Qlik > Case Studies > Driving transportation forward: Qlik Data Integration accelerates access to real-time data

Driving transportation forward: Qlik Data Integration accelerates access to real-time data

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Customer Company Size
Large Corporate
Region
  • America
Country
  • United States
  • Canada
  • Mexico
Product
  • Qlik Data Integration
  • Microsoft Azure Databricks
  • Azure Synapse
Tech Stack
  • Data Lake
  • SQL Server
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Productivity Improvements
  • Customer Satisfaction
Technology Category
  • Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
  • Transportation
Applicable Functions
  • Logistics & Transportation
  • Sales & Marketing
Use Cases
  • Supply Chain Visibility
  • Predictive Replenishment
Services
  • Data Science Services
  • System Integration
About The Customer
J.B. Hunt is a leading transportation and logistics company in North America. The company operates extensively throughout the United States, Canada, and Mexico. As a major player in the transportation industry, J.B. Hunt is constantly seeking ways to increase efficiency and improve customer responsiveness. To achieve these goals, the company relies heavily on real-time insights into its operations and assets. These insights are crucial for making informed decisions and staying competitive in the fast-paced transportation industry.
The Challenge
J.B. Hunt, one of the largest transportation and logistics companies in North America, was seeking to increase efficiency and customer responsiveness by gaining real-time insights into its operations and assets. However, the company faced the challenge of not impacting production systems while doing so. They had implemented a Microsoft Azure Databricks data lake but needed to accelerate the flow of analytics-ready data into the lake.
The Solution
To address the challenge, J.B. Hunt leveraged Qlik Data Integration. This solution enabled the company to deliver near real-time data from a variety of sources, including legacy mainframe systems and SQL server, directly into the Delta Lake. Furthermore, it automated data modeling and transformation for Azure Synapse data warehouse. This approach allowed the team to provide analytics-ready data quickly to multiple user groups without the need to deploy a large number of data engineers.
Operational Impact
  • The solution has enabled greater access to real-time data for multiple users.
  • Application engineers have experienced reduced latency to just minutes.
  • Data scientists have been able to power machine learning models to auto-generate competitive counterbids on available freight.
  • The solution has enabled the offering of dynamic shipment pricing based on carrier availability and current market conditions.

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