AtScale Case Studies Analytics Modernization at Tyson Foods

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	Analytics Modernization at Tyson Foods AtScale

Analytics Modernization at Tyson Foods

AtScale

Technology Category	Analytics & Modeling - Big Data Analytics Analytics & Modeling - Real Time Analytics Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries	Food & Beverage Retail
Applicable Functions	Business Operation Discrete Manufacturing
Use Cases	Manufacturing System Automation Predictive Maintenance
Services	Cloud Planning, Design & Implementation Services Data Science Services
Challenge	Tyson Foods, a global food giant, aimed to deliver self-service data analytics to its 144,000 employees. However, the company faced a significant challenge due to its fragmented data spread across diverse platforms. The primary goal of their analytics modernization journey was to better connect their data. With massive amounts of disparate data moving across data lakes, it was a challenge to navigate this information effectively. The business was stuck in an analog experience and needed to pursue a more scalable and flexible data strategy to stay competitive and successful. Read More
About Customer	Tyson Foods is a global food giant with a vision of delivering self-service data analytics to its 144,000 employees. The company believes that doing so would enable them to make smarter decisions, respond nimbly to changes in the market and global supply chain, and ultimately democratize access to data for their entire company. However, the company's data was fragmented and spread across diverse platforms, making it difficult to unify and modernize its data architecture to support an organization-wide analytics strategy. Read More
Solution	Tyson Foods partnered with AtScale to ensure continuity of BI and reporting through the transition from Hadoop to Amazon RedShift and ultimately to Google BigQuery. AtScale's semantic layer was used to unify disparate data into a governed data model that was analysis-ready. This saved the business time and reduced errors and conflicting analyses, empowering business analysts to use trusted building blocks of data. This formed the cornerstone of self-service analytics at the company and led to more empowered and data-driven decision-making. The ability to abstract the model that data consumers work with from the underlying raw data sources also supports infrastructure agility. With AtScale in place, it no longer matters whether data lives in Hadoop, Amazon RedShift or Google BigQuery. This has enabled cloud migration without disruption to end users. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Analytics & Modeling - Real Time Analytics

Infrastructure as a Service (IaaS) - Cloud Computing

Applicable Industries

Food & Beverage

Retail

Applicable Functions

Business Operation

Discrete Manufacturing

Use Cases

Manufacturing System Automation

Predictive Maintenance

Services

Cloud Planning, Design & Implementation Services

Data Science Services

Challenge

Tyson Foods, a global food giant, aimed to deliver self-service data analytics to its 144,000 employees. However, the company faced a significant challenge due to its fragmented data spread across diverse platforms. The primary goal of their analytics modernization journey was to better connect their data. With massive amounts of disparate data moving across data lakes, it was a challenge to navigate this information effectively. The business was stuck in an analog experience and needed to pursue a more scalable and flexible data strategy to stay competitive and successful.

About Customer

Tyson Foods is a global food giant with a vision of delivering self-service data analytics to its 144,000 employees. The company believes that doing so would enable them to make smarter decisions, respond nimbly to changes in the market and global supply chain, and ultimately democratize access to data for their entire company. However, the company's data was fragmented and spread across diverse platforms, making it difficult to unify and modernize its data architecture to support an organization-wide analytics strategy.

Solution

Tyson Foods partnered with AtScale to ensure continuity of BI and reporting through the transition from Hadoop to Amazon RedShift and ultimately to Google BigQuery. AtScale's semantic layer was used to unify disparate data into a governed data model that was analysis-ready. This saved the business time and reduced errors and conflicting analyses, empowering business analysts to use trusted building blocks of data. This formed the cornerstone of self-service analytics at the company and led to more empowered and data-driven decision-making. The ability to abstract the model that data consumers work with from the underlying raw data sources also supports infrastructure agility. With AtScale in place, it no longer matters whether data lives in Hadoop, Amazon RedShift or Google BigQuery. This has enabled cloud migration without disruption to end users.

Impact #1	AtScale supported Tyson in their analytics modernization journey. They are now able to access data from any source, centralize it, and enforce analytics governance policies using AtScale’s semantic layer.
Impact #2	The ability to manage data models, calculations, dimension definitions, access control, and governance in a single location instead of in individual data stores or in individual BI tools is far more efficient. It also reduces security risks and misinterpretation of data.
Impact #3	With AtScale, data consumers can easily confirm where the data came from and that it will match what other teams are reporting. Tyson was able to get more out of AtScale by taking the opportunity to put controls in place for ingestion into the lake. This enables them to ensure data is categorized and classified properly and reduce chaos.

Impact #1

AtScale supported Tyson in their analytics modernization journey. They are now able to access data from any source, centralize it, and enforce analytics governance policies using AtScale’s semantic layer.

Impact #2

The ability to manage data models, calculations, dimension definitions, access control, and governance in a single location instead of in individual data stores or in individual BI tools is far more efficient. It also reduces security risks and misinterpretation of data.

Impact #3

With AtScale, data consumers can easily confirm where the data came from and that it will match what other teams are reporting. Tyson was able to get more out of AtScale by taking the opportunity to put controls in place for ingestion into the lake. This enables them to ensure data is categorized and classified properly and reduce chaos.

Benefit #1	Tyson’s analysis “building blocks” delivered through AtScale enable a more composable approach to analytics, allowing analyst teams to test new ideas in hours instead of days.
Benefit #2	With AtScale, Tyson was able to achieve world-class analytics, moving well beyond their legacy analog approach. They are now equipped to leverage the power of modern data and support better decision-making at scale.

Benefit #1

Tyson’s analysis “building blocks” delivered through AtScale enable a more composable approach to analytics, allowing analyst teams to test new ideas in hours instead of days.

Benefit #2

With AtScale, Tyson was able to achieve world-class analytics, moving well beyond their legacy analog approach. They are now equipped to leverage the power of modern data and support better decision-making at scale.

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Overview

Analytics Modernization at Tyson Foods

Operational Impact

Quantitative Benefit