Navigant Research Blog

Distributed Energy’s Big Data Moment

— April 9, 2014

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.

 

Cleantech in the Era of Big Data

— April 1, 2014

The concept of big data – the notion that we are overwhelmed by a flood of digital information like nothing we’ve seen before – holds both promise and peril.  The allure is centered on the benefits that big data will bring, in areas from medicine to traffic to agriculture.  These benefits will translate into profits for companies that manage, transmit, and store all that data.

Then there’s the other side: that big data will lead to privacy intrusions, lack of freedom, and, from a very practical standpoint, yet another headache for executives and IT managers.  We have covered this topic in the past (see a great description of how automated demand response firms are focusing on data analytics or click here to read more about framing the problem for building operators) and our recent webinar, Innovations in Smart Building Data Analytics, also presented some excellent examples of how industry leaders are using data analytics for their customers.

The Three Vs

Many definitions of big data are available, but the most compelling framework was created by Doug Laney in a 2001 research report.  This description focuses on three prime elements: volume, velocity, and variety.  Volume refers to the bigness of the data – there are more sensors and signals than ever before, pumping out data on everything from location to temperature to transactions.  Velocity addresses the speed that the data is being created, from subsecond phasor measurement unit (PMU) data describing the power quality on the grid to the rate at which Facebook is gathering our likes.  (It should be noted that one overlooked aspect of velocity is not just speed, but also direction.  Data is streaming not just from our devices, but also to servers, corporate analytics processors, and back to customers, all over the world.)  Lastly, there is variety, which is the real game-changer.  Data has never been unitary, and the diversity of data forms, standards, protocols, and utilities is growing by the day.  While often presented as separate concepts, these three elements are intrinsically linked.  I’d like to present the three Vs as a nested hierarchy (see below).

The 3 Elements of Big Data

 

(Source: Navigant Research)

Data volume gets most of the attention (hence the name big data, not fast data or diverse data) and velocity gets the communication and IT folks excited.  But it’s the variety of the data, and the variety of the velocity and the variety of the volume, that makes the big data interesting.  It’s not just that data is big or fast; it’s the diversity of speeds and directions that data travels to its many users.

Big Data, Big Challenges

For example, utilities used to report monthly electricity usage; now customers can see how much power they use every 15 minutes – that’s three orders of magnitude difference!  In addition, utility data is now being served to customers, local grid operators, energy efficiency firms, and facility managers.  Lastly, it is the complexity of the variety (the variety of the variety) that creates challenges, as well.  For example, in the developing world, buildings are at many different levels of IT sophistication and electrical grids have to integrate old equipment and management processes along with new state-of-the-art high-tech factories that need highly reliable power.

So how is big data actually affecting cleantech markets and technologies?  Going forward, in our research and our blogs, we will touch on how big data is changing cities and how it’s being integrated into regular business practices.  We will explore how traditional firms are coming up to speed, while startups are using it to leapfrog their competition.  We’ll  also examine how big data is providing new opportunities and challenges to the cleantech markets and how those markets are responding.

 

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