) to isolate each company’s data.
2. Shared data warehouse: Consolidate data from all portfolio companies into a single warehouse (Snowflake, Databricks, ClickHouse). Use schema or database-level isolation for each company.
3. Federated authentication: Implement a shared identity provider (Okta, Azure AD) so users from all portfolio companies authenticate centrally.
4. Shared Airflow: Manage data pipelines for all companies in a single Airflow instance, with separate DAGs for each company.

For PE firms, this model is highly attractive. It reduces cost per portfolio company by 40–60% and enables data-driven portfolio management (e.g., comparing revenue growth, customer acquisition cost, or operational efficiency across all companies).

If you’re operating at scale across multiple geographies, consider platform development in Melbourne, platform development in Sydney, platform development in New York, platform development in Chicago, and platform development in Austin to build region-specific instances with shared governance.

Enabling Self-Service Analytics

Post-migration, empower business users to build their own dashboards and answer their own questions. This shifts analytics from a bottleneck to a competitive advantage.

Tactics:

1. Curated datasets: Create a library of clean, well-documented datasets that users can query without touching raw tables. Use Superset’s dataset feature to define these.
2. SQL templates: Build a library of common SQL queries (e.g., “monthly revenue by segment”, “customer cohort analysis”) that users can copy and modify.
3. Dashboard templates: Create reusable dashboard templates for common use cases (e.g., departmental KPI dashboards, customer health dashboards).
4. SQL Lab training: Conduct hands-on training on Superset’s SQL Lab. Show users how to write safe queries, use filters, and export results.
5. Office hours: Hold weekly “analytics office hours” where users can ask questions and get help building dashboards.

Over 6–12 months, you should see 30–50% of new dashboards created by business users, not engineers. This frees your analytics team to focus on data quality, governance, and strategic analysis.

Advanced Analytics and Embedded Dashboards

Once the core migration is stable, consider these next-level capabilities:

Embedded analytics: If your portco operates a B2B SaaS product, embed Superset dashboards into your application. Use Superset’s embedding API to serve dashboards to customers without requiring a Superset login. This enables white-label analytics and is a differentiator vs. Qlik.

Advanced analytics: Integrate Superset with Python-based analytics libraries (Pandas, Scikit-learn, Plotly) to enable forecasting, anomaly detection, and machine learning insights. Superset’s Python integration is more flexible than Qlik’s.

Real-time dashboards: If your business requires real-time insights (trading, fraud detection, operational monitoring), use Superset with a real-time data source like ClickHouse or Kafka. Qlik can do this but it’s more complex and expensive.

For PE portcos pursuing operational value creation, these capabilities are often the difference between a good migration and a transformational one. Modern analytics infrastructure enables faster decision-making and better operational outcomes.

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Recommended Approach for PE Portcos

Based on our experience working with PE-backed companies and operators modernising analytics stacks, here’s the recommended approach:

1. Assess and Scope (Weeks 1–4)

• Audit your Qlik estate; classify as Tier 1/2/3
• Evaluate your data architecture; identify gaps
• Define success criteria and ROI targets
• Estimate migration cost and timeline
• Secure executive alignment and budget

2. Build the Foundation (Weeks 5–8)

• Deploy Superset on your cloud platform (AWS, Azure, GCP)
• Build or consolidate your data warehouse (if needed)
• Implement authentication and access control
• Set up monitoring and alerting

3. Pilot and Learn (Weeks 9–12)

• Migrate one critical Tier 1 dashboard
• Run parallel with Qlik; gather user feedback
• Refine the process; document lessons learned
• Build confidence with early adopters

4. Wave 1 Migration (Weeks 13–20)

• Migrate remaining Tier 1 and priority Tier 2 dashboards
• Conduct user training; manage change
• Retire Tier 3 dashboards
• Decommission Qlik

5. Optimise and Scale (Weeks 21+)

• Tune performance; enable self-service
• Consolidate portfolio companies (if applicable)
• Unlock advanced analytics and embedded dashboards
• Measure ROI and plan next phase

For PE firms with fractional or full-time engineering leadership needs, consider engaging a fractional CTO in Sydney or fractional CTO in Melbourne to oversee the migration and establish governance. A fractional CTO can manage vendor selection, oversee implementation, and ensure the migration aligns with your broader technology strategy.

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Next Steps and Getting Started

If you’re a PE firm or portco operator considering a migration from Qlik to Superset, here’s how to move forward:

Immediate Actions

1. Audit your Qlik estate: Pull a list of all Qlik applications, dashboards, and users. Estimate the cost of your current Qlik spend.
2. Assess your data architecture: Do you have a modern data warehouse? If not, evaluate Snowflake, BigQuery, or Databricks.
3. Define your success criteria: What does a successful migration look like? Cost reduction? Speed to dashboard? User adoption?
4. Get internal alignment: Secure buy-in from your CFO (cost savings), CTO (technical approach), and business unit heads (user impact).

Engage External Expertise

Unless your team has deep experience with Superset and modern data warehouses, engage a partner to guide the migration. Look for:

• Platform engineering expertise: Experience building data warehouses, ETL pipelines, and analytics infrastructure. PADISO’s platform development in Canada and platform development in United States teams have shipped Superset migrations for PE-backed companies.
• Superset expertise: Deep knowledge of Superset’s architecture, security model, and integration patterns.
• Change management: Experience helping organisations adopt new tools and building user confidence.
• PE experience: Understanding of portfolio company dynamics, cost pressures, and value creation timelines.

PADISO’s services include custom software development, platform engineering, and fractional CTO support. We’ve helped PE portcos migrate from legacy BI tools to modern open-source stacks and have seen the tangible cost and agility benefits.

Building Your Business Case

Use the cost benchmarks and ROI analysis in this guide to build your business case. Key talking points:

• Cost reduction: 30–50% reduction in analytics licensing and infrastructure costs within 12 months
• Speed: New dashboards built in days, not weeks
• Flexibility: Embedded analytics, multi-tenancy, and self-service capabilities enable new revenue models
• Governance: Modern access control, data lineage, and compliance readiness
• Scalability: Shared infrastructure across portfolio companies reduces per-company cost

For a typical mid-market portco, the migration pays for itself in 12 months and delivers $150,000+ in annual savings thereafter. For PE firms with 5+ portfolio companies, the savings are substantially larger.

Timeline and Commitment

Expect a 5–6 month migration timeline from scoping to decommissioning Qlik. Plan for:

• Internal commitment: 0.5–1 FTE from your business (project sponsor, data owners, power users)
• External commitment: 1–2 FTEs from your implementation partner
• Budget: $80,000–$250,000 in professional services, depending on complexity
• Ongoing: 0.5–1 FTE to maintain Superset and manage analytics post-migration

The investment is material but the returns are substantial. For PE firms, this is a high-ROI value creation initiative that typically sits in the first 100 days of a new platform company acquisition.

Get in Touch

If you’d like to discuss your specific situation—whether you’re scoping a migration, evaluating vendors, or planning a portfolio-wide analytics consolidation—reach out to PADISO. We’ve guided dozens of PE-backed companies through similar transitions and can help you navigate the technical, organisational, and financial dimensions.

Visit PADISO’s services page to learn more about platform engineering, fractional CTO support, and custom software development. Or book a call with our team to discuss your migration strategy.

The shift from Qlik to Superset is not just a tool change; it’s an opportunity to modernise your analytics infrastructure, reduce costs, and enable faster, data-driven decision-making across your organisation. Done well, it’s one of the highest-ROI technology investments a PE portco can make.