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Sigma Computing and Improvado both help teams make sense of data, but they approach the problem from opposite directions. Sigma gives business users a spreadsheet-like interface to query live data in cloud warehouses — it's built for exploratory analysis. Improvado is a marketing-specific data pipeline platform that extracts from 500+ sources, transforms the data, and delivers governed, analysis-ready datasets to your BI tool or warehouse. Same goal of enabling data-driven decisions, fundamentally different architectures.
Sigma Computing vs Improvado: Which Is Right for Marketing Teams?
Sigma Computing positions itself as a modern BI layer — business users query Snowflake, BigQuery, or Redshift data directly through a familiar spreadsheet interface, no SQL required. Improvado is an end-to-end marketing data platform: it pulls data from advertising platforms, analytics tools, and CRMs, transforms it into analysis-ready formats, applies governance rules, and delivers clean datasets to whatever BI tool you already use.
The core difference: Sigma assumes your data is already in a warehouse and focuses on the exploration and visualization layer. Improvado handles the entire upstream pipeline — extraction, transformation, normalization, and governance — before your data ever reaches the analysis stage.
A Note on Perspective
Full disclosure: we're Improvado, and this page is written from our perspective. We've tried to represent Sigma Computing's capabilities accurately — and where we've gotten it wrong, email us and we'll fix it. Our goal is to help you make the right call, even if that's not us.
Quick Verdict
Feature Comparison: Improvado vs Sigma Computing
| Feature | Improvado | Sigma Computing |
|---|---|---|
| Platform type | Marketing ETL + transformation + governance platform | Cloud BI and exploration layer for warehouse data |
| Data connectors | 500+ pre-built, custom builds in 2–4 weeks (SLA) | Direct warehouse access (Snowflake, BigQuery, Redshift); third-party via Stitch/Fivetran |
| Data transformation | No-code for marketers + full SQL; 46,000 pre-mapped metrics; Marketing Cloud Data Model (MCDM) | Spreadsheet-like formulas + SQL interface; users build custom models |
| Marketing Data Governance | 250+ pre-built rules, pre-launch budget validation, 2-year historical preservation | Role-based access, audit logs, version control; no marketing-specific rules |
| AI capabilities | AI-assisted metric mapping, anomaly detection, query assistance | AI-driven recommendations, natural language generation (NLG) for insights |
| Data destinations | Any BI tool (Looker, Tableau, Power BI), any warehouse, custom apps via API | Native dashboards + embedded analytics; exports to Google Sheets, Excel |
| Implementation model | Dedicated CSM + professional services included; 2–4 week typical onboarding | Self-service onboarding; support via business hours tickets |
| Pricing model | Outcome-based; scales with data sources and team size | Starts at $300/month; pricing details not publicly disclosed beyond entry tier |
| Enterprise compliance | SOC 2 Type II, HIPAA, GDPR certified | SSO/IdP integration, container security, role-based access |
The Core Architectural Difference
Sigma Computing is a BI tool. Improvado is a data pipeline platform. That's not a value judgment — it's a category distinction that determines what problems each platform actually solves.
Sigma's Approach: Explore What's Already There
Sigma connects directly to your cloud data warehouse and lets business users build queries, dashboards, and visualizations using a spreadsheet-like interface. The value proposition: no SQL required, no data movement, live access to warehouse data. You write formulas the way you would in Excel, and Sigma translates them into optimized SQL queries that run against Snowflake or BigQuery.
The critical assumption: your marketing data is already in the warehouse, already cleaned, already joined, already in a queryable schema. Sigma doesn't extract data from Facebook Ads or Google Analytics — it visualizes data that's already landed in your warehouse via some other ETL process.
Improvado's Approach: Build the Pipeline, Then Explore
Improvado starts earlier in the data lifecycle. It extracts data from 500+ marketing sources (ad platforms, analytics tools, CRMs, attribution systems), transforms it into a unified schema using the Marketing Cloud Data Model, applies governance rules to catch budget errors and data quality issues, and then delivers clean datasets to your BI tool or warehouse. You can use Sigma on top of Improvado's output — or Looker, Tableau, Power BI, or a custom dashboard.
