Mode Analytics is a collaborative business intelligence platform that combines SQL editing, Python notebooks, and interactive dashboards in a single workspace. Designed to bridge the gap between data teams and business users, Mode makes it easy to build analyses and share findings across the organization.
Product Overview
Mode gives analysts a multi-language notebook environment (SQL + Python + R) connected directly to their cloud data warehouse. Results are rendered as interactive charts and dashboards that business users can explore, filter, and share without writing code. Its version control and commenting features support collaboration across data and business teams.
Key Features
- Multi-Language Notebooks: Write SQL, Python, and R in the same report with results rendered inline as visualizations.
- Interactive Dashboards: Publish reports as interactive dashboards with filter controls, drill-downs, and sharing permissions.
- Report Scheduling: Automatically run and refresh reports on a schedule with email delivery.
- Data Studio: Exploratory data environment for ad-hoc analysis with chart builder and statistics summary views.
- Team Collaboration: Comment on reports, fork analyses, and track version history for collaborative data work.
Best For
Data analytics teams at mid-market and enterprise companies that need to collaborate with business users and publish polished, self-service reports.
Pricing
Free plan for individual use. Teams plan priced per user/month. Enterprise pricing available. Contact Mode for details.
Key Integrations
Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, MySQL, Looker, Slack
Pros
- Multi-language support in a single notebook environment
- Strong collaboration features for data teams and business users
- Direct query connections eliminate data extraction and staging
Cons
- Not as well-suited for non-technical business users as Tableau or Power BI
- Limited built-in data prep and transformation capabilities
RevOps Jobs-to-Be-Done
- SQL-First Analytics for Data Teams — Build, share, and schedule SQL-powered reports and dashboards with a workflow designed for data analysts and analytics engineers. KPI: Enable data teams to publish self-service analytics 3x faster with built-in version control and scheduling
- Python and R Analytics Integration — Extend SQL analysis with Python and R notebooks in the same workflow for advanced statistical analysis and data science. KPI: Bridge the gap between business analytics and data science in a single collaborative platform
- Revenue Metrics and Pipeline Reporting — Connect directly to the data warehouse and build live pipeline, revenue, and funnel reports that update automatically. KPI: Replace manual pipeline reports with warehouse-connected live dashboards available to go-to-market leadership
How It Fits Your Stack
Primary system of record: Data warehouses and databases
Key integrations: Snowflake, BigQuery, Redshift, PostgreSQL, Spark, dbt, Slack
Data flows: Direct database connections; SQL, Python, and R analysis; results scheduled and shared; Slack integrations for report delivery
Security & Compliance
- SSO / SAML: SAML 2.0 and Google SSO
- RBAC / permissions: Yes
- Audit logs: Yes
- Certifications: SOC 2 Type II
- Data residency: US
Implementation & Ownership
- Time to first value: 1–3 days for data analysts
- Implementation complexity: Low — SQL-native tool; requires data warehouse access
- Typical owners: Data Analyst, Analytics Engineer, RevOps Analyst
Strong in data-forward startups and growth companies; acquired by ThoughtSpot in 2023
Proof & Buyer Signals
Ratings: G2: 4.5/5 (300+ reviews)
What buyers praise:
- SQL-first workflow is clean
- Good collaborative features for data teams
- Python/R integration is unique among BI tools
Common complaints:
- Less intuitive for non-SQL business users
- ThoughtSpot acquisition direction is uncertain
Often Compared With
- Count — Count offers a more visual collaborative canvas; Mode is more structured for data teams that want SQL-first analysis with scheduling
- Sisense — Sisense is more business-user-oriented with embedded analytics; Mode targets data analysts and engineers who prefer code-first workflows
- Observable — Observable uses JavaScript notebooks for developers; Mode provides SQL/Python/R notebooks for analytics teams building business reports
