As we watch America’s greatest auto racing spectacle this Memorial Day weekend, what we won’t see is even bigger than the event itself, faster than the cars themselves, and more varied than the driver personalities. Of course I’m talking about the data. Racing teams now eat Big Data for breakfast, lunch and dinner. And for snacks in-between.
Outside, Indy cars and their cousin Formula-1 cars may be covered with dozens of sponsor logos, but inside they’re smattered with nearly 200 sensors constantly measuring the performance of the engine, clutch, gearbox, differential, fuel system, oil, steering, tires, drag reduction system (DRS), and dozens of other components, as well as the drivers’ health. These sensors spew about 1GB of telemetry per race to engineers pouring over them during the race and data scientists crunching them between races. According to McClaren, its computers run a thousand simulations during the race. After just a couple laps they can predict the performance of each subsystem with up to 90% accuracy. And since most of these subsystems can be tuned during the race, engineers pit crews and drivers can proactively make minute adjustments throughout the race as the car and conditions change.
Throughout the season, based on this accumulated data warehouse of information on car performance, driver performance, tracks and conditions, racing teams will make 50 or more mods per day. And for each season, new cars are built from the ground up using 95% new parts designed using this data.
Of course all these modifications need to adhere to fluctuating, fastidious and unforgiving racing league specifications. So analytics to ensure compliance is just as important.
Telemetry Tech on the Track
So what’s behind all this Big Data wizardry? Here’s a summary of some of what McLaren Electronics has built and baked into and around its team’s cars:
- Its latest data collection device, the TAG-320, features 4000MIPS of processing power, 512MB internal RAM, 8GB of logged data capacity, 13 buses, up to 100kHz analog sampling rate, internal accelerometer, 4000 logging channels, and a 1Gbps Ethernet link speed. Most of these characteristics are a 5-10x improvement over the previous 2008 TAG-310b model.
- The ATLAS (Advanced Telemetry & Linked Acquisition System) is a suite of analytics tools for real time storage, analysis, visualization and manipulation of data. It provides a customizable workbook, graphical timelines and other comparative visualization, heuristic car system checks, automated data alignment and sequencing, and a Microsoft SQL Server API. ATLAS offers analysis features called functions to combine parameters and develop sophisticated analytics, checks to automatically assess any car component, and markers to automatically or manually pinpoint the time when some anomaly happens.
- Accelerated data analytics is achieved using SAP’s HANA in-memory database
- Its Remote Data Server (RDS) enables live telemetry to be viewed simultaneously anywhere in the world by factory engineers, parts suppliers and data analysts
- Simulation capabilities using MATLAB (Simulink) can determine what might happen under different track or race situations, or if a driver behavior or car system were changed
- Special servers are used for collecting and integrating weather and other external data
Is Your Business on Track with Big Data?
All the excitement of auto racing aside, consider the key underlying components of what racing teams are doing to accelerate the performance of their cars and drivers and how these techniques can and should apply to your albeit relatively mundane business.
Use this checklist to see if your business will have a checkered future or get the checkered flag:
- Are you sufficiently monitoring key business processes, systems and personnel using available sensors and instrumentation?
- Are your data streams collected frequently enough for real-time process adjustments (i.e. complex event processing)?
- Do your business processes support real-time or near real-time inputs to adjust their operation or performance?
- Can you anticipate business process or system failures before they occur, or are you doing too much reactive maintenance?
- Do you centrally collect data about business function performance?
- Do you make use of advances in high-performance analytics such as in-memory databases, NoSQL databases, data warehouse appliances, etc.?
- Do you gather important external data (e.g. weather, economic) to supplement and integrate with your own data?
- Do you synchronize, align and integrate data that comes from different streams?
- Do you make your data available to key business partners, suppliers and customers to help them provide better products and services to you?
- Do you have a common, sophisticated analytics platform that includes the ability to establish new analytic functions, alerts, triggers, visualizations?
- Can you run simulations on business systems while they’re operating and also between events to adjust strategies?
- Does your architecture support multiple users around the world seeing real-time business performance simultaneously?
- Do you have teams of business experts, product/service experts and data scientists collaborating on making sense of the data?
- Do you modify your products or services as frequently as you could or should based on available data?
- Do you also use data you collect to develop new products or services as frequently as you could or should?
Racing teams are able to invest in advanced analytics because millions of dollars and euros are on the line from hundreds of sponsors. Hopefully your own big data project sponsors appreciate that big money is on the line for your business as well. Winning the race in your industry now probably depends on it.
Also follow Doug on Twitter @Doug_Laney
Read Complimentary Relevant Research
Predicts 2017: Artificial Intelligence
Artificial intelligence is changing the way in which organizations innovate and communicate their processes, products and services. Practical...
View Relevant Webinars
The Mobile Scenario: Taking Mobility to the Next Level
The definition of "mobile" in the post-app era will involve new interactions such as bots and conversations, new devices such as wearables...
Comments or opinions expressed on this blog are those of the individual contributors only, and do not necessarily represent the views of Gartner, Inc. or its management. Readers may copy and redistribute blog postings on other blogs, or otherwise for private, non-commercial or journalistic purposes, with attribution to Gartner. This content may not be used for any other purposes in any other formats or media. The content on this blog is provided on an "as-is" basis. Gartner shall not be liable for any damages whatsoever arising out of the content or use of this blog.