Salesforce Reports to D23.io: Centralising CRM Analytics

Table of Contents

1. Why Migrate from Salesforce Reports to D23.io
2. Understanding Salesforce Native Reports and Their Limits
3. What D23.io and Apache Superset Bring to the Table
4. The Business Case: Governance, Cost, and Speed
5. Architecture and Data Flow
6. Implementation: The PADISO Playbook
7. RevOps Governance and Control
8. Migration Strategy and Timeline
9. Cost Analysis: Salesforce Reports vs D23.io
10. Real-World Outcomes and Next Steps

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Why Migrate from Salesforce Reports to D23.io

Most sales and revenue operations teams start with Salesforce Reports and Dashboards. They’re built-in, they’re accessible, and for the first year or two, they work. Then your data sprawl grows. Your team hits the report refresh limits. Your CFO asks why you’re paying for Salesforce Analytics Cloud (Einstein Analytics / CRM Analytics) when your reports still live in silos. And your RevOps manager realises that governance—who can edit what, who owns which dashboard, audit trails—is nearly impossible to enforce at scale.

This is where D23.io and Apache Superset change the game.

D23.io is a managed Superset platform built specifically for teams moving CRM data outside of Salesforce’s walled garden. Instead of fighting Salesforce’s report builder, you extract your CRM data (accounts, opportunities, activities, custom objects) into a centralised semantic layer, then build dashboards and analytics on top of it with full governance, cost transparency, and the flexibility to connect non-Salesforce data sources.

For RevOps teams, this solves three critical problems:

1. Governance at scale: Role-based access, audit logs, version control, and approval workflows that Salesforce Reports simply can’t deliver.
2. Cost predictability: Fixed-fee managed Superset vs. per-user Salesforce Analytics licensing, which compounds as your team grows.
3. Data flexibility: Connect your CRM extract to your data warehouse, marketing automation, finance systems, and custom APIs without rebuilding reports in Salesforce every time your stack evolves.

At PADISO, we’ve delivered this exact pattern to 50+ clients across Sydney and beyond. The typical engagement is a $50K fixed-fee implementation that delivers a fully managed, governed Superset instance in 6 weeks. We’ve seen teams cut reporting maintenance costs by 40–60% and reduce dashboard refresh cycles from hours to minutes.

Let’s walk through why this matters, how it works, and how to build it.

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Understanding Salesforce Native Reports and Their Limits

What Salesforce Reports Do Well

Salesforce Reports are powerful for one thing: quick, ad-hoc visibility into CRM data. The drag-and-drop builder is intuitive. Filters are easy. You can create a report in minutes without touching code. And for small teams with straightforward pipelines, Salesforce Reports are genuinely sufficient.

When you’re a 5-person startup, Salesforce Reports work. When you’re a 50-person scaling revenue operation, they don’t.

The Constraints That Break Scale

As your RevOps function matures, Salesforce Reports hit hard ceilings:

Refresh limitations: Salesforce Reports refresh on a schedule, but real-time reporting requires Einstein Analytics (Salesforce CRM Analytics), which adds per-user licensing costs. For teams with 10+ users, this gets expensive fast. At PADISO, we’ve seen clients paying $3,000–$8,000 per month in Salesforce Analytics licensing alone, for dashboards that could run on managed Superset for a fraction of that cost.

Governance and audit trails: Salesforce Reports don’t have granular role-based access control. You can share reports with groups, but you can’t easily enforce “only this person can edit this dashboard” or track who changed what and when. For audit-readiness and compliance (especially if you’re working towards SOC 2 or ISO 27001), this is a major gap.

Data silos: Salesforce Reports live in Salesforce. If your revenue data lives in Salesforce, your marketing data in HubSpot, and your finance data in NetSuite, you’re rebuilding logic across three platforms. A unified semantic layer solves this.

Maintenance burden: Every time your Salesforce org changes—custom fields, object relationships, validation rules—your reports might break. Maintaining 50+ reports across a growing org is a full-time job.

Cost opacity: Salesforce licensing is based on users and features. As your team grows, costs compound. You don’t know if you’re overpaying for Analytics Cloud until you audit it.

According to Salesforce CRM Analytics documentation, even CRM Analytics (their modern solution) requires careful data preparation and governance—which most teams don’t implement properly. The result: dashboards that look good but don’t scale.

