Specialty Insurance MGAs: Why Most Pick D23.io Over Tableau in 2026
Discover why specialty insurance MGAs choose D23.io's managed Superset over Tableau for portfolio reporting. Compare cost, governance, and embedding advantages.
Table of Contents
- The Shift Away from Tableau in Specialty Insurance
- What D23.io Brings to the Table
- Cost Advantage: The Real Numbers
- Governance Without the Overhead
- Embedding Capabilities That Actually Work
- Portfolio Reporting for MGAs: A Different Beast
- Capacity Dashboards and Real-Time Visibility
- Migration from Tableau: What MGAs Need to Know
- Security, Compliance, and Audit Readiness
- The Verdict: Why D23.io Wins for Specialty Insurance
The Shift Away from Tableau in Specialty Insurance
Tableau has dominated enterprise data visualisation for over a decade. For most organisations, it remains the default choice when executives ask for a dashboard platform. But specialty insurance managing general agents (MGAs) are increasingly walking away from Tableau—not because it’s bad software, but because it’s solving the wrong problem at the wrong price point.
The shift is real. Across the MGA segment—particularly in excess and surplus lines, delegated authority, and specialty underwriting—teams are migrating to D23.io’s managed Superset as their primary analytics and reporting platform. The reasons are consistent: cost, governance flexibility, and embedded reporting capabilities that don’t require a separate licensing tier.
According to 2026 Best MGAs for P&C Insurance in the US rankings, the top non-exclusive and exclusive MGAs are now competing on operational efficiency and data-driven decision-making. Portfolio reporting and capacity dashboards have become table stakes. The question isn’t whether MGAs need real-time visibility into underwriting performance, premium volume, and claims patterns—it’s which platform delivers that visibility without bleeding the budget.
Tableau’s pricing model—per-seat licensing, additional costs for embedded analytics, and premium support—makes sense for large enterprises with dedicated BI teams. For MGAs, particularly those with 50–300 employees managing multiple underwriting teams and syndicate relationships, the math breaks down fast.
D23.io offers a fundamentally different model: managed Superset deployment with flexible user licensing, built-in embedding, and governance controls that don’t require hiring a separate data platform engineer. For specialty insurance, where underwriting cycles are fast, compliance requirements are strict, and budgets are lean, that difference is material.
What D23.io Brings to the Table
D23.io is a managed analytics platform built on Apache Superset, the open-source visualisation engine trusted by data teams across fintech, insurtech, and regulated industries. But D23.io isn’t just Superset in the cloud—it’s Superset with the operational overhead removed.
When an MGA chooses D23.io, they get:
Managed infrastructure and updates. No patching, no version management, no DevOps headcount required. D23.io handles upgrades, security patches, and infrastructure scaling. For an MGA with lean tech teams, this alone justifies the switch.
Flexible user licensing. Unlike Tableau, which charges per named user for most features, D23.io offers seat-based or consumption-based pricing. An MGA can onboard underwriters, claims managers, and brokers without triggering per-seat costs that scale linearly with headcount.
Native embedding. D23.io dashboards embed directly into underwriting platforms, broker portals, and internal applications without additional licensing. Tableau’s embedded analytics require a separate tier (Tableau Embedded) and carry their own cost structure. For MGAs that need to surface portfolio dashboards to brokers or embed capacity reports into underwriting systems, D23.io’s embedding is a game-changer.
Role-based access control (RBAC) and row-level security (RLS). MGAs manage complex permission hierarchies: underwriters see only their book, brokers see only their placements, management sees the full portfolio. D23.io’s governance model supports these requirements natively, without custom configuration or ongoing maintenance.
Integration with insurance data sources. D23.io connects seamlessly to the databases and data warehouses that MGAs already use: Snowflake, BigQuery, PostgreSQL, and cloud data platforms. The platform doesn’t force data migration or proprietary connectors.
Tableau can do most of these things. The difference is implementation burden and cost. D23.io is designed to make these capabilities the default, not the exception.
Cost Advantage: The Real Numbers
Let’s cut through the marketing and look at actual MGA scenarios.
Scenario 1: A mid-sized excess and surplus lines MGA with 100 employees.
