Sigma Computing QuickStart

The Sigma Computing QuickStart focuses on rapid activation of self-service analytics on top of an existing or newly connected data platform. The goal is not to perfect the data model, but to get business users answering real questions in Sigma as quickly as possible, while keeping governance and performance under control.

This QuickStart typically runs over 2–4 weeks, depending on scope and data readiness.

How this QuickStart works

1. Use Case & User Alignment

The QuickStart starts by aligning on who Sigma is for and what they need to do with it. You identify the primary business personas (e.g. finance, revenue ops, operations) and the decisions they struggle to answer today.

You intentionally limit scope to 1–2 core use cases to ensure fast adoption and visible impact.

Output:
Clear Sigma use cases, target user groups, and success criteria.

2. Data & Platform Readiness Check

Before connecting Sigma, you validate that the underlying data platform is ready for interactive analytics. This includes checking table structures, permissions, and performance characteristics relevant to Sigma’s live-query model.

Rather than redesigning data models, you assess fitness for purpose:

Output:
Confirmed readiness (or a short fix list) for Sigma activation.

3. Sigma Environment Setup & Connectivity

You then set up the Sigma environment and connect it to the target data platform (Snowflake, Databricks SQL, BigQuery, etc.). Authentication and access are aligned with existing identity and data governance policies.

Sigma is positioned as a thin, governed analytics layer — not a new data silo.

Output:
Live Sigma environment connected to production data.

4. Core Analytics & Metrics Setup

With connectivity in place, you create the first Sigma datasets, calculations, and metrics that map directly to the agreed use cases. The goal is to define just enough logic to ensure consistency, without over-engineering a semantic layer.

This step often replaces Excel logic that previously lived in individual files.

Output:
Sigma workbooks and datasets that reflect real business questions.

5. User Enablement & Guided Adoption

Once analytics are live, you onboard users through hands-on enablement sessions. Instead of generic training, users work with their own data and use cases.

Adoption is measured by usage, not by number of dashboards.

Output:
Confident users actively using Sigma for real work.