Dataiku > Case Studies > Anomaly Detection: How to Improve Core Product Accuracy and Efficiency with IoT

Anomaly Detection: How to Improve Core Product Accuracy and Efficiency with IoT

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Customer Company Size
Mid-size Company
Region
  • Europe
Country
  • France
Product
  • Coyote IoT devices
  • Coyote mobile applications
  • Dataiku Data Science Studio
Tech Stack
  • Vertica
  • Python
  • PostgreSQL
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Productivity Improvements
  • Customer Satisfaction
Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Analytics & Modeling - Real Time Analytics
Applicable Industries
  • Transportation
Applicable Functions
  • Logistics & Transportation
Use Cases
  • Real-Time Location System (RTLS)
  • Predictive Maintenance
Services
  • Data Science Services
About The Customer
Coyote is the European leader in real-time road information. The company was founded in 2005 and has 250 employees. It boasts 4.8 million users across Europe and generated a turnover of over €100 million in 2014. Coyote's products include IoT-based devices and mobile applications that enable users to warn other drivers of traffic hazards and conditions that they detect while driving. These products rely heavily on the accuracy of incoming data, particularly the driving speed limits within their embedded maps.
The Challenge
Coyote, the European leader of real-time road information, faced a significant challenge in maintaining the accuracy of speed limit data within their embedded maps. This data is crucial for the functioning of their IoT devices and mobile applications, which warn drivers of traffic hazards and conditions. The company needed an automated, algorithm-based solution that could correct speed limit data and leverage the high volume of incoming data from their IoT devices to generate actionable insights and predictions. This also required instilling a data-driven approach within the company, where decisions are based on real-world data rather than standard analytics reports.
The Solution
Coyote used Dataiku Data Science Studio to develop an algorithm that leverages vast amounts of IoT-derived data. The algorithm segments roads into sections and analyzes patterns in each section, enabling Coyote to build a predictive model that estimates the speed limit of the road section. This machine learning process facilitated the detection of speed limit anomalies and enabled Coyote to estimate the global quality and reliability of the displayed speed limit. The collaborative functionalities of Dataiku DSS were crucial in this process, enabling employees with differing skill-sets to work together and promoting a widespread understanding of data mining, visualization, and smart data issues within the company.
Operational Impact
  • Speed limit reliability increased by 9% on analyzed datasets.
  • Automation of the speed limit correction process was achieved.
  • A global data-driven spirit was instilled within the company.
  • Customer loyalty was increased.
Quantitative Benefit
  • Speed limit reliability increased by 9% on analyzed datasets.
  • Automation of the speed limit correction process was achieved.
  • Customer loyalty was increased.

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