With Tableau, the typical setup is:
- Tableau Server (on-premises or cloud): $70,000–$100,000 annually for a small-to-medium deployment
- Creator licenses (data analysts, underwriting managers): 5–8 seats × $2,160/year = $10,800–$17,280
- Viewer licenses (brokers, underwriters, management): 30–50 seats × $432/year = $12,960–$21,600
- Embedded analytics (broker portals, underwriting systems): Additional licensing tier, $15,000–$30,000+
- Support and maintenance: 15–20% of software costs annually
Total Year 1 cost: $110,000–$170,000+
With D23.io:
- Managed Superset platform: $20,000–$35,000 annually (all-inclusive: infrastructure, updates, support)
- User seats (no distinction between creators and viewers): 50–100 users × $200–$300/year = $10,000–$30,000
- Embedded dashboards: Included (no additional tier)
- Integrations and custom connectors: Minimal additional cost
Total Year 1 cost: $30,000–$65,000
The savings compound. By year three, an MGA has saved $150,000–$300,000 in licensing and infrastructure costs alone. Those dollars go back into underwriting operations, claims technology, or broker relationships.
According to Best Tableau Alternatives 2026 — Matched to Why You’re Leaving, cost is the primary driver of Tableau migration, particularly for mid-market organisations where per-seat licensing becomes untenable as user bases grow.
For MGAs, the math is even more compelling because embedding is non-negotiable. Brokers expect real-time visibility into placement status, capacity, and underwriting performance. With Tableau, that visibility comes with a separate bill. With D23.io, it’s baked in.
Governance Without the Overhead
Specialty insurance is regulated. MGAs operate under delegated authority from carriers, which means underwriting decisions, premium calculations, and claims handling must be auditable and compliant. Data governance isn’t optional—it’s mandatory.
Tableau’s governance model is powerful but requires ongoing administration. Creating and maintaining row-level security rules, managing user groups, and ensuring compliance with carrier requirements typically requires a dedicated Tableau administrator or consultant engagement. For small to mid-sized MGAs, that’s an overhead cost that doesn’t directly generate revenue.
D23.io’s governance approach is different. The platform includes:
Pre-built role templates. MGAs can define roles (Underwriter, Broker, Manager, Claims Handler) once and apply them across the platform. D23.io handles the underlying permission logic.
Audit logging and compliance reporting. Every dashboard view, filter change, and data export is logged. MGAs can generate compliance reports for carrier audits without custom SQL or external tools. This is critical for managing general agents that underwrite on behalf of multiple carriers with different compliance requirements.
Dynamic row-level security. D23.io automatically filters data based on user attributes (underwriter ID, broker code, book of business). An underwriter logs in and sees only their placements. A broker logs in and sees only their submissions. No manual configuration per user; no security rules that fall out of sync with the underwriting system.
Data lineage and metadata management. MGAs can document where data comes from, how it’s transformed, and who owns it. This is essential for audit readiness and for explaining to brokers and carriers how performance metrics are calculated.
Tableau offers these capabilities through Tableau Server and Tableau Cloud, but implementation requires expertise and ongoing maintenance. D23.io’s managed model means these governance features are maintained by the vendor, not the MGA’s IT team.
For MGAs pursuing SOC 2 compliance or working with carriers that require audit-ready data environments, D23.io’s built-in governance is a significant advantage. It reduces the security and compliance burden that Tableau pushes onto customers.
Embedding Capabilities That Actually Work
One of the most underrated reasons MGAs choose D23.io over Tableau is embedding—specifically, the ability to embed dashboards in broker portals and underwriting applications without friction or additional licensing costs.
Broker portals are core to MGA operations. Brokers need to see placement status, capacity availability, underwriting guidelines, and premium quotes in real time. If that visibility requires logging into a separate analytics platform, adoption drops. If it requires Tableau Embedded licensing, the cost-benefit analysis becomes negative.
D23.io dashboards embed natively. An MGA can:
- Build a dashboard in D23.io showing placement status, capacity by line, and underwriting performance.
- Generate an embed token with user-specific filters (broker sees only their placements).
- Embed the dashboard directly in the broker portal using an iframe or API call.
- No additional licensing. No Tableau Embedded tier. No per-embed costs.
The underwriter portal works the same way. Underwriters can see their book of business, performance metrics, and claims trends without leaving their primary underwriting application.
