Sigma for dashboards
Dashboards that get to the answer
Sigma for modeling
Model every lever on live data
Change an input and the whole model recalculates on live warehouse data, so your team stops rebuilding it every week.
Schedule demo2 ways to model, and what today costs
What the current cycle costs
Every number below describes how teams model today, before Sigma.
1 day/wkWorkbook upkeep per user
0Upkeep, the model recalculates
2 to 3 wksBudgeted for a first model
3 hrsTo a first model prototype
1Version, emailed around
2Scenarios side by side, live
How it works
Build a model in 3 steps
- 1Set the assumptions
Set rate bands and driver weights in an Input Table. Every value lands in the governed warehouse.
- 2Compare scenarios
Duplicate the inputs, change 3 drivers, and see both versions on one chart in seconds.
- 3Lock and publish
Approve the version, then share the model as an app. Audit history shows who changed which input.
Set the assumptions. Compare scenarios. Lock and publish
Version history
Every version keeps its own inputs
v11approved
The signed baseline
v12 draft2 of 3
3 inputs changed since v11
v12locked
Rate band, accelerator, payout cap
A reviewer can open any version and see which value went in, who changed it, and when it locked.
Built for real modeling depth
01Explore
250+ functions in one workbook
Sigma keeps the formula freedom people expect from a spreadsheet, with 250+ functions and lookups on live tables. Custom SQL and Python run in the same workbook, so the logic stays together.
- Write formulas, SQL, and Python against the same live tables
- Query the warehouse live so no result rides on a stale extract
- Prototype in a sheet, then ship the same logic as an app
02Act
Editable inputs with a full audit trail
The people who own the assumptions type them in, and writeback lands in the warehouse. Approvals lock a version, and the audit trail answers which value went in.
- Type an override, lock the approved version, and trace every change
- Keep production tables untouched with row-level inserts
- Work from one governed metric definition across scenarios
03Govern
Governed AI in the loop
Sigma ships no model of its own. You point the workbook at your own LLM provider or a warehouse-native model, so every AI call runs inside the stack you already govern.
- Route every AI call through the provider you already approved
- Ask the Assistant which inputs drove a number
- Review an agent's scenario, then accept or reject it
Connections
Always on the newest trusted data
Sigma queries your warehouse live, so a workbook reads what landed a moment ago.
Governance stays in the warehouse
Sigma queries live tables on Databricks and Snowflake, and others like BigQuery and Amazon Redshift, under your roles.
Change historyAppend only
DWFP&A lead updated Driver on Enterprise West14:22
MEAnalyst edited Assumption on Enterprise East14:09
ROFinance director approved Scenario on Enterprise Central13:51
JTAnalyst reassigned Owner on Enterprise West11:04
Live query, no extracts
Every calculation reads the warehouse at run time, so the model and the source never drift apart.
Row-level security inherited
Each contributor sees only the rows their warehouse role allows. One set of rules governs the model.
Audit history on inputs
Input Tables keep a full change history, so a reviewer can see which value went in and when.
6 modeling apps to start from
Each app runs on live warehouse data, so you can copy the pattern and point it at your own tables.
Dynamic Profit & Loss Statement
Build a P&L in Sigma. Toggle years, granularity, and forecasts at the click of a button, all on live warehouse data.
Portfolio Risk Modeling and Analysis
Apply VaR and CVaR analytics to manage portfolio risk on Snowflake-integrated data. Real-time analysis and rebalancing.
Retail Dynamic Pricing
Analyze pricing adjustments and discount types vs. sales. Simulate promotion lift using a Databricks-hosted price-elasticity model.
Promotion Pricing Management
Optimize pricing and promotions on live warehouse data. Model scenarios, forecast lift, and act in a governed AI app.
ESG Portfolio Simulator
Analyze portfolio ESG ratings and model how holding adjustments shift the overall score. Blends portfolio data with ESG history.
Negative Margin
Financial app focused on margin nuances. Modify values for forecasting and planning across SKUs.
Every kind of app
Sigma for every kind of app
All 7 build on the same live, governed platform.
Explore
Sigma for forecasting
Own every forecast version
Sigma for planning
Own your plan in one place
Sigma for project tracking
Project tracking you can act on
Sigma for reconciliation
Reconcile every number in one place
Sigma for reference data
Fix reference data without a ticket
Common modeling questions
Make modeling a standing capability
Model pricing, commissions, or capacity whenever the question comes up, and let the decision hold up in an audit.
Schedule demoActivate your data warehouse
Stop buying a new tool for every workflow. Build it once on governed data, then scale it across the business.