Migrating from Sisense to Superset for PE Portco Organisations

Table of Contents

1. Executive Summary: Why PE Portcos Move from Sisense to Superset
2. Pre-Migration Assessment and Scoping
3. Cost Benchmarking and Financial Case
4. Data Governance and Compliance Readiness
5. Technical Architecture and Cutover Planning
6. Dashboard and Reporting Migration Strategy
7. Team Enablement and Change Management
8. Post-Migration Optimisation and Scaling
9. Common Pitfalls and How to Avoid Them
10. Next Steps and Implementation Timeline

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Executive Summary: Why PE Portcos Move from Sisense to Superset

Private equity portfolio companies face a unique operational challenge: inherited analytics stacks that don’t scale, cost too much per seat, and lock teams into expensive licensing models. Sisense, whilst powerful, often becomes a bottleneck for growing organisations—especially when you’re consolidating multiple portfolio companies or preparing for an exit.

Apache Superset offers PE-backed teams a fundamentally different model. It’s open-source, self-hosted or cloud-deployed, and scales to hundreds of users without per-seat licensing. For a typical mid-market portco running 50–200 analytical users, the annual cost difference between Sisense and Superset can exceed $500,000. That’s material.

But migration isn’t trivial. You’re moving dashboards, datasets, user permissions, and operational workflows from a proprietary platform to an open-source one. Done poorly, you’ll lose institutional knowledge, break reporting schedules, and frustrate your business users. Done well, you’ll cut costs, improve governance, and unlock agility.

This guide is built for PE operators, fractional CTOs, and portfolio company heads of engineering who need to execute a Sisense-to-Superset migration in weeks, not months. We’ll cover scoping, cost benchmarking, governance, the technical cutover, and the human side—how to move your teams without breaking their workflows.

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Pre-Migration Assessment and Scoping

Inventory Your Sisense Estate

Before you plan a single dashboard migration, you need to know what you’re moving. Too many portcos underestimate this step and end up discovering hidden dashboards, orphaned datasets, and undocumented dependencies mid-project.

Start with a complete audit:

• Dashboard count and usage: How many dashboards exist? Which are actively used by business teams? Which are legacy or redundant?
• Data source inventory: How many databases, data warehouses, or APIs does Sisense connect to? Are they internal or third-party SaaS platforms?
• User and permission model: How many active users? Are permissions role-based, user-specific, or a hybrid? Which teams own which dashboards?
• Custom code and extensions: Does your Sisense instance use custom plugins, custom SQL, or Sisense’s JavaScript SDK? These don’t port directly to Superset.
• Refresh schedules and alerting: Which dashboards have scheduled refreshes? Are there email alerts or webhooks triggered by data changes?
• Embedded analytics: Are any Sisense dashboards embedded in your product or internal applications? This changes the migration scope significantly.

For a typical mid-market portco, this audit takes 2–3 weeks and uncovers 30–40% more complexity than initial estimates. Budget accordingly.

Define Migration Scope and Phases

Not all dashboards migrate at once. Successful portco migrations are phased:

Phase 1: Pilot (4–6 weeks) Select 3–5 high-value dashboards used by finance, operations, or the CEO. These become your proof-of-concept. You’ll learn what works, what breaks, and what your team needs to unlearn about Sisense.

Phase 2: Core Reporting (8–12 weeks) Move the dashboards that drive weekly or monthly business reviews. Finance, sales operations, and executive reporting. These are the ones that will cause pain if they break.

Phase 3: Departmental Analytics (12–16 weeks) Migrate remaining dashboards by business unit. Marketing, customer success, product, engineering. Less time-critical but higher volume.

Phase 4: Decommission and Optimise (ongoing) Retire Sisense licenses as teams transition. Optimise Superset performance based on real usage patterns.

This phased approach reduces risk, builds team confidence, and lets you adjust scope as you learn. It also spreads the operational load, which matters when you’re running a lean team.

Assess Your Data Architecture

Sisense is often deployed with direct database connections or ETL pipelines feeding it nightly. Superset works the same way, but the architecture matters for performance and cost.