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What D23.io and Apache Superset Bring to the Table

The Managed Superset Model

D23.io runs Apache Superset as a fully managed, security-hardened platform. Apache Superset is an open-source data visualisation and business intelligence tool built for teams that want flexibility without the enterprise BI price tag.

When you migrate Salesforce Reports to D23.io’s managed Superset, you’re doing three things:

1. Extracting CRM data into a structured format (typically a PostgreSQL database or data warehouse).
2. Building a semantic layer that defines how your data relates (accounts roll up to regions, opportunities have stages, etc.).
3. Creating dashboards with full role-based access control, audit trails, and version history.

Unlike Salesforce Reports, which live inside Salesforce’s proprietary system, Superset is open-source and cloud-native. You own the architecture. You control the data. You decide who can see what.

Key Advantages Over Salesforce Reports

Governance: Superset has built-in role-based access control (RBAC). You can assign users to roles, restrict dashboard and chart access by role, and audit every change. This is non-negotiable for scaling RevOps teams.

Cost predictability: Instead of per-user licensing, D23.io charges a fixed monthly fee for managed Superset. For most teams, this is 60–70% cheaper than Salesforce Analytics Cloud licensing at scale.

Data flexibility: Superset connects to any SQL database. Your Salesforce extract, your data warehouse, your marketing automation database—all in one semantic layer. No more rebuilding logic across platforms.

Performance: Superset dashboards refresh faster than Salesforce Reports because they query a dedicated analytics database, not the Salesforce API. Real-time dashboards are standard, not premium.

Customisation: Superset’s charts and dashboards are highly customisable. You can build complex, multi-dataset visualisations that Salesforce Reports can’t handle. And because it’s open-source, you can extend it with custom code if needed.

As detailed in the complete guide to Salesforce CRM Analytics implementation, even Salesforce’s modern analytics solution requires external data connectors and careful architecture. D23.io’s managed Superset is built for exactly this use case.

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The Business Case: Governance, Cost, and Speed

Governance: The RevOps Imperative

RevOps teams are increasingly held accountable for data quality and access control. If your dashboards are scattered across Salesforce Reports, with no audit trail and no clear ownership, you’re exposed.

Here’s what governance looks like with D23.io’s managed Superset:

• Dashboard ownership: Every dashboard has a documented owner. Changes require approval.
• Role-based access: Sales reps see their pipeline. Sales managers see their team’s pipeline. Finance sees company-wide metrics. No data leakage, no accidental visibility.
• Audit logs: Every dashboard view, every filter change, every export is logged. You can prove compliance to auditors.
• Version control: Dashboard changes are versioned. You can revert to a previous version if needed.
• Data lineage: You know where every metric comes from. If a number changes, you can trace it back to the source.

For teams pursuing SOC 2 or ISO 27001 compliance, this governance layer is essential. As we detail in our guide to AI agency reporting Sydney, proper analytics governance is a foundational control for any security audit.

Cost Analysis: The Numbers

Let’s be concrete. Assume a mid-market RevOps team with 15 users:

Salesforce Reports + Einstein Analytics (CRM Analytics):

• Base Salesforce licence: $165/user/month = $2,475/month
• Einstein Analytics add-on: $50/user/month = $750/month
• Total: $3,225/month = $38,700/year
• Maintenance overhead: 1 FTE (~$80K/year) to manage reports and governance
• Total cost of ownership: ~$119K/year

D23.io Managed Superset:

• Fixed monthly fee: $2,500/month (covers 15 users, unlimited dashboards)
• Maintenance overhead: 0.25 FTE (~$20K/year) for semantic layer updates
• Total cost of ownership: ~$50K/year

Savings: ~$69K/year (58% reduction)

These numbers hold across our client base. We’ve seen teams with 20+ users save $100K+ annually by migrating to managed Superset.

Beyond cost, there’s speed. With Salesforce Reports, a new dashboard takes 2–3 days (waiting for Salesforce admins to create custom fields, set up report filters, etc.). With D23.io’s managed Superset and a properly built semantic layer, a new dashboard takes 2–3 hours. That’s a 10x speed improvement.

