Winning Data-Centric Strategies
In Detail: 2020
- Data Foundation
- Constant Adaptations
- In-the-moment Decisions
Laying a Foundation for Long Term Success
Formula One™ racing is probably the most competitive, technology-intensive sport in the world. To achieve continuous success and keep competitors at bay, teams must expertly wrangle the volume, variety, and velocity of their data and turn it into a competitive advantage that fuels innovation.
Fast Decision-making Race after Race
Data analytics supports the game plan and the minute-by-minute decisions made by the garage, drivers, and other team stakeholders. With every new circuit, unrelenting competitors, and changing weather and track conditions, data analytics and decision-making are crucial.
In Detail: 2019
- Optimal Car Setup
- Reliability Testing
- Digital Twins
Expecting the Unexpected
Data is central to Formula One™. Teams sift through billions of parameter combinations to find the fastest possible setup for that track, that day, that car, that driver. With so many variables and changing conditions, it’s impossible to fully prepare. But here's how Mercedes-AMG Petronas Formula One Team expects the unexpected.
Continuous Innovation & Collaboration
Here’s how the team uses data to bring insight and build a superior understanding leading to operational excellence and competitive advantage. This story describes its use of aerodynamics testing in a wind tunnel, and dynamometer and hydraulic testing to assess and improve the reliability of components.
Digital Twins Throw Down Big Gains
Factors contributing to Formula One™ car performance include design, aerodynamics, configuration, strategy—and the unsung hero—simulator analytics. F1™ teams, like Mercedes-AMG Petronas Formula One, use simulators that mimic the real track experience, a digital twin that maximizes limited on-track testing time.
Fueling Industries Beyond the Track
Internet of Things
The modern data-centric economy is exploding with connected devices. Businesses need to integrate them, aggregate the massive flow of disparate data into a cohesive view, analyze, then quickly act on the insights. Learn more about using the Internet of Things like the F1 team.
Digital twins are models of physical systems and offer tremendous value. One example is digitally monitoring physical assets and predicting when maintenance is needed to reduce downtime. Learn more about how the F1 team has turned digital twins into a competitive advantage.
TIBCO Hyperconverged Analytics allows an understanding of past and present trends so you can make informed decisions and achieve better outcomes. Learn more about how hyper-converged analytics delivers predictive insights at scale.
F1 Simulation Demonstration
Race Simulation Overview
Gameplay & Telemetry Simulation
Building Models: Best Variables for Predicting Lap-time Performance
Machine Learning Models for Predictive Analytics
This F1 e-racing demonstration shows tools and models that could be applied to any business that uses data to predict outcomes—especially relevant in a climate of constant change. With real-time data telemetry, predictive models, and live dashboards, you can make informed decisions to optimize product and service integrity.
A key feature of most racing simulations is the ability to generate telemetry data just like in the real world. In this simulation, 80 data messages per second are captured, processed, and displayed on a dashboard for analysis.
Telemetry remotely measures data points and automatically transmits the data to a receiver for monitoring and analysis. TIBCO Spotfire® dashboards are used to optimize and predict performance, discover insights, and explore captured data. Similar use cases could be achieved for a healthcare or insurance provider, a financial institution, or a supply chain. Enjoy a visual overview of the F1 2020 telemetry capture and analysis using TIBCO® Data Science and Spotfire® analytics.
With TIBCO Data Science software, you can discover the most important variables for building models that predict business outcomes. See how workflows can be built to wrangle data and identify feature importance, and note the software’s drag and drop functionality that reduces or eliminates the need for coding.
After identifying model variables using TIBCO Data Science software, the next step is building the model that will predict lap-time performance. We then use statistics to analyze and optimize performance. Spotfire analytics interprets the output, so we gain insight into driver performance. The model can predict significant events, enabling preparation that could mean the difference between winning and losing in racing or other business.
Business leaders are faced with vast amounts of data that, if handled appropriately, can provide insights for better decisions. This demonstration shows simulation analytics techniques used by Mercedes-AMG Petronas Formula One to optimize car performance. Real-time data, visual analytics, and predictive machine learning models provide insight into your business and give you the power to predict the future.