Ask yourself:

• Do you have a data warehouse (Snowflake, BigQuery, Redshift, Databricks)? If yes, Superset will perform better and be cheaper to run.
• Are you querying operational databases directly? You’ll need to rearchitect this—Superset isn’t designed for heavy operational database load.
• What’s your current data freshness requirement? Real-time, hourly, daily, weekly? Superset’s caching and query performance will shape your refresh strategy.
• Do you have a dbt project or data transformation layer? This is gold for Superset—it lets you build semantic layers and reduce dashboard-level complexity.

If you don’t have a modern data warehouse, the Sisense-to-Superset migration is a good forcing function to build one. Many portcos use this moment to consolidate data sources, implement a proper ELT stack, and move away from direct operational database queries. That’s a bigger project, but it compounds the value of the migration.

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Cost Benchmarking and Financial Case

Sisense Cost Model

Sisense licensing is per-named user, with annual maintenance and support. A typical portco with 100 analytical users pays:

• Sisense licenses: 100 users × $3,000–$5,000 per user per year = $300,000–$500,000
• Maintenance and support: 20% of license cost = $60,000–$100,000
• Infrastructure: Hosted or on-premise, typically $30,000–$80,000 per year
• Professional services: Data modelling, dashboard build, training = $50,000–$150,000 per year

Total annual cost: $440,000–$830,000

For a growing portco, this creeps up. Add 50 users? You’re paying an extra $150,000–$250,000. That’s a hard stop on analytics democratisation.

Superset Cost Model

Superset shifts from per-user licensing to infrastructure and operational overhead:

• Cloud infrastructure (AWS/GCP/Azure): A production Superset instance with redundancy, monitoring, and backups costs $2,000–$8,000 per month, depending on query volume and concurrency. For most portcos, budget $40,000–$80,000 annually.
• Managed Superset platforms (e.g., Preset, which is Superset-as-a-service): $5,000–$15,000 per month for enterprise features, SSO, and managed hosting. More expensive than self-hosted, but removes operational burden.
• Internal engineering time: You need someone to maintain the Superset instance, manage users, tune queries, and handle upgrades. For a mid-market portco, budget 0.5–1 FTE ($80,000–$150,000 annually).
• Data warehouse costs: If you’re migrating to a modern data warehouse (Snowflake, BigQuery), expect $3,000–$15,000 per month depending on query volume and storage. But you’d likely use this for other analytics anyway, so it’s not purely incremental.

Total annual cost: $120,000–$250,000 (self-hosted) or $180,000–$350,000 (managed platform + data warehouse)

The Financial Case

For a 100-user portco moving from Sisense to self-hosted Superset:

• Year 1 savings: $440,000 (Sisense) − $150,000 (Superset + engineering) = $290,000
• Year 2+ savings: $290,000+ (Superset costs remain flat as you add users)

Even with migration costs ($100,000–$200,000), you break even in 6–9 months. For portcos with 150+ analytical users, the payback is 3–4 months.

That’s a strong financial case. But it only works if you:

1. Actually decommission Sisense (don’t run both in parallel longer than 2–3 months)
2. Don’t over-engineer Superset infrastructure (self-hosted is fine for most portcos)
3. Absorb the operational overhead with existing engineering staff (hiring a dedicated Superset admin erodes the savings)

Building the Business Case

When pitching this to the PE sponsor or board:

• Lead with the annual cost delta and payback period
• Show the headcount impact: you’re not hiring new analytics engineers, you’re redeploying existing ones
• Frame it as a modernisation play: Superset is open-source, cloud-native, and integrates with modern data stacks (dbt, Databricks, Snowflake). Sisense is legacy.
• Mention the user scaling story: once Superset is live, adding 50 more analytical users costs you nothing in licensing

For PE sponsors, this lands as a value-creation lever. It’s not sexy, but it’s real, measurable, and repeatable across the portfolio.

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Data Governance and Compliance Readiness

Permission Model Migration

Sisense and Superset both support role-based access control (RBAC), but the mechanics differ. You’ll need to rethink your permission model, not just copy it.