The platform owns the entire upstream pipeline. API maintenance, schema changes, historical data preservation, cross-platform metric normalization — Improvado handles it. Your team gets analysis-ready data without building and maintaining ETL infrastructure.
Where Improvado and Sigma Computing Diverge
Your Marketing Team Operates the Pipeline — No Engineering Tickets
Most marketing teams don't have dedicated data engineers. And even when they do, those engineers are solving product analytics problems, not stitching together Facebook Ads API changes every quarter. Improvado's 500+ pre-built connectors eliminate that dependency entirely. When TikTok releases a new attribution endpoint or Google Ads deprecates a metric, Improvado updates the connector. Your dashboards keep running.
When you need a connector that doesn't exist — an internal attribution system, a proprietary ad server, a regional analytics platform — Improvado builds it under a 2–4 week SLA. That's in the contract, not a vague promise. Sigma doesn't build connectors at all; it assumes your warehouse integration is handled elsewhere.
“We needed to add platforms quickly, and Improvado offered a clear path forward. Once we integrated with it, we could skip the learning curve for complex systems like Google Ad Manager. Instead of investing months to understand each API, we could add them directly to our portfolio and see the data almost immediately.”
For agencies managing 30+ client data stacks or enterprises running campaigns across 50+ channels, connector breadth and maintenance ownership isn't a feature — it's the entire cost structure of the data function.
Marketing Data Governance: 250+ Rules That Prevent Budget Errors Before They Happen
Clean data and accurate data are not the same thing. You can have perfectly formatted CSVs that confidently report you spent $120K over budget because a UTM parameter was misspelled or a conversion event fired twice. Sigma has role-based access and audit logs — standard BI security. It doesn't know what a "valid CPM range" is, or that your Facebook spend shouldn't exceed the campaign budget you set in the platform.
Improvado's Marketing Data Governance layer includes 250+ pre-built rules: budget pacing alerts, metric anomaly detection, cross-platform consistency checks, UTM taxonomy validation. These rules run before data hits your dashboards. If a campaign overspends, you get a Slack alert before the CFO sees the report. If Google Ads and Facebook Ads report conflicting conversion counts for the same campaign, the system flags it for reconciliation.
There's also pre-launch validation: before a new campaign goes live, Improvado checks that tracking parameters are correct, budgets are set, and attribution logic aligns with your taxonomy. Most marketing data platforms have no equivalent to this — they report what happened, not whether what happened makes sense.
“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.”
No-Code for Marketers, Full SQL for Engineers — The Same Platform
Sigma's spreadsheet interface is genuinely accessible to non-technical users — that's its flagship strength. But it's still a query tool. You're writing formulas against warehouse tables, and if those tables aren't already structured for marketing analysis, you're doing a lot of joins and aggregations before you get to the actual question you wanted to answer.
Improvado ships with the Marketing Cloud Data Model: a pre-built schema that normalizes Facebook Ads, Google Ads, LinkedIn Ads, TikTok Ads, and 500+ other sources into a unified structure. Metrics like "cost per conversion" mean the same thing across platforms. Campaign hierarchies are consistent. Attribution windows are standardized. Marketers can build reports in the no-code interface without understanding SQL joins. Data engineers can drop into full SQL mode when they need custom transformations.
That dual-persona design is rare. Most platforms pick one audience — either they're SQL-heavy and alienate marketers, or they're no-code and frustrate engineers who need flexibility. Improvado doesn't make you choose.
Dedicated Customer Success vs. Ticket-Only Support
Sigma offers business-hours support via tickets, documentation, and webinars. Standard SaaS support model. Improvado includes a dedicated Customer Success Manager and professional services as part of the platform — not an add-on. When you onboard, you're assigned a CSM who learns your data stack, helps configure governance rules, and handles connector requests.
The difference shows up when APIs change. Facebook deprecates an endpoint; Google Ads renames a metric. With ticket-based support, you file a ticket when your dashboard breaks, wait for a response, and manually update your queries. With dedicated CSM support, Improvado's team updates the connector before the API change goes live, tests it against your data, and emails you a summary of what changed. Your dashboards don't break in the first place.
