As my colleague Noah Goldstein explained in a recent blog, the arrival of big data presents a multitude of challenges and opportunities across the cleantech landscape. Within the context of distributed energy resources (DER), among other things, big data is unlocking huge revenue opportunities around operations and maintenance (O&M) services.
As illustrated by large multinational equipment manufacturers like GE and Caterpillar, big data represents not only a potential key revenue source, but also an important brand differentiator within an increasingly crowded manufacturing marketplace. Experience shows, however, that capitalizing on this opportunity requires much more than integrating sensors into otherwise dumb machinery on the factory floor.
The recent tragedy of Malaysia Airlines Flight 370 brought international focus to the concept of satellite pings whereby aircraft send maintenance alerts known as ACARS messages. These types of alerts highlight the degree to which O&M communication systems are already in place in modern machinery. But Malaysia Airlines reportedly did not subscribe to the level of service that would enable the transmission of key data to Boeing and Rolls Royce in this instance. Although data may be produced via a complex network of onboard sensors, it is not always collected in the first place.
The collection and utilization of big data is not necessarily as simple as subscribing to a service, however. Today, the sheer volume of data produced by industrial machinery is among the main challenges facing manufacturers of DER equipment.
A Different Animal
Bill Ruh, vice president and corporate officer of GE Global Software Center, which helped lead GE into the big data age in 2013, describes the Internet of sensors as a very different animal than the Internet used by humans. While “the Internet is optimized for transactions,” he explains, “in machine-to-machine communications there is a greater need for real time and much larger datasets.” The amount of data generated by sensor networks on heavy equipment is astounding. A day’s worth of real-time feeds on Twitter amounts to 80 GB. According to Ruh, “One sensor on a blade of a gas turbine engine generates 520 GB per day, and you have 20 of them.”
Despite volume-related challenges, this opportunity proved too lucrative for GE to pass up. Estimating that industrial data will grow at 2 times the rate of any other big data segment within the next 10 years, the company launched a cloud-based data analytics platform in 2013 to benefit major global industries, including energy production and transmission.
Similarly, Caterpillar is one of the latest industrial equipment manufacturers to recognize the value of streaming a torrent of real-time information about the health of products in order to generate new revenue. Already integrating diagnostic technologies into its nearly 3.5 million pieces of equipment in the field, the company launched an initiative across its extensive dealer network aimed at leveraging big data to drive additional sales and service opportunities. Currently, the company’s aftermarket business accounts for 25% of its total annual revenue. As Caterpillar and other companies manufacturing energy technologies have realized, a healthy pipeline of aftermarket sales and service opportunities is of vital importance to market competitiveness in an increasingly competitive manufacturing landscape.
With distributed power capacity expected to increase by 142 GW according to a white paper published by GE in February, the addressable market for aftermarket DER data is rapidly expanding. Despite these opportunities, data analytics still represents a mostly untapped opportunity for manufacturers of emerging DER technologies. Allowing manufacturers and installers of everything from solar panels to biogas-fueled generator sets (gensets) to closely monitor hardware performance, better utilization of data has the potential to not only drive aftermarket service offerings, but also accelerate return on investment (ROI) through better optimization and greater efficiency. And this is a highly valuable differentiator for a class of technologies still scrambling for broad grid parity.