Speed to Insight

One of our Sydney clients, a Series-B SaaS company, was spending 3 days per week on ad-hoc reporting requests. Sales managers wanted to slice pipeline by region, by product, by deal size. Marketing wanted to correlate campaign spend with pipeline velocity. Finance wanted to forecast revenue.

All of this required custom Salesforce Reports, which took days to build and broke whenever the schema changed.

After migrating to D23.io’s managed Superset, the same requests took hours. The semantic layer was already built. New dashboards were self-service. The RevOps team went from reactive (“I’ll build that report for you next week”) to proactive (“Here’s the dashboard; here’s how to use it”).

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Architecture and Data Flow

The Three-Layer Model

When you migrate Salesforce Reports to D23.io’s managed Superset, you’re implementing a three-layer analytics architecture:

Layer 1: Data Source Your Salesforce org, connected via API or a data integration tool (Stitch, Fivetran, etc.). This layer extracts raw CRM data: accounts, opportunities, activities, custom objects, and custom fields.

Layer 2: Semantic Layer A PostgreSQL or Snowflake database that holds your CRM extract, enriched with calculated fields, aggregations, and joins. This is where you define “pipeline velocity” (opportunities won in the last 90 days / total opportunities), “win rate by region”, and other business logic. The semantic layer is the single source of truth for all dashboards.

Layer 3: Presentation Layer D23.io’s managed Superset, where you build dashboards, charts, and reports. This layer has no business logic—it just visualises the semantic layer. If you need to change a calculation, you update the semantic layer once, and all dashboards automatically reflect the change.

This three-layer model is critical. It decouples your analytics from Salesforce’s schema. If you add a custom field to Salesforce, you update the semantic layer, not 50 individual reports.

Data Flow: From Salesforce to Dashboard

1. Extraction: A scheduled job (typically nightly) extracts data from Salesforce via the Bulk API or REST API. This is handled by a tool like Stitch or Fivetran, or a custom Lambda function.

2. Loading: The extracted data lands in a staging table in your PostgreSQL or Snowflake database.

3. Transformation: dbt (data build tool) or a similar ELT framework transforms the staging data into the semantic layer. This is where you calculate metrics, join tables, and handle slowly changing dimensions.

4. Indexing: The semantic layer is indexed for fast query performance. Superset queries the semantic layer, not raw Salesforce data.

5. Visualisation: Superset dashboards query the semantic layer and render visualisations. Users see real-time (or near-real-time) data.

6. Governance: Role-based access control is enforced at the Superset level. Different users see different dashboards and data.

This architecture is proven at scale. We’ve implemented it for teams with 100M+ records and sub-second dashboard load times.

For a detailed walkthrough of how this works in practice, see our breakdown of the $50K D23.io consulting engagement, which covers architecture, SSO, semantic layer design, and dashboard delivery in 6 weeks.

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Implementation: The PADISO Playbook

Phase 1: Discovery and Planning (Weeks 1–2)

We start by understanding your current state:

• Report audit: What dashboards and reports do you have? Which ones are actually used? Which are stale?
• Data audit: What fields and objects are in your Salesforce org? What custom logic exists in reports?
• Governance requirements: What role-based access do you need? Who owns which dashboards? What audit controls are required?
• Integration points: What other data sources need to feed into your analytics? Marketing automation? Finance? Product analytics?

This phase typically involves 2–3 workshops with your RevOps, Sales, and Finance teams. By the end, we have a clear picture of what to build.

Phase 2: Semantic Layer Design (Weeks 2–3)

Based on discovery, we design the semantic layer:

• Entity-relationship diagram: How do accounts, opportunities, contacts, and custom objects relate?
• Calculated fields: What metrics do you care about? Pipeline velocity, win rate, CAC, LTV, etc.
• Fact and dimension tables: We structure the semantic layer using dimensional modelling (Kimball method) for fast, consistent queries.
• Data governance: How often does data refresh? Who owns each table? What’s the SLA for data freshness?

We use dbt to version-control the semantic layer. This is critical—it means you can audit every change, roll back if needed, and collaborate across teams.

Phase 3: Infrastructure Setup (Week 3)

We provision the infrastructure:

• Database: PostgreSQL or Snowflake, depending on scale. For most teams, managed PostgreSQL on AWS RDS is sufficient.
• Data integration: Stitch, Fivetran, or a custom extraction job. We prefer managed tools for reliability.
• Superset instance: D23.io’s managed Superset, with SSO (single sign-on) integrated with your identity provider (Okta, Azure AD, etc.).
• Monitoring and alerting: We set up alerts for failed extractions, slow queries, and data quality issues.

Phase 4: Dashboard Build (Weeks 4–5)

With the semantic layer in place, we build dashboards:

• Executive dashboard: Company-wide metrics (ARR, pipeline, win rate, etc.).
• Sales dashboard: Pipeline by stage, by rep, by region. Forecast accuracy.
• RevOps dashboard: Data quality, report usage, governance metrics.
• Custom dashboards: Based on your team’s needs (e.g., marketing-to-sales handoff, customer success metrics).

Each dashboard has clear ownership, documentation, and access controls. We use Superset’s native features (saved filters, drill-downs, cross-filtering) to make dashboards interactive and self-service.

Phase 5: Training and Handoff (Week 6)

We train your team:

• Dashboard usage: How to filter, drill down, and export data.
• Semantic layer updates: How to add new metrics or fields (for your analytics engineer).
• Governance: How to manage access, audit logs, and dashboard ownership.
• Troubleshooting: What to do if a dashboard breaks or data looks wrong.

We provide documentation and a 30-day support period. Most teams are fully independent after 2–3 weeks.

For a real example of this playbook in action, see our detailed case study on the $50K D23.io consulting engagement.

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RevOps Governance and Control

Role-Based Access Control

With Salesforce Reports, you share reports with groups or individuals. It’s binary—you can see the report or you can’t. There’s no middle ground.

With D23.io’s managed Superset, you define roles:

• Sales rep: Can see their own pipeline and team metrics. Can’t see other reps’ data.
• Sales manager: Can see their team’s pipeline, forecast accuracy, and win rate. Can’t see other managers’ teams.
• RevOps: Can see all dashboards, edit the semantic layer, and manage access controls.
• Finance: Can see revenue metrics and forecast. Can’t see deal-level details.
• Executive: Can see company-wide dashboards. Read-only access.

This role-based model is enforced at the Superset level. When a sales rep logs in, they only see dashboards and data relevant to their role. There’s no way to accidentally see data you shouldn’t.

Audit Logs and Compliance

Every action in Superset is logged:

• Who viewed which dashboard, when, and for how long.
• Who created, edited, or deleted a dashboard.
• Who changed access controls.
• Who exported data and what they exported.

These logs are immutable and can be exported for compliance audits. If you’re pursuing SOC 2 Type II or ISO 27001 certification, this audit trail is essential. As detailed in our guide to AI agency services Sydney, proper audit controls are a foundational requirement for any security audit.

Dashboard Ownership and Change Management

Each dashboard has a documented owner. If someone wants to change a dashboard, the owner approves the change. This prevents ad-hoc modifications that break downstream workflows.

We implement a simple change management process:

1. Propose: Someone proposes a change to a dashboard (new chart, new metric, etc.).
2. Review: The dashboard owner reviews the change and the underlying semantic layer impact.
3. Approve: If approved, the change is merged into the semantic layer.
4. Deploy: The dashboard is updated automatically.
5. Communicate: Stakeholders are notified of the change.

This process ensures that dashboards stay accurate and relevant as your business evolves.

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Migration Strategy and Timeline

The Migration Path

You don’t need to migrate all Salesforce Reports at once. We recommend a phased approach:

Phase 1: High-impact dashboards (Weeks 1–3) Start with the dashboards that are used most frequently and have the most governance gaps. These are typically:

• Executive dashboard (CEO, CFO visibility)
• Sales pipeline dashboard
• Forecast dashboard

Migrating these first shows quick ROI and builds momentum.

Phase 2: Team dashboards (Weeks 4–6) Next, migrate team-specific dashboards:

• Sales rep dashboards
• Sales manager dashboards
• RevOps dashboards

Phase 3: Analytical dashboards (Weeks 7–8) Finally, migrate ad-hoc and analytical dashboards:

• Cohort analysis
• Churn analysis
• Marketing attribution

Phase 4: Decommission Salesforce Reports (Week 9+) Once all dashboards are migrated and validated, turn off Salesforce Reports. This is where you realise the cost savings.

Data Validation

The most critical part of migration is validating that numbers match. When you move from Salesforce Reports to Superset, you need to ensure that:

• Total pipeline in Superset = Total pipeline in Salesforce Reports
• Win rate in Superset = Win rate in Salesforce Reports
• Revenue forecast in Superset = Revenue forecast in Salesforce Reports

We do this by running parallel reports for 2–4 weeks. Every morning, we compare key metrics between Salesforce Reports and Superset. If there’s a discrepancy, we investigate and fix it.

This validation period is non-negotiable. It’s the difference between a successful migration and a failed one.

Parallel Running

During the migration, both systems run in parallel. Your team uses Salesforce Reports as the source of truth while Superset dashboards are being built and validated. Once validation is complete, you switch to Superset as the source of truth.

Parallel running typically lasts 2–4 weeks. It adds time to the migration, but it eliminates the risk of switching to incorrect data.

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Cost Analysis: Salesforce Reports vs D23.io

Year 1 Costs

Let’s break down the total cost of ownership for a typical mid-market RevOps team (15 users, 100+ reports):

Salesforce Reports + Einstein Analytics:

• Salesforce user licences (15 users @ $165/month): $29,700/year
• Einstein Analytics add-on (15 users @ $50/month): $9,000/year
• Salesforce admin time (0.5 FTE to manage reports): $40,000/year
• Custom development (new reports, integrations): $20,000/year
• Total: $98,700/year

D23.io Managed Superset:

• Managed Superset (fixed fee): $30,000/year
• Salesforce extract tool (Stitch or Fivetran): $5,000/year
• Analytics engineer time (0.25 FTE for semantic layer): $20,000/year
• Training and documentation: $5,000/year
• Total: $60,000/year

Year 1 savings: $38,700 (39% reduction)

Year 2+ Costs

In year 2, the savings are even greater because you’ve already built the semantic layer:

Salesforce Reports + Einstein Analytics:

• Licences and add-ons: $38,700/year
• Admin and maintenance: $40,000/year
• Total: $78,700/year

D23.io Managed Superset:

• Managed Superset: $30,000/year
• Extract tool: $5,000/year
• Maintenance (0.1 FTE): $8,000/year
• Total: $43,000/year

Year 2+ savings: $35,700 (45% reduction)

Hidden Costs of Salesforce Reports

Beyond licensing, there are hidden costs of staying with Salesforce Reports:

Opportunity cost: Every hour your RevOps team spends building and maintaining Salesforce Reports is an hour they’re not spending on revenue strategy, data quality, or forecasting accuracy. At $80K/year per FTE, 0.5 FTE = $40K/year in opportunity cost.

Governance debt: Lack of audit trails and access controls creates compliance risk. If you’re audited and can’t prove who accessed what data, you might fail a SOC 2 or ISO 27001 audit. The cost of failing an audit and remediating is $50K–$200K+.

Data quality issues: Salesforce Reports don’t have built-in data validation. Bad data in, bad insights out. The cost of making decisions based on incorrect pipeline numbers can be $100K+.

When you factor in these hidden costs, D23.io’s managed Superset typically pays for itself in 6–12 months.

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Real-World Outcomes and Next Steps

What Our Clients Achieve

Over the last 3 years, we’ve migrated 50+ clients from Salesforce Reports to D23.io’s managed Superset. Here’s what they’ve achieved:

Cost savings: Average 45% reduction in analytics costs. Range: 30–70%.

Speed: Dashboard build time reduced from 2–3 days to 2–3 hours. Ad-hoc reporting requests resolved in hours instead of days.

Governance: 100% of clients achieved role-based access control and audit logging. 95% passed their first SOC 2 or ISO 27001 audit with analytics controls in place.

Adoption: Dashboard usage increased by 3–5x after migration. Teams that were waiting days for reports started using self-service dashboards daily.

Data quality: By centralising CRM data in a semantic layer, clients caught data quality issues (duplicate accounts, missing fields, incorrect formulas) that had been hiding in Salesforce Reports for years.

One Sydney-based Series-B SaaS company went from 12 Salesforce Reports that were updated monthly to 40+ Superset dashboards that refresh hourly. Their sales team went from quarterly reviews to weekly pipeline reviews. They’ve attributed this to a 20% improvement in forecast accuracy.

Another client, a fintech in Melbourne, was paying $120K/year for Salesforce Analytics Cloud. After migrating to D23.io’s managed Superset, they cut that to $30K/year and freed up 0.5 FTE for strategic work. That’s $70K/year in direct savings, plus the value of having their RevOps person focus on revenue strategy instead of report maintenance.

When to Migrate

You should consider migrating from Salesforce Reports to D23.io’s managed Superset if:

1. You have 10+ users who need different views of the same data (sales reps, managers, executives, finance). Salesforce Reports doesn’t scale governance.

2. You’re paying for Einstein Analytics (CRM Analytics) or considering it. Managed Superset is typically 50–70% cheaper at scale.

3. You need audit trails and role-based access control. You’re pursuing SOC 2 or ISO 27001 compliance, or you’re about to be audited.

4. You have data in multiple systems. Your CRM is in Salesforce, but you need to correlate it with marketing data, finance data, or product data. Salesforce Reports can’t easily do this.

5. Your RevOps team is spending 20%+ of their time building and maintaining reports. That’s a sign that Salesforce Reports has become a bottleneck.

6. You’re a scaling startup or mid-market company. You’re growing fast, your data complexity is increasing, and you need analytics to scale with you.

If none of these apply, Salesforce Reports might be fine for now. But as you scale, one of these will become true, and you’ll wish you’d migrated earlier.

The Implementation Decision

If you decide to migrate, here’s what to do:

1. Audit your current state: How many reports do you have? Which ones are actually used? What’s your current analytics cost?

2. Define your target state: What dashboards do you need? What role-based access do you need? What’s your governance requirement?

3. Calculate ROI: Using the cost analysis above, estimate your year 1 and year 2 savings.

4. Choose a partner: You need someone who understands both Salesforce and analytics infrastructure. Look for a vendor (like PADISO) who has shipped this pattern 50+ times and can deliver on a fixed timeline and budget.

5. Plan the migration: Use the phased approach outlined above. Start with high-impact dashboards, validate data, and migrate in waves.

6. Invest in training: Your team needs to understand the new architecture, how to update the semantic layer, and how to manage governance. This is non-negotiable.

For a concrete example of how this works, see our case study on the $50K D23.io consulting engagement. It covers everything from architecture to delivery in 6 weeks.

Beyond Reports: Agentic AI and Natural Language Queries

Once you have a managed Superset instance with a clean semantic layer, you unlock the next frontier: agentic AI.

Imagine your sales manager asking, “What’s our pipeline velocity by region this quarter?” and getting an answer instantly from a dashboard query. That’s possible with agentic AI like Claude integrated with Superset.

As detailed in our guide on agentic AI + Apache Superset, you can build agents that let non-technical users query dashboards in natural language. “Show me deals at risk” becomes a database query. “What’s our win rate vs. last quarter?” becomes a chart.

This is the future of analytics. It starts with a well-architected semantic layer, which is exactly what D23.io’s managed Superset gives you.

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Conclusion: The Path Forward

Migrating from Salesforce Reports to D23.io’s managed Superset is not just a technical change—it’s a governance and cost optimisation play.

You’re moving from a system where reports are scattered, governance is manual, and costs compound with every new user. You’re moving to a system where dashboards are centralised, governance is automated, and costs are predictable.

The typical engagement is 6 weeks, $50K fixed fee, and delivers:

• A fully managed Superset instance with SSO and role-based access control.
• A semantic layer that’s version-controlled and documented.
• 5–10 production dashboards, validated against Salesforce Reports.
• Training and 30 days of support.
• 45% cost savings in year 2.

At PADISO, we’ve shipped this pattern 50+ times across Sydney, Melbourne, Brisbane, and beyond. We know what works, what breaks, and how to deliver on time and on budget.

If you’re a RevOps leader, CFO, or CTO looking to centralise your CRM analytics, let’s talk. We can walk you through a discovery call, audit your current state, and build a migration plan tailored to your team.

Reach out to PADISO at https://padiso.co to learn more about our AI automation agency services and how we help teams modernise their analytics stack.