Tableau’s embedded analytics capability (Tableau Embedded) is mature and reliable, but it carries a separate licensing model. For an MGA with 50 brokers and 10 underwriters, Tableau Embedded licensing can cost $20,000–$40,000 annually on top of the base Tableau Server costs.
With D23.io, embedding is part of the platform. The cost is the same whether dashboards are accessed directly or embedded in applications.
This matters for user adoption. When brokers and underwriters can access insights within their workflow—without extra authentication or separate tools—they use the data more. Better data usage drives better underwriting decisions, which drives better loss ratios, which drives profitability.
Portfolio Reporting for MGAs: A Different Beast
Portfolio reporting in specialty insurance is different from typical enterprise analytics. MGAs don’t report on a single book of business—they manage multiple underwriting teams, each with different carriers, lines, and performance metrics.
A typical specialty insurance MGA might manage:
- Excess and surplus lines (E&S) underwriting across 3–5 syndicates
- Delegated authority programs with 2–3 carriers
- Specialty lines (cyber, professional liability, management liability) with different underwriting criteria
- Broker relationships with varying commission structures and volume commitments
- Carrier relationships with different reporting requirements and compliance mandates
Portfolio reporting needs to surface:
Premium volume and growth. Written premium, earned premium, and growth trends by line, by carrier, by broker, and by underwriter. This drives compensation, carrier relationships, and business development priorities.
Loss ratios and profitability. Incurred losses, loss ratios, and combined ratios by underwriting team and by carrier. This is the core metric for underwriting performance and for managing general agent profitability.
Capacity and utilisation. Available capacity by line and by carrier, utilisation rates, and capacity forecasts. This drives underwriting strategy and broker communication.
Compliance and regulatory reporting. Premium and loss data by state, by line, and by carrier, formatted for regulatory filings and carrier reporting requirements.
Broker and underwriter performance. Metrics by broker (volume, loss ratio, profitability) and by underwriter (production, loss ratio, turnaround time). This drives compensation, hiring, and resource allocation.
Tableau can build all of these reports. But the typical Tableau implementation for an MGA involves:
- A data warehouse or data mart (often built on Snowflake or BigQuery)
- A Tableau Server deployment with 5–10 Creator licenses
- Custom dashboards and workbooks built by analysts
- Ongoing maintenance as business requirements change
- A Tableau administrator to manage users, permissions, and performance
D23.io approaches portfolio reporting differently. The platform is designed for self-service analytics, which means underwriters and managers can build and modify dashboards without analyst involvement. This is critical for MGAs because:
Underwriting teams move fast. Carriers change underwriting guidelines, brokers request new metrics, and market conditions shift. If every dashboard change requires an analyst to modify Tableau workbooks, the MGA falls behind.
MGAs have lean analyst teams. Unlike large insurers, most MGAs don’t have dedicated BI or analytics teams. Analysts are embedded in underwriting or operations, and they don’t have time to maintain Tableau.
Self-service reduces bottlenecks. With D23.io, underwriters can explore data, build ad hoc reports, and answer their own questions. This reduces the demand on analysts and accelerates decision-making.
D23.io’s interface is designed for non-technical users. Underwriters can drag and drop metrics, filter by carrier or broker, and export results without SQL knowledge. This democratises data access in a way that Tableau, despite its ease-of-use improvements, hasn’t fully achieved for non-technical users.
For portfolio reporting, that difference is material. MGAs that choose D23.io report faster time to insight, higher user adoption, and lower analyst overhead.
Capacity Dashboards and Real-Time Visibility
Capacity management is a live operational function in specialty insurance. Underwriters need to know, at any moment, how much capacity they have available by line, by carrier, and by risk type. Brokers need to know if an opportunity can be placed before they pitch it to the client.
Capacity dashboards aren’t static reports—they’re operational tools that update in real time as placements are written and claims are reported.
Tableau can deliver real-time dashboards, but the infrastructure required is more complex. Real-time data refresh typically requires:
- Direct database connections (not extracts)
- High-performance database tuning
- Careful dashboard design to avoid expensive queries
- Monitoring and alerting if queries slow down
For an MGA with limited IT resources, this complexity is a burden.
D23.io’s architecture is built for real-time dashboards. The platform:
Connects directly to databases without requiring data warehouse transformation. An underwriting system’s capacity table can be queried directly by D23.io.
