The tens of billions of dollars of smart grid technology being deployed across the world is going to create a huge amount of data — and that means that building the tools to collect, understand and act on that data is going to be a billion-dollar-plus business in its own right.
In fact, the U.S. market for smart grid analytics is set to grow from about $ 322.5 million this year to $ 1.4 billion by 2020, according to GTM Research’s report released this week, The Soft Grid 2013-2020: Big Data & Utility Analytics for Smart Grid.
That’s a combined total of $ 8.2 billion over the next eight years expected to be spent on the hardware, software and technical and business expertise needed to bring the U.S. smart grid into the mainstream of IT and big data. Much of it is being forced on utilities struggling to manage the massive flood of data coming from smart meters and other grid devices being deployed in the millions across the country.
But there’s also plenty of money flowing from venture capital investors, corporate investors and government-industry partnerships into the next generation of big data intelligence for the smart grid. After all, one of the key goals of analytics is to discover insights that have previously been obscured by a lack of data, and then to devise ways to act on those insights to save energy and money — or even create new markets and business models.
So what are the top trends for smart grid data analytics in 2013? We’ve been covering the soft grid in depth for some time now, but GTM Research’s new report lays out some key findings:
1) Unstructured data is a major challenge, and tools like Hadoop and NoSQL architectures — and companies that know how to use them for the smart grid — will find willing customers. Examples of smart grid companies leveraging Hadoop include home energy analysis and efficiency startup Opower, utility-to-home-energy-management startup Tendril and the cloud-based, home-thermostat-optimizing technology of EcoFactor, to name a few. Companies like Versant and AutoGrid are building smart grid applications on top of their unstructured data analytics software platforms built out of the latest developments from the telecommunications and finance IT sectors, for instance.
In the meantime, IT giants like Oracle, IBM, EMC and Microsoft are promising unstructured data-ready platforms using Hadoop, though they’re not known for using open-source tools. That could mean acquisitions — Oracle just bought big-data startup DataRaker, and Teradata, which is working closely with partners like Southern California Edison and Itron on smart meter data management, bought big-data startup Aster Data in 2011. Expect that big players in business analytics like SAS, IBM and SAP, all of which have utility and energy customers, will be watching the field closely as well.
2) MDM is today’s challenge, but the grid at large is the future. GTM Research highlights the fact that meter data management (MDM) is and will remain the top big data priority for utilities next year. However, “In the next wave of innovation, transformer sensors, cap banks, voltage regulators, distributed PV solar panels, and other grid assets will gain the ability to communicate their status updates back to the utility,” the report states.
In other words, we’re talking about the Internet of Things, applied to everything that attaches to the utility side of the grid. Companies like General Electric, Siemens, ABB, Alstom and Schneider Electric are all in a position to leverage their existing base of “smart” devices in the field to capitalize on this opportunity, the report finds, though it also notes that most have been slow to do so.
But that’s starting to change. Last month, General Electric launched its “Industrial Internet” initiative meant to network and analyze data from devices ranging from jet engines to smart meters, and has rolled out a cloud-based smart grid deployment and analytics software-as-a-service package it’s hoping will gain interest with utilities, for example.
3) Integration will remain a core function. First, utilities have to get all of their existing data sets to fit together somehow. For the foreseeable future, that kind of complex integration task will remain the realm of masters in the field, like IBM, Infosys, Wipro, Capgemini and Accenture, the report found.
4) Companies we’ve never heard of may be leaders in short order. The report noted that a string of big-data startups, such as Cloudera, Hortonworks, Hadapt, Platfora, Karmasphere, Domo, and Sumo Logic, to name just a few, have received roughly a half-billion dollars in VC capital so far. Of course, smart grid may be far down the list of profitable industries that big-data startups are targeting — but as we’ve seen, developments in industries like telecommunications and mobile devices for consumer markets eventually filter their way down to the utility industry as well. We should expect the same for big data.
5) Trust for the cloud and software-as-a-solution smart grid offering is growing, but at utility (i.e., slow) speed. GTM Research takes the example of Salesforce.com here to show that, for certain utility business processes like customer information software (CIS) and enterprise IT, turning to SaaS is an increasingly viable options for utilities. But utilities are slow-moving and regulation-bound entities, and that makes change slow coming for many.
Indeed, according to GTM Research’s survey of utility executives, only 20 percent of respondents were implementing SaaS for enterprise IT or CIS, although another quarter or so of respondents said they were evaluating or planning to move certain apps to a SaaS provider in the next two years. Still, about 35 percent of respondents said they aren’t planning any SaaS implementations.
Turning over energy pricing or grid operations functions to a third-party platform provider is even less popular, however. Only one in ten respondents said they’re doing that now, and about 45 percent of respondents said they have no plans on that front. In the main, the next big wave of customers for SaaS utility platforms is expected to be municipal and cooperative utilities, which don’t have investor-owned utilities’ preference for capital (i.e., self-owned) projects over service-based models.
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