.png){kind=logo}
Matillion excels at moving data from cloud applications into warehouses — building ETL/ELT pipelines with SQL transformations and enterprise-grade orchestration. Improvado extracts, transforms, normalizes, governs, and activates marketing data in one managed platform. Both integrate with Snowflake and BigQuery. One requires data engineers to maintain it; the other gives marketing teams direct control. If your team writes DBT models and manages warehouse infrastructure, Matillion offers depth and flexibility. If marketing owns the insights workflow and engineering wants to stay out of maintaining 500 API connectors, Improvado delivers the full lifecycle in a purpose-built system. The deciding factor: does your organization need general-purpose data movement, or does marketing need a governed platform that speaks campaign taxonomy natively?
Quick Verdict
Full disclosure: we're Improvado, and this page is written from our perspective. We've represented Matillion's capabilities based on public documentation, G2 reviews, and competitor research. Where we've gotten details wrong, email us and we'll correct them. Our goal is to help you make the right decision for your architecture — even if that's Matillion.
Platform Architecture: ELT for Engineers vs End-to-End for Marketers
Matillion is a cloud-native ELT platform. It extracts data from 150+ sources, loads raw data into your warehouse (Snowflake, BigQuery, Redshift, Databricks), and provides visual + SQL tools for transformation. The platform assumes you have engineering resources to build recipes, manage dependencies, and handle schema changes. AI Copilot accelerates pipeline creation, but the paradigm remains code-adjacent: data engineers own the transformations, and business users consume outputs via BI tools downstream.
Improvado is a marketing intelligence platform. It extracts from 500+ marketing, sales, and analytics sources; applies the Marketing Common Data Model (pre-built schema normalization for campaigns, ad groups, UTM parameters, cost, conversions); enforces governance rules before data hits the warehouse; and activates insights via native dashboards or pushes clean data to Looker, Tableau, or Power BI. The paradigm: marketing teams configure pipelines via drag-and-drop, data engineers access full SQL when needed, and everyone works from the same governed dataset.
The architectural difference: Matillion is warehouse-first (raw data lands, you transform it). Improvado is insight-first (transformation happens before warehouse delivery, governed by marketing logic). Matillion gives engineers maximum control. Improvado gives marketers maximum self-service.
Feature Comparison: Improvado vs Matillion
| Capability | Improvado | Matillion |
|---|---|---|
| Platform Type | End-to-end marketing intelligence (ETL + transformation + governance + activation) | Cloud-native ELT for general data pipelines |
| Data Connectors | 500+ pre-built (marketing, sales, analytics); custom connectors in 2–4 weeks with SLA | 150+ pre-built (SaaS, databases, cloud services); community exchange for custom |
| Transformation Approach | No-code Marketing Common Data Model + full SQL access; marketers configure, engineers extend | Visual drag-and-drop + custom SQL + DBT integration; requires engineering ownership |
| Marketing Data Governance | 250+ pre-built rules (budget pacing, pre-launch validation, cross-channel consistency) | General data quality checks; no marketing-specific governance layer |
| Data Destinations | Any warehouse (Snowflake, BigQuery, Redshift) + native dashboards + BI tool compatibility | Snowflake, BigQuery, Redshift, Databricks, Azure Synapse; BI tools consume downstream |
| AI Capabilities | AI Agent for natural language queries; automated anomaly detection; insights summarization | AI Copilot generates pipelines from prompts; Maia bot assists with workflow creation |
| Implementation Model | Dedicated CSM + professional services included; 2–4 week onboarding for most deployments | Self-service or enterprise implementation; support via tickets + enterprise plans include TAM |
| Pricing Model | Outcome-based (data sources + volume); predictable annual commitment | Pay-as-you-go tied to warehouse compute credits; scales with usage |
| Enterprise Compliance | SOC 2 Type II, HIPAA, GDPR certified | Enterprise-grade security; certifications available for cloud deployments |
Feature comparison: Improvado vs Matillion (updated February 2026)
Where Improvado and Matillion Diverge
Marketing Teams Own the Pipeline — No Engineering Bottleneck
Matillion's visual interface reduces the need for hand-coded SQL, but pipeline creation, maintenance, and schema evolution still require data engineering skills. When a new campaign launches and you need The Trade Desk data flowing into your attribution model by Monday, that's a ticket to the data team. When Facebook changes its API structure (again), someone technical debugs the transformation recipe. The platform is accessible compared to legacy ETL tools, but it's not self-serve for marketers.