In Sisense, permissions are often granular: user-level access to specific dashboards, datasets, or rows. In Superset, the model is simpler: users have roles (Admin, Alpha, Gamma, SQL Lab), and dashboards/datasets are tagged or grouped.

When migrating:

• Map Sisense roles to Superset roles: Sisense “Viewer” → Superset “Gamma” (read-only), Sisense “Designer” → Superset “Alpha” (can create dashboards), Sisense “Admin” → Superset “Admin”.
• Use dataset-level permissions: Superset lets you restrict which datasets users can query. This is your primary control. If a user shouldn’t see financial data, they don’t get access to the financial dataset.
• Implement row-level security (RLS): If you need to show users only their region’s or customer’s data, Superset supports RLS via SQL conditions. This requires coordination with your data team but is powerful.
• Plan for SSO: If you’re not using SSO already, this is the moment to implement it. Superset integrates with Okta, Azure AD, and other identity providers. It makes user management dramatically easier as you scale.

Budget 2–4 weeks for permission model design and testing. This is where most migrations stumble—teams underestimate the complexity of translating Sisense permissions to Superset.

Compliance and Audit Readiness

If your portco needs to pass SOC 2 or ISO 27001 audits (increasingly common for B2B SaaS or healthcare portfolio companies), Superset requires intentional setup.

Key controls:

• Authentication and access logs: Superset logs all user actions (login, dashboard view, query execution). These logs are critical for audit trails. Ensure they’re being captured and retained.
• Data encryption in transit and at rest: If you’re using a managed platform like Preset or deploying on AWS, ensure TLS/SSL for data in transit and encryption at rest for sensitive data.
• Secrets management: Database credentials, API keys, and other secrets should be stored in a secrets manager (AWS Secrets Manager, HashiCorp Vault), not hardcoded.
• Audit logging for data access: If you’re handling PII or regulated data, you need to log who accessed what, when, and why. Superset supports this via its metadata database.
• Backup and disaster recovery: Superset’s configuration, dashboards, and metadata should be backed up regularly. Test your recovery process.

If compliance is a requirement, budget an extra 4–6 weeks for security hardening and audit preparation. This is where working with a fractional CTO with security experience becomes valuable—they’ll ensure your Superset deployment passes audit scrutiny.

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Technical Architecture and Cutover Planning

Deployment Architecture Decision

You have three primary options:

Option 1: Self-Hosted Superset (AWS/GCP/Azure)

You deploy Superset on your own cloud infrastructure. Full control, lowest per-seat cost, but you own operational overhead.

• Pros: Complete control, cheapest at scale, can customise deeply
• Cons: You manage upgrades, patches, scaling, backups, monitoring
• Best for: Portcos with strong engineering teams and mature cloud infrastructure
• Timeline: 4–8 weeks to production-ready deployment

Option 2: Managed Superset (Preset or similar)

You use a third-party managed platform built on Superset. They handle infrastructure, upgrades, and scaling.

• Pros: No operational overhead, built-in SSO, managed backups, faster time-to-value
• Cons: Higher per-user cost, less customisation, vendor dependency
• Best for: Portcos without dedicated DevOps/platform engineering capacity
• Timeline: 2–4 weeks to production

Option 3: Hybrid (Self-Hosted + Managed Services)

You deploy Superset yourself but use managed services for specific components (e.g., managed Postgres for metadata, managed Redis for caching).

• Pros: Balance of control and operational simplicity
• Cons: Still requires some operational overhead
• Best for: Portcos with medium engineering capacity
• Timeline: 6–10 weeks to production

For most PE portcos, Option 2 (Managed Superset) is the right call. The operational overhead of self-hosting isn’t worth the 20–30% cost savings unless you already have a strong platform engineering team. And if you do, you’re probably building custom analytics infrastructure anyway.

Data Source Configuration

Superset connects to your data warehouse or databases via SQLAlchemy drivers. Configuration is straightforward but requires careful planning.

