Technology Category
- Analytics & Modeling - Big Data Analytics
- Infrastructure as a Service (IaaS) - Cloud Databases
Applicable Industries
- Finance & Insurance
- Oil & Gas
Applicable Functions
- Maintenance
- Sales & Marketing
Use Cases
- Real-Time Location System (RTLS)
- Time Sensitive Networking
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
World Fuel Services (WFS) is a Fortune 150 company that markets, sells, and delivers fuel globally. The company has grown significantly over the past decade through more than a dozen acquisitions, each operating semi-independently with their own client lists. WFS deals with a volatile market, requiring real-time decision-making across the organization. This includes fuel brokers making large sales to capitalize on pricing dips, marketing teams launching or scaling campaigns in response to supply and demand changes, and optimizing delivery routes for drivers. With over $20 billion in annual revenue, data is a critical business driver for WFS, informing decisions that improve customer experience and operational efficiency.
The Challenge
World Fuel Services (WFS), a Fortune 150 company, faced significant challenges in managing and utilizing its data effectively. The company had grown through numerous acquisitions, each with its own client lists and data sources, making it difficult to gain a comprehensive view of customers across the entire organization. Additionally, the company's existing ETL pipelines pulled data in batches once a day into an on-premise Oracle database, which quickly became too large to run live queries effectively. The company also faced the challenge of managing data from dozens of ERP and billing information services across its subsidiaries, which was particularly critical during the global pandemic when the company needed to increase accounts receivable efforts to maintain revenue.
The Solution
WFS decided to undergo a major digital transformation project, moving 22 data centers to the cloud and replacing the on-premise Oracle database with a cloud-based data warehouse. After evaluating several options, the company chose Snowflake for its ease of use and immense elastic scalability. However, the legacy ETL solution required significant effort to write custom ETL jobs for each table in Snowflake. To address this, WFS implemented Fivetran, a SaaS platform that automates data ingestion in real time. Fivetran pulls in data from dozens of sources, including Salesforce, Box, SQL, and Postgres databases, into Snowflake. The platform also handles change management throughout the data pipeline, ensuring continuous data ingestion without additional configuration, even when source schemas change.
Operational Impact
Quantitative Benefit
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