Oracle Says Utilities Botch Smart Meter Data Analysis
Utilities collect a lot of big data, but most don't use that data to spot theft, plan maintenance or improve service, Oracle study finds.
Utility companies should be exemplars of big data analysis, but thus far they're not living up to their potential. So finds a just-released study by Oracle that reveals that utility companies are gathering big data, but have yet to do deep analysis.
Oracle's second-annual study, "Utilities and Big Data: Accelerating the Drive to Value," finds that only 17% of utilities say they are "fully prepared" for the onslaught of high-volume smart-meter data, and that fewer than half use that information to provide alerts or drive customer service improvements. The survey, which is based on interviews with 151 senior-level executives at North American utilities, also finds that 62% of respondents said their firms have big data skills gaps.
"Human resources are clearly a big issue for utilities," said Guerry Waters, a VP of utility industry strategy at Oracle, in an interview with InformationWeek. "When we asked whether they have the data scientists they need to do deep analytics, they clearly said no."
There are many opportunities for utilities to put data to use. One category of keen interest is "revenue protection," which is a euphemism for theft detection. Using combinations of smart-meter data, network sensor data and operational voltage load SCADA (supervisory control and data acquisition) data, utilities can spot meter tampering and instances when consumption goes to zero when it clearly shouldn't.
Asset management is another big opportunity whereby utilities can detect patterns that show when items such as transformers are about to fail. This lets utilities avoid service interruptions by replacing equipment before it fails, and they can save money by not blindly replacing items based on the averages of scheduled maintenance cycles. Why replace if there's no sign of failure?
With more solar power sources coming online, Oracle says utilities have an opportunity to use weather data in combination with voltage information to better predict loads and supplemental power requirements.
"You can use Doppler radar to see cloud density and then forecast spatial photovoltaic output," explained Brad Williams, an Oracle VP of industry strategy. "That allows the utility to be more proactive in determining power requirements given expected fluctuations in output."
Getting to all those sophisticated analyses is obviously easier said than done, and one of Oracle's motivations for conducting the study is to promote its own, cloud-based analysis services. Late last year, Oracle acquired DataRaker, a cloud-based platform used by electric, gas and water utilities to analyze smart-meter and sensor data to optimize operations and improve customer service. Utilities may have mastered the challenge of collecting high-scale data, but the DataRaker service handles the bigger problem of developing analyses and filling the skills gap, according to Oracle.
"You don't have to put in servers and infrastructure, and you also get access to the data scientists that utilities typically do not have," said Waters. "It's also a collaborative environment, and our experience has been that customers using the service have formed a community and are sharing good ideas."
Oracle isn't alone in pursuing smart-meter analytics. IBM and SAP also have initiatives underway. IBM is working with Texas utility Oncor as part of its Smarter Utilities program, and SAP is using its Hana database technology at U.K.-based gas and electric utility Centrica.
Items from pills to power plants will soon generate billions of data points. How will this movement change your industry? Also in the new, all-digital Here Comes The Internet Of Things issue of InformationWeek: How IT can capitalize on the NSA's big data prowess. (Free registration required.)
The Agile ArchiveWhen it comes to managing data, donít look at backup and archiving systems as burdens and cost centers. A well-designed archive can enhance data protection and restores, ease search and e-discovery efforts, and save money by intelligently moving data from expensive primary storage systems.
2014 Analytics, BI, and Information Management SurveyITís tried for years to simplify data analytics and business intelligence efforts. Have visual analysis tools and Hadoop and NoSQL databases helped? Respondents to our 2014 InformationWeek Analytics, Business Intelligence, and Information Management Survey have a mixed outlook.