Improvado inverts that model. Marketing operations teams configure new connectors via UI, map fields using the Marketing Common Data Model (MCDM), and activate dashboards — no SQL required for 95% of workflows. The MCDM automatically normalizes campaign structures, UTM taxonomies, and cost-per-conversion calculations across 500+ sources. When edge cases arise (custom attribution logic, multi-touch modeling), data engineers access the full SQL layer to extend transformations. But the baseline pipeline doesn't require engineering involvement.
The outcome: marketing velocity increases. New channels go live in days, not sprints. Analysts spend time answering business questions instead of filing Jira tickets for data access.
Signal Theory operates Improvado pipelines for dozens of clients without expanding their data engineering headcount — the self-serve model scales agency operations in ways general-purpose ELT platforms can't match.
Marketing Data Governance — Pre-Built Rules, Not Custom Validation Scripts
Matillion offers data quality checks and validation workflows, but these are generic: null value detection, schema compliance, row count monitoring. Building marketing-specific governance — budget pacing alerts, cross-channel taxonomy consistency, pre-launch creative validation — requires custom SQL logic and orchestration. Data teams write the rules. Marketing teams wait for them.
Improvado ships 250+ pre-built marketing governance rules. The platform validates campaign naming conventions against your taxonomy before data reaches the warehouse. It flags budget overruns in real time (not after month-end reconciliation). It detects when UTM parameters diverge across channels and surfaces the inconsistency before reports go to stakeholders. These aren't add-ons or customizations — they're core platform features that activate on day one.
Why this matters: governance failures are expensive. A $50K budget error caught in-flight saves the campaign. A naming taxonomy break caught before the executive dashboard prevents a two-hour explanation meeting. Matillion gives you the tools to build governance. Improvado delivers governance as a managed service.
“If we don't trust the data, the agency won’t trust the reports and won't give them to the client. They’ll start pulling data manually to Excel and spend a lot of time comparing platform numbers to reports. With Improvado, we now trust the data. If anything is wrong, it’s how someone on the team is viewing it, not the data itself. It’s 99.9% accurate.”
500+ Marketing Connectors with SLA-Backed Custom Builds
Matillion's 150+ connectors cover major SaaS platforms, databases, and cloud services. For general data engineering, that's sufficient. For marketing teams running campaigns across 30+ platforms — including emerging channels like TikTok Ads, Reddit Ads, Snapchat, AppsFlyer, Adjust, and niche affiliate networks — connector gaps become operational blockers. Matillion's community exchange offers some coverage, but quality and maintenance are inconsistent. Building a custom connector is possible, but it's your engineering team's project.
Improvado maintains 500+ pre-built connectors purpose-built for marketing, sales, and analytics use cases. The library includes every major ad platform, CRM, marketing automation tool, analytics suite, and affiliate network. When a connector doesn't exist, Improvado builds it with a 2–4 week SLA as part of standard service — not as a billable custom project. The connector library isn't static; it evolves with the marketing technology landscape.
The impact: marketing teams launch new channels without engineering dependency. When a client requests LinkedIn Campaign Manager integration on Wednesday, it's live by the following Monday. That speed compounds — agencies managing 50 client accounts can't wait three months for custom connector development every time a client adds a platform.
Dedicated CSM and Professional Services — Included, Not Add-On
Matillion's support model scales by tier. Free and basic plans rely on documentation and community forums. Enterprise customers access priority support and technical account managers. Implementation is self-service unless you purchase professional services separately. For teams with strong internal data engineering talent, this works. For organizations where marketing owns analytics and engineering is already stretched thin, the support gap becomes a hidden cost.
Improvado includes a dedicated Customer Success Manager and professional services in every contract. The CSM owns onboarding, pipeline configuration, dashboard setup, and ongoing optimization. When new connectors launch or schema changes occur upstream, the CSM proactively manages the transition — clients don't discover breaks via failed reports. Professional services aren't billable add-ons; they're how the platform operates.
The ROI surfaces quickly. A DIY implementation that takes three months with internal resources ships in two weeks with Improvado's managed service. Ongoing maintenance — connector updates, schema migrations, dashboard iterations — happens without internal tickets. The platform becomes an extension of your team, not a tool your team maintains.
No-Code for Marketers, Full SQL for Engineers — Dual-Persona Design
Matillion's interface balances visual workflows with SQL power, but the target user is unambiguous: data engineers. Marketers can view dashboards downstream, but they don't configure transformations or build pipelines. The platform assumes technical ownership. That's appropriate for general data infrastructure, but it creates dependency when marketing needs to move quickly.