Step 1: Identify all data sources From your Sisense audit, you know which databases, data warehouses, and APIs feed your dashboards. Each becomes a Superset “database” connection.

Step 2: Test connectivity Before migrating a single dashboard, ensure Superset can connect to each source. This is often where you discover network access issues, credential problems, or deprecated APIs.

Step 3: Configure caching and query limits Superset can cache query results to improve performance. For dashboards with heavy usage, set appropriate cache TTLs (time-to-live). Also set query timeouts—Superset won’t run queries longer than 5 minutes by default, which is sensible but worth confirming against your Sisense baseline.

Step 4: Set up query logging Ensure all queries executed via Superset are logged. This is critical for performance troubleshooting and compliance.

For portcos with multiple data sources, budget 2–3 weeks for data source configuration and testing. This is often where you discover that Sisense has been querying deprecated tables or that some “datasets” are actually complex SQL views that need refactoring.

The Cutover Plan

Your cutover plan is the operational sequence for moving from Sisense to Superset. It should be documented, sequenced, and rehearsed.

Week 1: Soft cutover Superset is live, but Sisense remains operational. Your business teams start using Superset dashboards. You monitor for issues, performance problems, or missing functionality. Sisense is still the source of truth.

Week 2–3: Parallel running Both systems are live. You’re validating that Superset dashboards match Sisense dashboards in terms of data, logic, and performance. You’re also training teams on Superset workflows. Sisense is still available for ad-hoc queries or troubleshooting.

Week 4: Hard cutover You disable Sisense access (or set it to read-only). All new dashboards, reports, and queries go through Superset. You keep Sisense running for 2–4 weeks as a fallback, then decommission it.

Week 5+: Optimisation You monitor Superset performance, tune slow queries, and optimise caching. You also decommission Sisense licenses and infrastructure.

The key is never rush the cutover. A 4-week soft cutover period is better than a 1-week cutover followed by 8 weeks of firefighting.

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Dashboard and Reporting Migration Strategy

Dashboard Assessment and Triage

Not all Sisense dashboards are worth migrating. Some are legacy, some are rarely used, and some are so complex that rebuilding them in Superset isn’t justified.

Triage dashboards into three buckets:

Tier 1: Migrate as-is High-value, frequently used dashboards that have clear business owners. These are your executive dashboards, operational reports, and core metrics. Typically 20–30% of your dashboard estate.

Tier 2: Simplify and rebuild Medium-value dashboards that are used but could be improved. Use the migration as an opportunity to simplify, add context, or consolidate related dashboards. Typically 40–50% of your dashboard estate.

Tier 3: Retire or archive Low-value, rarely-used, or redundant dashboards. Archive them (don’t delete—you might need them for historical reference) and retire the Sisense dashboards. Typically 20–30% of your dashboard estate.

This triage conversation should involve your business stakeholders (finance, operations, sales leadership). It’s not just a technical decision; it’s a chance to clean up your analytics portfolio.

Migration Patterns

Superset dashboards are built from datasets (logical tables) and visualisations (charts, tables, maps). The migration pattern depends on your Sisense architecture.

Pattern 1: Sisense Elasticube → Superset Dataset If your Sisense dashboards are built on Elasticubes (Sisense’s in-memory data model), you’ll need to replicate that logic in Superset. This typically means creating a Superset dataset that queries your data warehouse with the same filters, joins, and aggregations. This takes 1–2 weeks per complex Elasticube.

Pattern 2: Sisense SQL/Direct Query → Superset SQL Dataset If your Sisense dashboards query databases or data warehouses directly, you can often port the SQL directly to Superset. This is faster—typically 2–3 days per dashboard—but requires careful validation that the SQL behaves identically in Superset.

Pattern 3: Sisense Custom Code → Superset Python/SQL If your Sisense dashboards use custom JavaScript or plugins, you’ll need to rebuild that logic in Superset. Superset supports Python for custom metrics and SQL for complex calculations. This is the slowest pattern—typically 2–4 weeks per complex dashboard—but necessary for truly unique functionality.

