Microsoft Azure (Microsoft) > Case Studies > Dairy Giant Arla Foods Centralizes Data in Azure for Enhanced Productivity and Flexibility

Dairy Giant Arla Foods Centralizes Data in Azure for Enhanced Productivity and Flexibility

Technology Category

Application Infrastructure & Middleware - Data Visualization
Infrastructure as a Service (IaaS) - Cloud Computing

Applicable Industries

Consumer Goods
Electrical Grids

Applicable Functions

Maintenance
Product Research & Development

Use Cases

Predictive Waste Reduction
Time Sensitive Networking

Services

System Integration
Training

About The Customer

Arla Foods is a Denmark-based dairy company and the fifth-largest in the world. The company has a rich history dating back to the 1800s when the first cooperative dairy was established in Sweden. Arla is farmer-owned, and all earnings go back to the farm owners. The company places a large emphasis on the journey that its milk products take from cows to customers, recording and tracing every step of the way. Arla is committed to high standards of animal welfare, product quality, and safety, combining traditional craftsmanship and technology to ensure products remain as close to nature as possible. The company has over 10,000 employees and operates in the consumer goods industry.

The Challenge

Arla Foods, the fifth-largest dairy company in the world, faced significant challenges with its data management. The company had a complex, spaghetti-like structure of data systems, each with unique upkeep and management challenges. The cost of maintaining these systems was growing, and employees were spending more time managing the systems than gaining insights from the data within them. The company had thousands of different applications, but none were transferable, creating a highly inefficient set of siloed solutions. Furthermore, the company was using Microsoft Power BI as a visualization tool, but without proper data architecture, the tool quickly became overloaded and began to fail. The company recognized that its current process was not working and set out to find a solution that would both save it money and move its business forward.

The Solution

Arla Foods decided to centralize its data using Azure. The company used SAP BW on HANA for financial data and analysis, and moved all non-SAP data to the cloud. Azure provided every tool necessary for a complete solution, eliminating the need for any third-party tools. The company developed a data foundation on Azure that created one centralized location for data anywhere in the company to be ingested and consumed. The centralized data could then be used for a variety of business needs, from self-service reporting in Power BI to exploratory analytics and for powering data-driven applications. The company also used Azure Data Factory to extract data from other on-premises sources and stored it raw in Azure Data Lake Store Gen2. The data was then cleansed and transformed into a curated data layer, also stored in Azure Data Lake Store Gen2. For reporting, the curated data layer was loaded into specific data marts built per use case. The new architecture allowed Power BI to be used to its full extent, capacity could be managed, and data models could be significantly simplified to avoid performance issues.

Operational Impact

The new data architecture has transformed Arla Foods from a company with multiple siloed solutions to an enterprise leveraging data centralization to perform multiple processes across its business. The company now transforms data in Azure and makes it available through semantic models in Power BI, empowering business users to create data visualizations on curated data. This has resulted in significant cost and time savings, as well as reduced bottom-line costs by using the Azure cloud platform and Power BI to determine where to adjust costs and prices. The new architecture also makes it simpler to manage costs of delivery and maintenance and optimize business spending. Furthermore, the company now has flexibility in using its data, opening doors for endless innovation. Future plans include implementing a data catalog for understanding how to get information out of Azure, standardizing data across the data foundation, and upskilling non-IT users in using the data foundation correctly.

Quantitative Benefit

Reduced maintenance costs by centralizing data and eliminating the need for multiple separate systems.
Increased efficiency by creating a single, transferable application for all business processes.
Improved data management and reporting capabilities by integrating Power BI with Azure.