For agencies managing multiple client accounts, that proactive model is the only way the support economics work. You can't file 30 tickets every time Meta changes an API.
When to Choose Sigma Computing
Sigma is the right choice if you already have a well-maintained data warehouse and need a self-service BI layer for business users. Specifically, choose Sigma when:
- Your marketing data is already in Snowflake, BigQuery, or Redshift via Fivetran, Stitch, or a custom ETL process — and it's clean, transformed, and ready to query.
- You have a data engineering team that owns warehouse schema design and transformation logic, and they're comfortable maintaining that infrastructure long-term.
- Your primary use case is exploratory analysis and ad-hoc dashboarding by business users who prefer a spreadsheet-like interface over SQL or traditional BI tools.
- You need embedded analytics — Sigma's embeddable dashboards integrate into SaaS products and customer-facing portals more easily than most ETL platforms.
- Your marketing stack is relatively simple (fewer than 15 data sources) and you're not running complex multi-touch attribution or cross-channel budget governance.
In short: Sigma excels when the hard part of data work — extraction, transformation, governance — is already solved elsewhere. It's a visualization and exploration tool, not a pipeline platform.
What Customers Say About Improvado
Improvado's customers consistently highlight three things: the breadth of connectors, the elimination of manual reporting work, and the responsiveness of the customer success team. Here's what that looks like in practice.
At Signal Theory, Improvado’s wide range of connectors—including the much-needed GA4—was a game-changer. Improvado also simplified the process of merging data from various sources and solved a major bottleneck that had been too cumbersome in the agency’s previous platform. Read full case study →
“Improvado’s connectors were huge for us in overcoming the limitations of our previous platform. I don't think we would have been able to get as far with data as we are now.”
At Yodel Mobile, the most significant impact has been the dramatic reduction in time spent on routine reporting tasks. What previously required hours of manual data manipulation can now be completed in minutes. Read full case study →
“On the reporting side, we saw a significant amount of time saved! Some of our data sources required lots of manipulation, and now it's automated and done very quickly. Now we save about 80% of time for the team.”
These aren't isolated examples. The pattern repeats across industries: agencies consolidating client data, enterprises unifying global campaigns, and in-house teams eliminating the weekly manual reporting ritual. The common thread is that Improvado handles the unglamorous infrastructure work — API maintenance, schema changes, data quality checks — so teams can focus on analysis and optimization.
Pricing Comparison
Sigma Computing Pricing
Sigma Computing's starting price is $300 per month, according to publicly available sources as of early 2026. Beyond that entry tier, Sigma does not publish detailed pricing breakdowns. Enterprise pricing is available on request and scales with the number of users, data volume, and feature requirements. Users rate Sigma's value for money at 4.7 out of 5 on review platforms, suggesting the pricing is perceived as fair relative to functionality.
Improvado Pricing
Improvado uses an outcome-based pricing model that scales with the number of data sources, the complexity of transformations, and team size. There's no per-row or per-query metering — the cost is predictable and tied to what you're actually using the platform to accomplish. Dedicated customer success management and professional services are included in all plans, not billed separately.
Pricing details are available on request — contact Improvado's team for a customized quote based on your data stack.
Total Cost of Ownership
Sigma's $300/month entry price doesn't include the cost of getting data into your warehouse in the first place. If you're using Fivetran or Stitch to extract marketing data, that's a separate subscription. If you're building custom connectors in-house, that's engineering time. If your data needs transformation after it lands in the warehouse, that's either dbt Cloud or more engineering hours.
Improvado's pricing includes extraction, transformation, governance, and support. You're comparing Sigma + Fivetran + dbt Cloud + engineering time against Improvado's single platform cost. For most marketing teams, the consolidated model is cheaper — and it eliminates the coordination overhead of managing three vendor relationships.
Frequently Asked Questions
What is the main difference between Improvado and Sigma Computing?
