Transportation and logistics industry has been disrupted by the native digital competitors and the internet of things (IoT) while facing many operational challenges through increased traffic congestion, reduced capacity, and driver shortage. With these pressures, data-driven decision making via advanced technologies—such as automated fleet management, cloud-based analytics, location detection— became critical to compete effectively. Striim enables you to offer innovative services with agility, and run your operations with maximum efficiency by integrating high-velocity enterprise data and sensor data to your existing analytics solutions.
Using real-time, pre-processed data in your operational analytics solutions allows you to:
Striim enables you to work with a smart data architecture by filtering, aggregating, transforming, enriching, and if desired analyzing, sensor data at the edge. Striim captures real-time change data non-intrusively from enterprise databases and integrates with machine and sensor data for a comprehensive view of operations. It processes data-in-motion to accelerate time-to-insight.
Striim’s advanced features such as predictive analytics and ability to embed machine learning algorithms allow you to make accurate and timely decisions that maximize operational productivity while reducing costs. With built-in, real-time dashboards you can visualize streaming data and compare to historical data for fast insights.
This leading courier company in Europe with over 4000 vehicles and over 12,000 employees embarked on its cloud journey with the help of Striim. The company is moving its data warehousing and analytics solutions to the cloud and uses Striim to move real-time data from transactional systems running on Oracle databases to Google BigQuery to enable cloud-based analytics.
Google BigQuery serves as the operational data store supporting real-time reporting and ad-hoc queries. By using real-time data in its analytical environment, the company can provide fast operational reports with up-to-date data. The company plans to use real-time transactional data for fleet optimization and real-time shipment status notifications to customers.
Set up the operational data store (ODS) in the cloud by ensuring up-to-date transactional data is available in the cloud
Eliminated the performance impact of running ad-hoc queries on the production OLTP systems by offloading reporting to real-time ODS in the cloud
Gained the ability to optimize fleet management and improve customer service using real-time GPS data
Streaming data integration with intelligence supports transportation and logistics providers with a variety of use cases including: