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Global Agriculture, Chemical, and Energy Leader Transforms Their Operational Data and Analytics for the Cloud
Technology Category
- Analytics & Modeling - Real Time Analytics
- Infrastructure as a Service (IaaS) - Cloud Computing
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Agriculture
- Chemicals
Applicable Functions
- Discrete Manufacturing
- Quality Assurance
Use Cases
- Inventory Management
- Predictive Maintenance
- Supply Chain Visibility
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
The Challenge
The Company’s operational data was siloed and difficult to access. Users wanted to leverage modern BI tools such as Tableau and Excel, and were being forced to extract data from databases and work with local copies. This “pump and dump” strategy of extracting data and working locally meant analytics were scattered across employees’ desktops, data governance and accuracy problems were endemic and the Company struggled to maintain one single version of the truth for their analytics. The Company developed strict criteria for their intended data solution: Ease of Use, Tool Agnostic, Disruption Free, and Single Source of Truth.
About The Customer
The Company and its affiliates are a global provider of value-added solutions for the agriculture, energy and chemical markets. The Company produces the vital ingredients used in food and energy creation. The Company’s operational data was siloed and difficult to access. Users wanted to leverage modern BI tools such as Tableau and Excel, and were being forced to extract data from databases and work with local copies. This “pump and dump” strategy of extracting data and working locally meant analytics were scattered across employees’ desktops, data governance and accuracy problems were endemic and the Company struggled to maintain one single version of the truth for their analytics.
The Solution
The Company selected AtScale for its ability to virtualize data across a hybrid cloud architecture enabling consistent and accurate query results across business intelligence (BI) tools and data platforms, acceleration of query performance, abstraction of data locations unlocking data agility and mobility, and a unified view of enterprise data. The implementation began with setting up instances of Amazon Redshift on Amazon Web Services. The Company seamlessly implemented data models they had been using with prior solutions. Within the proof of concept, the Company created cases to test building and consuming models with more than one fact table and mixed granularity across fact tables. The models performed to the Company’s satisfaction; for BI compatibility, they tested PowerBI, Tableau, and Excel to confirm queries would yield the same results, which they did.
Operational Impact
Quantitative Benefit
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