For a typical mid-market portco migration, expect a mix of all three patterns. Budget 3–5 days per Tier 1 dashboard, 2–3 days per Tier 2 dashboard, and 0 days for Tier 3 (they’re retired).

Validation and Sign-Off

Before a dashboard goes live in Superset, it needs sign-off from the business owner. This means:

• Data validation: Does the Superset dashboard show the same numbers as the Sisense dashboard? If not, why? (Often you’ll find data quality issues in Sisense that Superset exposes.)
• Functionality validation: Do filters, drill-downs, and exports work as expected?
• Performance validation: Is the dashboard fast enough? Does it load in under 5 seconds?
• User acceptance testing (UAT): Does the business team approve the look, feel, and functionality?

Build a sign-off template and process. This prevents scope creep and ensures accountability. For each dashboard, document:

• Dashboard name and owner
• Data sources and refresh schedule
• Key metrics and filters
• Known limitations or differences from Sisense
• Sign-off date and owner

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Team Enablement and Change Management

Training Strategy

Your team needs to learn Superset. But they don’t all need to learn it the same way.

For business users (dashboard viewers): They need to know how to navigate dashboards, use filters, export data, and ask for new reports. This is a 2-hour training session, ideally hands-on with your actual dashboards. Budget 1 hour per department.

For analytics engineers and dashboard creators: They need to understand Superset’s data model (databases, datasets, charts), how to build dashboards, and how to write SQL queries. This is a 1–2 day workshop, ideally with a Superset expert leading it. Budget 3–5 days for your team to become proficient.

For data engineers: They need to understand how Superset connects to your data sources, how to optimise queries, and how to manage the Superset infrastructure. This is a 2–3 day deep dive, ideally with hands-on configuration and troubleshooting.

For all groups, use a mix of training methods:

• Live demos with your actual data and dashboards
• Hands-on labs where participants build dashboards or queries
• Documentation tailored to each role (how to use filters for business users, how to write datasets for analytics engineers)
• Office hours in the weeks after launch where teams can ask questions

Change Management and Communication

Migrating analytics platforms is a change event. People are creatures of habit. If they’ve been using Sisense for 3 years, they have muscle memory, mental models, and workflows built around it. Superset will feel different, and that friction is real.

Manage this with clear, consistent communication:

Pre-launch (4 weeks before)

• Announce the migration, why it’s happening, and what’s changing
• Share the timeline and what to expect
• Address concerns and questions

Launch week

• Celebrate the launch
• Remind teams of training resources and office hours
• Share quick-start guides for common tasks

Post-launch (weeks 2–4)

• Share success stories and wins
• Highlight new capabilities or improved dashboards
• Gather feedback and iterate

Post-launch (weeks 5–8)

• Share lessons learned
• Announce Sisense decommissioning
• Celebrate cost savings and team effort

Assign a “migration champion” in each department—someone who’s enthusiastic about Superset and can answer questions from their peers. This peer-to-peer learning is often more effective than top-down training.

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Post-Migration Optimisation and Scaling

Performance Tuning

After your dashboards are live in Superset, you’ll likely find some are slow. This is normal. The good news: Superset has many levers for performance optimisation.

Query optimisation

• Identify slow queries using Superset’s query logs
• Rewrite SQL to use indexes, avoid full table scans, or push aggregations to the database
• Consider materialised views or aggregation tables for frequently-run queries

Caching strategy

• Set appropriate cache TTLs for each dashboard. Executive dashboards can tolerate 1-hour stale data; operational dashboards might need 5-minute freshness.
• Use Superset’s cache invalidation to refresh specific dashboards on-demand

Database optimisation

• Ensure your data warehouse or database has appropriate indexes
• Consider query result caching at the database level (e.g., Snowflake result caching)
• Partition large tables to speed up queries

Superset infrastructure

• If self-hosted, ensure your Superset server has sufficient CPU and memory
• Use Redis for result caching and session management
• Consider using a read replica for your metadata database if you have high concurrency

For most portcos, performance tuning takes 4–8 weeks post-launch. Budget this time and don’t cut corners—a slow analytics platform gets abandoned quickly.