Caches intelligently. D23.io caches query results and refreshes them on a schedule (every 5 minutes, every hour, etc.). This balances freshness with database load.
Scales horizontally. As more underwriters and brokers access capacity dashboards, D23.io’s infrastructure scales automatically. There’s no performance degradation as user load increases.
Provides drill-down and filtering. Underwriters can click on a capacity number to see the underlying placements, then drill down to individual risk details. This is essential for capacity management because underwriters need to understand not just how much capacity is available, but what types of risks are consuming it.
In practice, this means an MGA can deploy a capacity dashboard in D23.io and have underwriters and brokers accessing real-time data within days, not months. The dashboard updates every 5 minutes, and the underlying database isn’t strained because D23.io’s query engine is optimised for analytics workloads.
Tableau can achieve this, but it requires database expertise and ongoing tuning. D23.io makes it the default behaviour.
Migration from Tableau: What MGAs Need to Know
If an MGA is currently using Tableau and considering D23.io, the migration is straightforward but requires planning.
Step 1: Audit existing dashboards. Document which Tableau workbooks are in active use, who uses them, and what business questions they answer. Most MGAs find that 30–40% of their Tableau dashboards are unused or outdated. Don’t migrate those—retire them.
Step 2: Identify data sources. D23.io connects to the same databases and data warehouses that Tableau uses. Identify the source systems (underwriting platform, claims system, accounting system, data warehouse) and verify that D23.io can connect to them.
Step 3: Rebuild dashboards in D23.io. This isn’t a technical migration—D23.io doesn’t import Tableau workbooks. Instead, you rebuild dashboards using D23.io’s interface. This is actually an advantage because it forces you to rethink dashboard design. Many MGAs find that D23.io dashboards are simpler and more focused than their Tableau equivalents.
Step 4: Test with a pilot group. Start with one underwriting team or one broker group. Get feedback, refine dashboards, and build confidence before full rollout.
Step 5: Plan the cutover. Once D23.io dashboards are ready, communicate the change to users, provide training, and set a date to decommission Tableau. Most MGAs find that users adopt D23.io quickly because the interface is intuitive and the dashboards are faster.
The migration typically takes 4–8 weeks for a mid-sized MGA, depending on the number of dashboards and data sources. Importantly, the migration doesn’t require new data infrastructure. D23.io works with the data warehouse or databases the MGA already has.
According to Best Tableau Alternatives in 2026: A Strategic Guide for Analytics and Reporting Teams, migration from Tableau to alternatives like Superset is increasingly common for organisations prioritising cost reduction and operational simplicity. MGAs are part of this trend.
Security, Compliance, and Audit Readiness
Specialty insurance is regulated. MGAs must comply with state insurance regulations, carrier requirements, and increasingly, cybersecurity standards. Data security and audit readiness aren’t nice-to-have—they’re mandatory.
When evaluating D23.io against Tableau, security and compliance are critical factors.
Encryption in transit and at rest. Both platforms support encryption. D23.io offers encryption for data in transit (TLS) and at rest (AES-256). Tableau offers the same.
Role-based access control. D23.io’s RBAC is granular and flexible. Tableau’s RBAC is equally powerful but requires more configuration.
Audit logging. D23.io logs all user actions: dashboard views, data exports, filter changes, and user access. Audit logs are exportable and searchable. This is essential for compliance investigations and for demonstrating due diligence to carriers and regulators.
Data masking and anonymisation. D23.io supports column-level security and data masking, so sensitive fields (social security numbers, policy details) can be hidden from certain users. This is important for MGAs that need to limit broker access to underwriting criteria or loss history.
Single sign-on (SSO). D23.io supports SAML and OAuth, so users can authenticate using their corporate identity provider (Okta, Azure AD, etc.). This simplifies user management and improves security.
Tableau offers all of these capabilities as well. The difference is that D23.io’s managed model means the vendor is responsible for security updates and compliance maintenance. Tableau customers are responsible for patching and maintaining their own Tableau Server instances (unless they use Tableau Cloud, which is Tableau’s managed offering).
For MGAs with lean IT teams, D23.io’s managed security model is a significant advantage. Security patches are applied automatically. Compliance requirements are met by the platform, not by the MGA’s IT team.
If an MGA is pursuing SOC 2 compliance or ISO 27001 certification, PADISO’s security audit and compliance services can help assess the MGA’s data analytics platform and ensure it meets audit requirements. D23.io’s managed model and built-in audit logging typically align well with compliance frameworks.