Improvado is architected for two personas simultaneously. Marketing operations teams use the no-code UI to configure connectors, map fields via MCDM, and activate dashboards. Data engineers access the full SQL transformation layer when custom logic is required — multi-touch attribution models, cohort analysis, predictive scoring. Neither persona blocks the other. Marketers don't wait for engineering to add a new data source. Engineers don't get buried in tickets for routine pipeline maintenance.
This dual-access model is rare in the category. Most platforms optimize for one user type and compromise the other. Improvado treats both as first-class citizens. The result: faster iteration cycles, fewer bottlenecks, and analytics infrastructure that scales with organizational complexity.
When to Choose Matillion
Matillion is the right choice in specific scenarios where its general-purpose ELT architecture and engineering-first design align with your team structure and goals:
- Your data engineering team owns transformation logic across the enterprise. If you're building a unified data platform that integrates financial, operational, customer, and marketing data — and data engineers manage all transformation recipes via DBT or SQL — Matillion's flexibility and control are valuable.
- You need deep SQL-level transformation with CI/CD promotion workflows. Matillion supports draft/active pipeline promotion, version control integration, and enterprise DevOps practices. If your organization has strict change management requirements and data engineers review every transformation before production, Matillion's workflows fit that model.
- You're consolidating data from non-marketing sources. If your primary use case is integrating ERP systems, customer support platforms, product analytics, and operational databases — not managing 500 marketing API connectors — Matillion's connector library is sufficient and its warehouse-native approach is efficient.
- You have warehouse infrastructure already optimized. Teams running on Snowflake or BigQuery with established cost controls, query optimization, and capacity planning benefit from Matillion's pushdown ELT (transformations run in-warehouse, leveraging your existing compute). Improvado can deliver to any warehouse, but Matillion is purpose-built for that architecture.
- Your budget prioritizes pay-as-you-go flexibility over predictable annual costs. Matillion's usage-based pricing scales with warehouse compute. For teams with highly variable data volumes or seasonal workloads, this can be more cost-effective than Improvado's outcome-based annual commitment.
If your organization has the engineering bandwidth to maintain pipelines, values transformation flexibility above self-serve marketing access, and needs broad coverage beyond marketing data — Matillion is a defensible choice. Evaluate it against the total cost of engineering time required to build and maintain what Improvado delivers as a managed service.
What Improvado Customers Say
At ASUS, Enhanced data availability not only enabled faster experimentation but also allowed teams across regions to "speak the same language." This increased transparency in global marketing efforts provides a strong foundation for innovative, data-driven strategies that maximize the impact of global marketing investments. Read full case study →
"Now, we don't have to involve our technical team in the reporting part at all. Improvado saves about 90 hours per week and allows us to focus on data analysis rather than routine data aggregation, normalization, and formatting."
At Signal Theory, for most clients, the reporting process is now fully automated, taking 30 minutes to an hour at most. For clients with unique data sources requiring manual input, the system is flexible enough to accommodate specific needs without compromising overall efficiency. Read full case study →
“Reports that used to take hours now only take about 30 minutes. We're reporting for significantly more clients, even though it is only being handled by a single person. That's been huge for us.”
Pricing Comparison
Matillion pricing: Pay-as-you-go model tied to warehouse compute credits. No public pricing tiers; enterprises request custom quotes based on data volume, connector count, and usage patterns. The model scales cost with warehouse activity, which benefits teams with seasonal or variable workloads but introduces unpredictability when data volumes spike. Hidden costs include: separate DBT Cloud subscriptions if you're using DBT for transformation, BI tool licenses (Matillion doesn't include visualization), and engineering time to build and maintain custom connectors or transformations.
Improvado pricing: Outcome-based annual commitment. Cost drivers: number of data sources, data volume processed, and deployment model (managed warehouse vs. bring-your-own Snowflake/BigQuery). Custom connector builds included within SLA (2–4 weeks). Professional services, dedicated CSM, and Marketing Common Data Model access included — not add-ons. Predictable annual budgeting with no surprise usage spikes. Total cost of ownership is lower for marketing-heavy use cases because connector maintenance, transformation logic, and governance are managed services.
Total cost of ownership comparison: Matillion's sticker price may appear lower initially, but calculate engineering time required to maintain pipelines. If a data engineer spends 20 hours/month managing connector updates, schema changes, and transformation debugging at a $150K fully-loaded cost, that's $18K/year in hidden expense. Improvado's managed service eliminates that labor cost. For teams where engineering time is the bottleneck, Improvado delivers better ROI despite a higher platform fee.
See detailed pricing breakdown at improvado.io/pricing.
Frequently Asked Questions
What is the main difference between Improvado and Matillion?
Matillion is a general-purpose ELT platform designed for data engineers to build pipelines from any source to cloud warehouses, with transformation handled via SQL or visual workflows. Improvado is a marketing intelligence platform that extracts, transforms (via pre-built Marketing Common Data Model), governs, and activates marketing data — purpose-built for marketing teams to operate pipelines without engineering dependency. Matillion optimizes for flexibility and control. Improvado optimizes for marketing self-service and speed.
Can Improvado replace Matillion for non-marketing data pipelines?
No. Improvado is architected specifically for marketing, sales, and analytics data. If your organization needs to integrate ERP systems, financial data, customer support platforms, or operational databases, Matillion's broader connector library and general-purpose transformation tools are better suited. Improvado excels at the marketing data lifecycle; Matillion excels at enterprise data engineering across functions.
Does Matillion offer marketing-specific data governance?
Matillion provides general data quality checks (null detection, schema validation, row counts), but it doesn't include marketing-specific governance out of the box. Building rules for budget pacing, campaign naming taxonomy enforcement, or cross-channel consistency requires custom SQL logic. Improvado ships 250+ pre-built marketing governance rules that activate immediately — no custom scripting required.
How long does it take to migrate from Matillion to Improvado?
Typical migration takes 2–4 weeks for most marketing data stacks. Improvado's professional services team handles connector configuration, schema mapping via Marketing Common Data Model, and dashboard setup. The timeline depends on connector count, transformation complexity, and whether you're migrating historical data. Improvado runs parallel pipelines during migration to ensure zero downtime.
Can I use DBT with Improvado?
Yes. Improvado delivers transformed, governed data to your warehouse (Snowflake, BigQuery, Redshift), where DBT models can operate on top of it. Many customers use Improvado for marketing data extraction and MCDM normalization, then apply DBT for cross-functional transformations that join marketing data with product, finance, or customer data. The platforms are complementary, not mutually exclusive.
Does Improvado support the same warehouses as Matillion?
Yes. Improvado integrates with Snowflake, Google BigQuery, Amazon Redshift, Databricks, and Azure Synapse. You can bring your own warehouse (BYOW), use Improvado's managed warehouse, or deploy via Snowflake Native App. Matillion also supports these platforms with pushdown ELT optimization. Both platforms are warehouse-agnostic at the integration level.
Which platform is better for agencies managing multiple client accounts?
Improvado. Agencies need rapid client onboarding, multi-account workspace isolation, and self-serve pipeline configuration so client requests don't become engineering tickets. Improvado's 500+ connectors, 2–4 week custom connector SLA, and dedicated CSM model are purpose-built for agency operations. Matillion's engineering-first design requires more technical overhead per client, which doesn't scale efficiently for agencies managing dozens of accounts.
When does Matillion win over Improvado?
Matillion wins when your data engineering team owns transformation logic across the entire organization (not just marketing), you need deep SQL-level control with CI/CD workflows, and you're integrating non-marketing data sources like ERP, financial, or operational systems. If marketing is one input among many in a broader data platform strategy — and you have engineering bandwidth to maintain pipelines — Matillion's flexibility justifies the overhead. If marketing owns analytics end-to-end, Improvado's managed service delivers better ROI.
The Bottom Line
Matillion and Improvado solve different problems. Matillion is a data engineering tool that happens to connect to marketing platforms. Improvado is a marketing intelligence platform that happens to integrate with enterprise data infrastructure. The distinction matters.
Choose Matillion if your data team owns the transformation layer, you're building cross-functional data infrastructure, and you value control over self-service. Choose Improvado if marketing needs the full pipeline — extraction, transformation, governance, activation — without engineering dependency, and speed-to-insight is the priority.
The honest answer: most organizations evaluating these platforms aren't choosing between them — they're choosing between building marketing analytics infrastructure in-house with general-purpose tools (Matillion + DBT + custom scripts) or buying a managed solution purpose-built for marketing (Improvado). The build path gives you flexibility. The buy path gives you velocity. Calculate the cost of engineering time accordingly.
FAQ
.png)