Scaling for Growth

One of Superset’s key advantages is that it scales to hundreds or thousands of users without licensing costs. But operational scaling requires planning.

User growth

• As you add users, ensure your authentication system (SSO) can handle the load
• Monitor Superset’s database (where user, dashboard, and query metadata is stored) for growth
• Plan for periodic backups and disaster recovery as your data grows

Dashboard growth

• As you build more dashboards, organise them into folders or collections
• Use tagging to make dashboards discoverable
• Consider a “dashboard governance” process where new dashboards are reviewed before going live

Data growth

• As your data warehouse grows, ensure your queries remain performant
• Consider implementing a data retention policy (e.g., keep detailed data for 2 years, then archive)
• Use Superset’s incremental refresh capabilities to avoid full table scans

Continuous Improvement

Post-migration, you should have a cadence for improving your analytics:

Monthly

• Review Superset usage metrics: which dashboards are used, which are dormant?
• Identify slow queries and optimise them
• Gather feedback from business teams and prioritise improvements

Quarterly

• Review and update your data governance policies
• Assess whether your data sources are still relevant
• Plan for new dashboards or features

Annually

• Review your Superset infrastructure and scaling needs
• Assess whether your current deployment model (self-hosted, managed, hybrid) is still optimal
• Plan for major upgrades or architectural changes

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Common Pitfalls and How to Avoid Them

Pitfall 1: Underestimating Scope

What happens: You estimate 100 dashboards to migrate, but during the audit, you find 200. Or you discover that 50 dashboards have complex custom code that can’t be ported directly.

How to avoid it:

• Conduct a thorough Sisense audit before planning timelines
• Build a 30% contingency into your timeline
• Triage dashboards early and retire low-value ones
• Be honest about which dashboards are truly “high-value” vs. “nice-to-have”

Pitfall 2: Running Sisense and Superset in Parallel Too Long

What happens: You launch Superset but keep Sisense running “just in case.” Six months later, you’re still paying for both, teams are confused about which system to use, and you haven’t realised the cost savings.

How to avoid it:

• Set a hard cutover date (e.g., 4 weeks after Superset launch)
• Communicate this date clearly to all stakeholders
• Plan for Sisense decommissioning as part of your project
• Track and celebrate the cost savings once Sisense is decommissioned

Pitfall 3: Ignoring Data Quality Issues

What happens: Your Sisense dashboards have been showing incorrect data for months (e.g., duplicate rows, missing joins), but no one noticed. Superset exposes this because the SQL is more transparent. Now you’re dealing with a data quality crisis during migration.

How to avoid it:

• Use the migration as an opportunity to validate data quality
• Reconcile Sisense dashboards against source data before migrating
• Fix data quality issues in your source systems, not in Superset
• Implement data validation tests (dbt tests, SQL checks) to prevent future issues

Pitfall 4: Not Planning for Superset Maintenance

What happens: Superset goes live, but no one is assigned to maintain it. Dashboards break, queries slow down, user issues pile up. Six months later, Superset has a reputation for being slow and unreliable.

How to avoid it:

• Assign clear ownership for Superset (either an internal engineer or a managed service provider)
• Budget for ongoing maintenance: upgrades, patches, performance tuning, user support
• Document your Superset architecture, configuration, and operational procedures
• Set up monitoring and alerting for Superset infrastructure and query performance

Pitfall 5: Skipping the Soft Cutover

What happens: You launch Superset and immediately shut down Sisense. Within a week, someone finds a dashboard that wasn’t migrated, or a query that behaves differently. Now you’re scrambling to restore Sisense or rebuild the missing dashboard under pressure.

