AtScale > Case Studies > AtScale Helps Toyota Modernize Analytics

AtScale Helps Toyota Modernize Analytics

Customer Company Size

Large Corporate

Region

America

Country

United States

Product

AtScale
Amazon Redshift
Tableau
Power BI

Tech Stack

Cloud Data Management
Data Warehousing
Business Intelligence Tools

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Cost Savings
Productivity Improvements

Technology Category

Analytics & Modeling - Real Time Analytics
Infrastructure as a Service (IaaS) - Cloud Computing

Applicable Industries

Automotive

Applicable Functions

Business Operation
Discrete Manufacturing

Use Cases

Factory Operations Visibility & Intelligence
Predictive Maintenance

Services

Cloud Planning, Design & Implementation Services
Data Science Services

About The Customer

Toyota is an international automotive company that was looking to consolidate 35+ constituent North American companies into a single structure. This required a transformation of their data warehousing and analytics architecture across the business. The company's IT department was tasked with creating a semantic layer that supported high performance analytics that could be leveraged by all business analyst teams. Prior to the project’s implementation, analysts would often need to wait weeks for manual data engineering to take place, hindering their ability to provide actionable insights on key business questions. The company's backend infrastructure was partly to blame for the slow query response time, as data was siloed across thousands of individual data marts.

The Challenge

Toyota, an international automotive company, was faced with the challenge of consolidating 35+ constituent North American companies into a single structure. This required a transformation of their data warehousing and analytics architecture across the business. The IT department was tasked with creating a semantic layer that supported high performance analytics that could be leveraged by all business analyst teams. Prior to the project’s implementation, analysts would often need to wait weeks for manual data engineering to take place. This delay hindered their ability to provide actionable insights on key business questions. The company's backend infrastructure was partly to blame for the slow query response time, as data was siloed across thousands of individual data marts.

The Solution

With AtScale’s semantic layer underpinning their efforts, Toyota's IT department migrated data from a variety of legacy tools to an Amazon Redshift data warehouse. Once their data was in the warehouse, they could leverage AtScale to enable multidimensional modeling of their data, allowing them to connect their Redshift data warehouse on the back end with data visualization tools like Tableau and PowerBI on the front end. With AtScale, Toyota was able to scale its analytics program to support thousands of user using a range of BI tools. This eliminated the friction associated with moving thousands of SQL queries into a single data warehouse, enabling better user access to data. This also made improved governance possible, keeping data more secure as well as ensuring that various definitions were uniform and accurate across the company.

Operational Impact

With AtScale acting as a single semantic layer between the company’s data warehouse and its BI tools, business analysts are able to glean insight from their queries within seconds, with no need to wait for data engineering teams to get involved with every interaction.
AtScale has allowed the automotive company’s users of Tableau and PowerBI to utilize their preferred tool.
AtScale has enabled the company to consolidate disparate functions and democratize access to data. They are now more competitive than ever, with both technical and business users able to to make decisions based on dynamic analytics.

Quantitative Benefit

AtScale helped Toyota accelerate time to insight by 21x.
Delivered a 60% improvement on ROI through reducing infrastructure costs and accelerating analytical performance.
Doubled the number of users who have access to data.