Amazon Quick Suite Scenarios for Business Unit Metrics | Empowering Business Users with Cloud Cost Intelligence

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Cloud Cost Analysis Through the Lens of Business Performance

The Evolution of Cloud Cost Analysis

In the rapidly evolving landscape of cloud computing, organizations face an increasingly complex challenge: understanding the true business impact of their cloud investments. While traditional cloud cost management tools excel at providing granular spending visibility, they often fall short in connecting these costs to actual business outcomes. This disconnect has led to a critical gap in how organizations evaluate and optimize their cloud investments.

The Traditional Challenges

Organizations typically encounter several obstacles when attempting to align cloud expenditure with business performance:

1. Data Silos

   • Cloud cost data not tagged with business data.

   • Multiple disconnected reporting systems.

   • Manual data integration requirements.

2. Analysis Complexity

   • Time-consuming correlation processes.

   • Need for specialized expertise.

   • Delayed insights and decision-making.

3. Limited Business Context

   • Difficulty in mapping costs to business units.

   • Lack of real-time business performance correlation.

   • Incomplete view of cost-to-value ratio.

Amazon Quick Suite scenarios as a solution

Amazon Quick Suite scenarios represents a groundbreaking solution in the business intelligence landscape. At its core, this powerful platform seamlessly combines Cost Utilization Reports (CUR) with Enterprise Business Data, creating meaningful correlations through AWS resource tagging mechanisms. What sets this solution apart is its ability to transform complex data relationships into accessible insights through advanced Natural Language Processing (NLP) capabilities. Customers can now effortlessly explore their cloud spending patterns and understand their impact on business outcomes without requiring deep technical expertise.

The platform’s intelligent architecture enables organizations to:

· Track cloud expenditure against business metrics.

· Generate instant insights through natural language queries.

· Visualize complex cost-to-business performance relationships.

· Make data-driven decisions with confidence.

By leveraging Quick Suite scenarios, organizations can break down traditional data silos and establish a clear connection between cloud investments and business value. This integration not only streamlines cost analysis but also provides a comprehensive view of how cloud resources directly contribute to business success, enabling more strategic decision-making and resource allocation.

Architecture Diagram

Figure 1 – Application Architecture

This solution implements a comprehensive business intelligence solution using Amazon Quick Suite scenarios for analyzing business unit metrics.

1. The system ingests data from two primary sources: Cost Utilization Reports (CUR) in stored in Amazon S3 in Parquet format and custom Enterprise Business Data maintained in Amazon Aurora Postgres database.
2. The Amazon Aurora Postgres database can be replaced with any of the Relational Database Management System (RDBMS) based on customer’s choice.
3. The data processing layer utilizes AWS Glue to create data catalog of Parquet format CUR data stored in S3 bucket and enables Amazon Athena to query the data.
4. Amazon Athena serves as a serverless query engine, enabling SQL queries on the processed data.
5. Amazon Quick Suite provides the visualization layer with integrated natural language processing capabilities through Amazon Quick Suite scenarios.

Strategic Resource Tagging: The Foundation of Effective Cloud Cost Analysis

A robust AWS resource tagging strategy serves as the cornerstone of successful cloud cost management and business intelligence integration. This critical component acts as the bridge between technical infrastructure and business outcomes, enabling organizations to create meaningful connections between cloud spending and business performance metrics. By implementing a comprehensive tagging framework, organizations can transform raw cloud cost data into actionable business insights. Tags should follow standardized naming conventions and formats that align with business data. Organizations using abbreviated tags should implement a translation layer with database mapping tables to ensure accurate mapping in Quick Suite.

Sample application

Consider a comprehensive sales application that manages both online and in-store transactions, exemplifying the practical implementation of Quick Suite’s data integration capabilities. This system effectively combines enterprise data, which includes detailed monthly metrics for both online and in-store sales, with AWS resource utilization data through strategic tagging. The AWS infrastructure is organized using two primary tags: an application identifier that distinguishes between online and in-store sales platforms, and a business unit tag that categorizes all resources under 'Sales’. This streamlined tagging structure creates a clear relationship between cloud resources and business operations, enabling precise cost allocation and performance analysis. The integration of enterprise business data with Cost and Usage Report (CUR) data through these carefully structured tags allows organizations to gain comprehensive insights into how cloud resources directly support different sales channels and contribute to overall business performance.