How to avoid it:

• Always do a soft cutover: Superset is live, but Sisense remains available for 2–4 weeks
• Use this period to validate dashboards and catch missing functionality
• Communicate clearly that Sisense is temporary and will be decommissioned
• Be prepared to rebuild or adjust dashboards based on feedback during soft cutover

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Next Steps and Implementation Timeline

12-Week Implementation Roadmap

Weeks 1–2: Assessment and Planning

• Complete Sisense audit
• Triage dashboards into Tier 1, 2, 3
• Define data governance and permission model
• Select deployment option (self-hosted, managed, hybrid)
• Build detailed project plan and timeline

Weeks 3–4: Infrastructure and Setup

• Deploy Superset infrastructure
• Configure data source connections
• Test connectivity and query performance
• Set up SSO and authentication
• Implement monitoring and logging

Weeks 5–8: Pilot Migration

• Migrate Tier 1 dashboards (3–5 high-value dashboards)
• Conduct UAT with business owners
• Train analytics team on Superset
• Gather feedback and iterate

Weeks 9–11: Core Migration

• Migrate Tier 1 and Tier 2 dashboards
• Soft cutover: Superset live, Sisense remains available
• Train business users on Superset
• Monitor performance and fix issues

Week 12: Hard Cutover

• Disable Sisense access
• Decommission Sisense infrastructure
• Celebrate launch
• Begin optimisation phase

Resource Requirements

For a typical mid-market portco (100 analytical users, 100–200 dashboards):

• Project manager: 1 FTE for 12 weeks
• Analytics engineer: 1–2 FTE for 12 weeks (dashboard migration)
• Data engineer: 0.5 FTE for 8 weeks (data source configuration, query optimisation)
• Superset expert (external): 4–8 weeks of consulting (architecture, training, troubleshooting)
• Business stakeholders: 10–20% time for UAT, feedback, sign-off

Total cost: $200,000–$400,000 (internal + external consulting)

Compare this to your annual Sisense cost ($440,000–$830,000) and the payback is 3–9 months.

Building Your Migration Team

You don’t need to do this alone. If you’re a PE-backed portco without strong internal analytics engineering capacity, consider partnering with a platform engineering team that has Superset and migration experience. They can:

• Lead the technical architecture and deployment
• Migrate dashboards and datasets
• Train your internal team
• Provide post-launch support and optimisation

For portcos in Australia, Sydney-based platform teams often have deep experience with Superset, modern data warehouses, and PE-backed scaling. For US-based portcos, teams in New York, Los Angeles, and Seattle have strong track records.

Alternatively, if you’re looking for fractional technical leadership to oversee the migration and ensure it aligns with your broader tech strategy, a fractional CTO can provide governance, vendor selection, and risk management.

Key Success Metrics

Track these metrics to measure migration success:

Technical metrics

• % of Tier 1 dashboards migrated and live
• Average dashboard load time (target: <5 seconds)
• Query performance vs. Sisense baseline
• Uptime and availability (target: 99.5%+)

Business metrics

• Cost savings (Sisense licenses decommissioned)
• User adoption (% of users actively using Superset)
• Time to build new dashboards (should decrease)
• User satisfaction (NPS or survey score)

Operational metrics

• Time from dashboard request to live (cycle time)
• Number of support tickets and resolution time
• Data quality issues identified and fixed

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Conclusion

Migrating from Sisense to Superset is a material operational and financial undertaking for PE portcos. But it’s also a high-ROI project: you’ll cut costs by 30–50%, improve agility, and unlock your team’s ability to scale analytics without licensing constraints.

The key to success is treating it as a change programme, not just a technical project. You need clear scoping, a phased approach, strong governance, and intentional team enablement. You also need to be honest about what’s worth migrating and what should be retired.

If you’re a PE sponsor or portfolio company operator evaluating this migration, the financial case is clear. The operational case depends on your execution discipline. Use this playbook as your foundation, adapt it to your specific context, and don’t hesitate to bring in external expertise where you lack internal capacity.

For portcos in Australia or the US looking for hands-on support, consider working with a platform engineering partner that has shipped Superset migrations and can provide both technical execution and strategic guidance. The difference between a 16-week slog and a 12-week smooth migration often comes down to having experienced operators in the room.

Your analytics platform should enable your business, not constrain it. Superset gives you that freedom. The migration is the hard part, but it’s worth it.