Make Data Analysis as Easy as Chatting with Amazon Quick Suite

In the wave of digital transformation, enterprises accumulate massive business data across many databases, but traditional analysis often requires skilled users to write SQL or build complex BI reports, limiting rapid insight for business personnel.

Amazon Quick Suite, an AI‑driven digital workspace from AWS, merges Amazon QuickSight’s BI capabilities with an AI Agent that understands everyday language. Users can ask questions such as “Which product had the highest sales last quarter?” or “Which region’s customers are growing fastest?” and receive accurate data insights instantly.

The platform’s core advantages are:

Unified data access : Seamlessly connects to on‑premise and cloud sources including Amazon RDS, Redshift, MySQL, PostgreSQL, Oracle, and SaaS apps like Salesforce, ServiceNow, and Jira.

AI‑driven intelligent analysis : The AI Agent simplifies complex tasks, enabling users of any skill level to make decisions quickly.

Collaborative Spaces : The Spaces feature aggregates dashboards, Topics, files, knowledge bases, and application actions into a shared knowledge center.

Implementation Steps

Step 1 – Connect Databases

Prerequisites: the database instance must allow public access (or VPC), security‑group rules must permit Amazon Quick Suite IP ranges, and the user must have read permissions. In the Quick Suite console, create a data source, select the source type (e.g., MySQL, PostgreSQL, Redshift), and configure connection name, host, port, database name, username, and password. Test the connection and save.

Step 2 – Create a Dataset

After the connection succeeds, choose a table directly or write custom SQL. The system can import data into the SPICE in‑memory engine for fast queries or use Direct Query for real‑time access. Optional transformations include renaming fields, adding calculated fields (e.g., profit = (revenue‑cost)/revenue), setting formats, and defining hierarchies.

Step 3 – Build a Visual Dashboard

Create a new Analysis, select fields and aggregation methods, and add visualizations such as bar charts, line charts, pie charts, heatmaps, tables, or KPI cards. Configure filters, parameters, and filter scopes (chart‑level or dashboard‑level) to refine the view.

Step 4 – Create Topics for Natural‑Language Q&A

Topics define business meanings, synonyms (e.g., “revenue”, “sales”), field relationships, common question patterns, and default aggregation rules. By carefully designing Topics, the AI Agent can accurately interpret user questions and return correct answers.

Step 5 – Assemble a Space

In Spaces, add the created dashboards, Topics, files (CSV, PDF, etc.), knowledge bases, and actions (e.g., approval workflows). This creates a unified, customizable knowledge hub for the team.

Step 6 – Use Natural‑Language Data Q&A

Users can interact with the built‑in Chat Agent or a custom agent to ask questions. The agent retrieves answers from the Space’s Topics and dashboards, supports multi‑turn dialogue, and provides an “View explanation” link to trace the answer generation process.

Benefits include lowering the barrier to data analysis, accelerating decision cycles, fostering data collaboration, and ensuring security through VPC private connections and fine‑grained permission controls.