Figure 2 – Sample data structure

CUR Data loading to Amazon Athena using AWS Glue

The customers who have enterprise support should have the CUR data loaded in Athena already. The customers who do not have CUR data in Athena can follow the steps below to load CUR data in Athena.

Upload the CUR Parquet data in one of the S3 buckets. Search for AWS Glue in AWS Console. Click on AWS Glue to launch.

Perform the steps below to create a Crawler to get CUR data from S3 bucket, and to create a database to query in Athena and to integrate with Quick Suite.

1. Click in the hamburger icon on the top left corner and click on Crawlers under Data Catalog.

2. Click on Create Crawler.

3. Provide a name for Crawler. Click on Next.

4. Click on Add a data source.

   

   Figure 3.1 - CUR Data Transformation using Amazon Glue and Amazon Athena

5. Leave the Data source as S3.

6. Location of S3 data: Select S3 bucket which contains CUR Parquet file. Leave default selections. Click on Add an S3 data source.

7. Pick the IAM role if already available. Otherwise click on Create new IAM role. Create an IAM role and save. Select the same IAM role in the Existing IAM role drop down.

8. Click on Create database. Provide a name for the database. Click on create database.

   
   Figure 3.2 - CUR Data Transformation using Amazon Glue and Amazon Athena

9. Select the created database in the Target database. Click Next.

10. Review the entries and click on Create crawler.

11. Select the Crawler and click on Run.

Figure 3.3 – CUR Data loading in Amazon Athena using Amazon Glue

Enterprise Data Loading from Relational Database

To establish meaningful correlations between cloud expenditure and business performance metrics, organizations must integrate their enterprise data into a robust database system. While customers have flexibility in choosing their preferred database solution, this implementation leverages Amazon Aurora PostgreSQL for its scalability, performance, and seamless integration capabilities with AWS services. By loading sales data and other business metrics into Amazon Aurora PostgreSQL, organizations can create a comprehensive data foundation that enables detailed analysis of cloud spending patterns in relation to actual business outcomes. This integration is crucial for generating meaningful insights through Quick Suite, as it allows for the correlation of cloud costs with specific business activities, performance indicators, and revenue metrics.

Datasets in Quick Suite

Enable access to data sources in Quick Suite

The data sources should be enabled in Quick Suite to access data from those data sources. Follow the steps below to enable required data sources.

1. Click on right top corner User icon.
2. Click on Manage Quick Suite.
3. Click on Permissions → AWS Resources from the left menu.
4. IAM Role: Use the Quick Suite Managed Role (default) or Use an existing role.
5. Select the services which will be data sources for Quick Suite.
6. For this example: Amazon RDS, Amazon S3, Amazon Athena, IAM are selected.
7. Click on Save.

Figure 4 – Enable access to data sources in Quick Suite

Establish VPC connection in Quick Suite to access Enterprise Data

1. Click on right top corner User icon.
2. Click on Manage Quick Suite.
3. Click on Security → Manage VPC Connections.
4. Click on Add VPC Connection.
5. Provide a name for the connection.
6. Select the VPC ID from the drop down.
7. Select the execution role.
8. Select the subnets which are applicable.
9. Click on Save.

Figure 5 – Establish VPC connection in Quick Suite to access Enterprise Data

Load datasets in Dashboards

Enterprise Business Data

1. Click on Quick Suite at left top corner.
2. Click on Datasets.
3. Click on Create Dataset → Create Dataset
4. Click on Create data source in the pop-up window
5. Click on Amazon RDS.
6. Provide a data source name.
7. Select the instance ID.
8. Provide the database ID.
9. Provide the database passcode.
10. Click on Validate connection button and make sure the connectivity is successful. A green tick mark appears and the button name changes to Validated if the connection is successful.
11. Click on Create data source.
12. Select the database and the table.
13. Select Import to SPICE for quicker analysis and click on Visualize.
14. Click on all the fields to load all the data on right side view. Click on Publish to publish in a dashboard.

