Guest author Scott Amyx is founder and CEO of Amyx+McKinsey, a wearables and IoT (Internet of Things) strategy and execution agency.

We’ve moved into an era of Big Data, with petabytes of information available mere keystrokes away from your fingertips. The situation will only deepen as the IoT expands. An estimated 50-75 billion connected devices will flood the market over the next decade, and ignoring these massive quantities of data is a losing proposition.

Enterprises can use Big Data to their advantage with machine learning. If visions of the HAL 9000 from Arthur C. Clark’s 2001 or 2010 are springing to mind, rest easy: Modern machine learning is devoted to deriving value from data, not jamming the airlocks.

For example, IBM has targeted developers by going open source with part of its library of APIs for its Watson, but it’s not the only ML system out there. Other machine learning systems include Google’s Deepmind (part of the Google Brain project), Stanford’s Deepdive (partnered with DARPA), Microsoft’s Azure platform, and MIT’s ConceptNet5 (derived from open source code).

Let’s take a look at how tech innovators can use Big Data and machine learning to their advantage.

Putting Efficiency To Work

Efficiency is fundamental to creating viable solutions. It applies to all aspects of product or service creation and delivery, from the prototyping of a device to the type of marketing used to get that widget off the shelf.

As data gets pulled from sensors, in-house systems, and external partners, machine learning can help quickly deliver new insights, putting that information to the best possible use to streamline current processes. This efficiency can be utilized on an individual level or a company-wide scale.

When Attitude Sports owner David Haase participated in the grueling Race Across America, a 3,000-mile cycling competition in which he placed second, his crew monitored his biometric data and combined it with other information. Over nine days, they tracked things like wind speed, to help him determine the best times to rest or re-hydrate. Analytics created his efficiency: Haase was a day faster than the third-place cyclist.

Efficiency directly translates into cost savings and speed on a corporate level, too: Boeing is using analytics to explore novel approaches to data correlations, improving flight times and saving on fuel costs.

The Path To Innovation

Let’s face it: Without innovation, you wouldn’t be reading this. Constantly innovating can be challenging, though, and when moving forward with new ideas, it can be tough to understand where the greatest benefit lies.

Machine learning can support innovation through a variety of paths, such as determining weaknesses with current products, predictive analysis, or identifying previously concealed patterns.

DARPA is first for innovation, and has put data and machine learning to work in ways that most of us don’t know about (and won’t, because some projects are top secret). Credited for developing the precursor to the Internet (ARPANET), it is working on a machine learning system to expose and resolve software security holes. On the commercial side, Boeing’s ecoDemonstrator 787 plane relies heavily on data to respond to issues in real-time and takes an innovative approach to solving environmental issues.

Making Businesses Smarter With Data

New business models are the inevitable outgrowth of data utilization. How does your company deliver value to customers? How do you collect and use enterprise data?

Big Data can peg previously unrecognized patterns and connections, and deliver that value in real time: Front-line workers can quickly learn about how to deal with the customer standing in front of them. Customers gain value (and provide feedback) at the point of service. These insights can really shake up the way businesses operate.

Local Motors, for instance, is approaching auto manufacturing through 3-D printing. The company can print a fully operational vehicle in about 40 hours (complete with cup holders). With the right data in place, this approach would allow a small, efficient manufacturing plant to quickly serve customers’ needs with customized vehicles—a completely different way of manufacturing and selling cars.

Analytics and machine learning are challenging closely held beliefs about how to approach efficiency and innovation. They may even up-end traditional ways of operating, as enterprise adoption of analytics—enhanced with machine learning—grows. 

Lead photo courtesy of Shutterstock