Why MGAs Are Choosing D23.io: The Strategic Inflection Point
According to Why MGAs are poised to enter a ‘strategic inflection point’ in 2026, MGAs are at a critical juncture. Carriers are consolidating, competition is intensifying, and technology is becoming a competitive advantage. MGAs that invest in the right technology—particularly data and analytics—are winning.
D23.io fits this moment. It’s a platform built for MGAs that need:
- Cost efficiency. Lower licensing costs and lower operational overhead mean more budget for underwriting and broker relationships.
- Speed. Faster dashboard deployment and faster time to insight drive better underwriting decisions.
- Flexibility. Self-service analytics and easy embedding mean the MGA can adapt to changing business requirements without waiting for IT.
- Compliance. Built-in governance, audit logging, and security features reduce the compliance burden.
Tableau is a mature, powerful platform. But it’s built for large enterprises with dedicated BI teams and large analytics budgets. For specialty insurance MGAs, D23.io is a better fit.
The shift is accelerating. MGAs that made the move in 2024 and 2025 are now recommending D23.io to peers. The word-of-mouth effect is driving adoption across the MGA segment.
The Verdict: Why D23.io Wins for Specialty Insurance
Specialty insurance MGAs have specific needs that differ from typical enterprise analytics use cases. They need portfolio reporting, capacity dashboards, and broker portals. They operate with lean teams and tight budgets. They must comply with carrier requirements and regulatory mandates. They move fast and adapt constantly.
D23.io is built for this context. Tableau is built for large enterprises.
The comparison isn’t about which platform is technically superior—both are excellent. It’s about which platform is right for the MGA’s operational model, budget, and strategic priorities.
Cost. D23.io costs 40–60% less than Tableau for a typical MGA, with no per-seat licensing and no separate embedding tier. Over three years, that’s $150,000–$300,000 in savings that can be reinvested in underwriting operations.
Governance. D23.io’s role-based access control, audit logging, and data security are built in and maintained by the vendor. MGAs don’t need to hire a Tableau administrator or engage consultants to manage compliance.
Embedding. D23.io dashboards embed natively in broker portals and underwriting applications without additional licensing. This drives user adoption and enables real-time decision-making.
Portfolio reporting. D23.io’s self-service interface empowers underwriters and managers to build and modify dashboards without analyst involvement. This reduces bottlenecks and accelerates insights.
Capacity dashboards. D23.io’s real-time architecture and intelligent caching make capacity dashboards fast and responsive, even with multiple concurrent users.
Compliance. D23.io’s managed model and built-in audit logging reduce the burden of compliance and security maintenance.
For specialty insurance MGAs in 2026, the choice is increasingly clear. D23.io delivers the analytics capability that MGAs need, at a price and operational complexity that makes sense for their business.
Tableau remains a strong choice for large enterprises. But for MGAs, D23.io is the platform that aligns with how they operate and what they can afford to invest.
Next Steps: Making the Move
If you’re an MGA currently using Tableau and considering D23.io, here’s how to start:
1. Audit your current Tableau usage. Document active dashboards, user counts, and annual licensing costs. This gives you a baseline for comparison.
2. Schedule a demo. D23.io offers free trials and demos tailored to insurance use cases. See how your data sources connect and how quickly you can build portfolio dashboards.
3. Run a pilot. Start with one underwriting team or one broker group. Migrate 2–3 key dashboards to D23.io and gather feedback.
4. Calculate ROI. Compare D23.io licensing costs against your current Tableau spend. Factor in the cost of analyst time saved through self-service analytics and the value of faster insights.
5. Plan the migration. Most MGAs complete a full migration in 4–8 weeks. Plan for user training and communicate the change to stakeholders.
For MGAs that are also modernising their underwriting operations or automating claims processing, PADISO’s AI automation services for insurance can complement your analytics platform. Better data visibility (via D23.io) pairs well with intelligent automation (via AI agents), creating a comprehensive operational transformation.
The specialty insurance MGA segment is entering a strategic inflection point. Technology is becoming a competitive advantage. The MGAs that invest in the right platform—one that balances cost, capability, and operational fit—will outperform peers and win with carriers and brokers.
D23.io is the platform that makes that win possible.