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How Autonomous Cars Will Benefit Enterprise-Scale Analytics and BI

What does a car that drives itself have to do with BI? The connection is stronger than you might think.

By Brendon Whateley, Principal solutions architect, Starview Inc.

There's a lot of buzz lately about self-driving cars. Google leads the pack with many thousands of computer-driven miles on public streets. Many others are busy researching different aspects of the challenge of automating a task that humans find quite easy, although the initial learning curve can seem steep. I've had the pleasure of riding in BMW's self-driving "training car" at the Laguna Seca racetrack in California -- a strange experience at first to be in a car hurtling along at over 100mph with nobody touching the controls.

Stanford University also has an autonomous car called Shelley that is being used to explore how machines can learn to drive like racecar drivers, extracting the maximum performance from the car and its systems. Other manufacturers are in on the trend, and several states have changed laws to make self-driving cars legal. All these cars operate based on a constant stream of real-time information gathered from a variety of sensors that need to be processed in real time to make decisions and take actions. Road conditions, traffic conditions, other cars on the road, traffic lights, and countless other variables need to be identified and tracked, and predictions need to be made so that the vehicles can operate seamlessly in the complex road environment -- a true testament to how far automation has progressed.

Real-time decision making doesn't stop on the Pacific Coast Highway. As anyone who works in business intelligence is aware, analytics on the increasingly large and rich streams of data that a modern business produces is a very important component of keeping a company competitive. Current trends show the processing of the data stream is accelerating, but business value is being lost with every passing second that opportunity is ignored.

Active analytics technology has the potential to change the way most industries operate -- including manufacturing, energy, financial services, and telecommunications, just to name a few. Unlocking the opportunity and realizing the business value in the data is the goal of business intelligence. The industry's products are becoming more mature, utilizing more sophisticated methods of analysis and processing data at increasing rates that are enabling ever more exciting outcomes, very much like the advances that have enabled practical driverless cars to be within technological reach.

Active analytics enables manufacturing facilities, energy smart grids, and mobile networks to handle enormous amounts of information and take instant action to keep the factory floor operating, re-allocate energy where it's needed most, and ensure customers are getting the most out of their mobile data plans -- all without the usual delay it takes for a human to be alerted to a problem, run analytical scenarios, and take corrective action. Just as an automated car stops when a pedestrian steps in front of it, active analytics requires no driver input to come to a decision. However, like an automated business system, the consequences of the analytics coming to the wrong conclusion can be serious.

One of the main challenges standing in the way of these industries embracing automation, in real time and on a massive scale, is that many decision makers -- CEOs, business development managers, etc. -- are still in the dark about how active analytics works and why it's worth the massive investment (and, in their eyes, risk).

Although leaders know they have challenges and suspect that the solutions may be unlocking the hidden knowledge in the data their businesses generate, it is not clear how to get to the solution. That's why developments such as automated cars could be the best thing to happen to business intelligence. Many of the technologies and challenges are closely related to those in real-time analytics and, in particular, follow the move towards event-based analytics systems that perform the analytics as the data flows into the system without waiting for the data to first be stored in a database.

Finally, after years of trying to describe the spectacular, complex, and highly customizable benefits of active analytics and how it's applied in the real world, automated cars have provided an analogy we can all understand. If Google can sell automated cars to state legislators in Florida, Nevada, and California -- and many more to come, no doubt -- then surely providers of automated business intelligence solutions can take active analytics into industries previously hesitant to embrace emerging technology.

The world will be a better place for it, too.

Brendon Whateley is principal solutions architect at Starview Inc., an active analytics platform. The Starview Active Analytics Platform enables active analytics for organizations needing to manage, monitor, and maximize the potential of large amounts of fast-moving, fluid data in real time, improving efficiency, identifying new revenue opportunities, streamlining operations, and mitigating waste. You can contact the author at

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