Improvado is a marketing data pipeline platform that extracts data from 500+ sources, transforms it, and delivers analysis-ready datasets to your BI tool or warehouse. Sigma Computing is a cloud BI tool that lets business users explore and visualize data that already exists in Snowflake, BigQuery, or Redshift. Improvado solves the upstream pipeline problem; Sigma solves the downstream exploration problem. You can use both together — Improvado to populate the warehouse, Sigma to visualize it — or choose one based on where your data bottleneck actually is.
Can Sigma Computing replace Improvado?
No. Sigma doesn't extract data from marketing platforms, doesn't transform raw API responses into analysis-ready formats, and doesn't include marketing-specific governance rules. If your data is already in a warehouse and clean, Sigma is a strong BI layer. If you need to get marketing data into a warehouse in the first place, you need an ETL platform like Improvado, Fivetran, or a custom-built pipeline.
Does Improvado work with Sigma Computing?
Yes. Improvado can deliver data to Snowflake, BigQuery, or Redshift, and Sigma can query those warehouses. Many teams use Improvado to handle extraction, transformation, and governance, then use Sigma (or Looker, Tableau, Power BI) as the visualization layer. Improvado is BI-agnostic — it works with whatever tool your team prefers.
How long does it take to migrate from Sigma Computing to Improvado?
This isn't a migration in the traditional sense — Sigma and Improvado solve different problems. If you're currently using Sigma to visualize data that was manually uploaded or extracted via another tool, switching to Improvado means automating that extraction and transformation layer. Typical onboarding is 2–4 weeks: connector configuration, transformation rules setup, and initial data backfill. You can keep using Sigma for visualization if you want; you'd just be pointing it at Improvado-managed warehouse tables instead of manually maintained ones.
Which platform is better for agencies managing multiple clients?
Improvado. Agencies need to onboard new clients quickly, consolidate data from dozens of ad accounts across multiple platforms, and produce white-labeled reports without rebuilding infrastructure for each client. Improvado's 500+ connectors, multi-tenant architecture, and dedicated CSM support are purpose-built for that use case. Sigma is a strong tool for a single company's internal analytics team, but it doesn't solve the multi-client data aggregation and governance problem that agencies face.
Does Sigma Computing offer marketing-specific features?
No. Sigma is a general-purpose BI tool designed for exploring warehouse data. It doesn't include pre-built marketing metrics, campaign attribution models, budget governance rules, or UTM taxonomy validation. You can build those things yourself using Sigma's formula language and custom dashboards, but the platform doesn't ship with marketing-specific templates or governance frameworks the way Improvado does.
What happens when Facebook or Google changes their API?
With Improvado, the team updates the connector, tests it, and deploys the fix before the API change breaks your dashboards. You get an email summary of what changed. With Sigma, API changes don't affect Sigma itself — they affect whatever ETL tool you're using to get data into your warehouse. If that's Fivetran, you're waiting for Fivetran to update. If it's a custom script, you're fixing it yourself. Sigma visualizes data; it doesn't manage the upstream connectors.
Can I use Improvado without a data warehouse?
Yes. Improvado can deliver data directly to BI tools like Looker, Tableau, or Power BI without requiring a separate warehouse. It can also populate Google Sheets or Excel for teams that need quick reporting without infrastructure. If you do have a warehouse, Improvado supports Snowflake, BigQuery, Redshift, Databricks, and others — you bring your own warehouse, or Improvado can provision a managed instance.
Making the Right Choice
The Sigma vs. Improvado decision comes down to a single question: where does your data problem actually live? If your bottleneck is exploration and visualization — your data is already clean and in a warehouse, but business users can't easily query it — Sigma solves that. If your bottleneck is getting marketing data into a usable state in the first place — 30 API connections to maintain, inconsistent schemas, no governance, manual CSV exports every Monday — Improvado solves that.
You can use both. Improvado extracts and transforms; Sigma visualizes. But most teams find that once the pipeline is automated and governed, their existing BI tool (Looker, Tableau, Power BI, even Google Sheets) handles the visualization layer just fine. The expensive problem to solve is the infrastructure, not the charts.
If you're a marketing team without dedicated data engineering support, or an agency managing dozens of client accounts, or an enterprise running global campaigns across 50+ platforms — the pipeline is the problem. Solve that first.
FAQ
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