Figure 6 – Load Enterprise Business Data in Quick Suite Dashboards

CUR Data

1. Click on Datasets.
2. Click on Create Dataset → Create Dataset
3. Click on Create data source in the pop-up window
4. Click on Amazon Athena.
5. In the New Amazon Athena data source screen, provide a data source name for your reference, and select the Athena workgroup.
6. Click on Validate connection button and make sure the connectivity is successful. A green tick mark appears and the button name changes to Validated if the connection is successful.
7. Click on Create data source.
8. Repeat the same steps in Enterprise Business Data section to load the data on the dashboard.
9. If all the CUR data are not needed, create a view with limited data using custom SQL query in Athena and load the view instead of the CUR table.

Figure 7 – Load CUR Data in Quick Suite Dashboards

Quick Suite scenarios to gain insights

The section below explains how to use Quick Suite scenarios to gain insights by loading Enterprise Business Data and CUR data from Quick Suite Dashboards.

1. Click on Quick Suite → Scenarios.
2. Click on Create Scenario.
3. Click on Data icon and click on Find Data.
4. Click on All Dashboards.
5. Click on the CUR Data. Select the checkbox on right top corner of the dashboard and click on Add.
6. Click on Sales Data. Select the checkbox on right top corner of the dashboard and click on Add.
7. Go to Data to Insights section. Provide a name/ short description for the Scenario and click on Start Analysis.

Figure 8 – Select data from Quick Suite Dashboards for insights

Insights using the data

1. Type your question and click on Submit to get business unit metrics on cloud spend.
2. Sample questions:
   1. What is the AWS cost to Sales ratio for the month of Jan 2025?
   2. Compare AWS cost to Sales ratio for each month and create a bar chart
   3. What is the RDS cost trend for online sales in 2025?
   4. Based on the AWS cost and sales trend, what will be the forecast for AWS cost if the sales increase to 10% next month?
   5. Compare AWS cost to sales ratio for online sales and in-stores sales
3. Amazon Quick Suite provides insights to cloud spend in alignment with your business data for the questions asked in Natural Language Processing (NLP) in Quick Suite scenarios

Figure 9 – Ask questions about cloud spend for your Business Unit

Conclusion

The implementation of Amazon Quick Suite scenarios marks a significant milestone in the evolution of business intelligence and cloud cost management, fundamentally transforming how organizations understand and optimize their cloud investments. By seamlessly bridging the traditional gap between technical metrics and business outcomes through sophisticated integration of Cost Utilization Reports (CUR) with enterprise business data, enhanced by strategic AWS resource tagging and natural language processing capabilities, this solution empowers organizations to make more informed, data-driven decisions.

Organizations can now generate insights into cloud spending patterns, optimize resource allocation based on actual business outcomes, and break down data silos through intuitive natural language queries.

About the authors

Durai Krishnan is a Senior Customer Solution Manager at Amazon Web Services (AWS) with 26 years of rich background in consulting and leadership. Durai specializes in guiding customers through their cloud journey, focusing on migration, modernization, and cost optimization strategies. His journey before AWS included a significant tenure at Tata Consultancy Services, where he spearheaded transformative initiatives in the insurance sector.

Ganesh Thiyagarajan is a Senior Solutions Architect at Amazon Web Services (AWS) with over 20 years of experience in software architecture, IT consulting and delivery. He helps ISVs transform and modernize their applications on AWS. He is also part of the AI/ML Technical field community, helping customers to build and scale Gen AI solutions.

Arun Chellappa Ganesan is a Senior Customer Advocacy Specialist - GenAI with Amazon Web Services. With a solid foundation in technology and a knack for strategic thinking, Arun thrives on driving customer success through accelerated GenAI adoption, business value realization, and organizational change management across GenAI and Agentic AI transformation initiatives

Siraj Gadne is a Customer Solutions Leader at Amazon Web Services. He is a true builder and is passionate about helping customers maximize the benefits of cloud adoption through migration, modernization, and transformation. Siraj has held several leadership positions in the consulting and corporate worlds over the course of his 25-year career. Prior to AWS, Siraj worked for The Coca-Cola Company, Merkle Inc., and Capgemini, serving customers in the cloud enablement and digital transformation domains