Data analytics buying guide part 5: How to know if you got it right - measuring success
Elsewhere in this buying guide we've told you:
In the electric utility world, Big Data is a Big Deal. Attend just about any industry conference and youâ€™ll hear folks talking about the data deluge swamping utilities as smart grid technologies like smart meters and sensors and Phasor Measurement Units (PMUs) go live.
And here's why it's such a big deal: Utilities are starting to use advanced analytics software to turn that data into actionable business intelligence. If market forecasters have it right, the industry is just getting started down that path. Utility spend on data analytics â€“ less than half a billion dollars in North American in 2011 â€“ is expected to swell 29% a year to $2 billion in 2016, according to the Utility Analytics Institute. Worldwide, Pike Research expects cumulative spending on smart grid data analytics to total over $34 billion by 2020.
So if exploring data analytics options is on your agenda, this five-part buying guide â€“ created with valuable input from analytics experts at Origin, BRIDGE Energy Group, Space-Time Insight and Opower â€“ is designed to help you get started and know the right questions to ask.
Now it's time to look at how you'll know if your efforts are generating the results you hoped for.
Not everyone within your organization and beyond (customers and regulators, for instance) will apply the same yardstick to measure the success of your analytics progress. But that doesn't mean it isn't important to constantly monitor and measure.
Opower, the company that works with utilities on customer engagement, recommends a clearly defined measurement and verification approach. This approach follows the experimental design blueprint, using randomized test and control groups to measure efficiency savings and progress, says Opower's Carly Baker Llewellyn.
Another way to look at success metrics, says Ethan Cohen of BRIDGE Energy Group, would be to include:
Â· The number of analytical projects executed in addition to outcomes, revenue recovered, expenses and large capital cost avoided (due to more efficient equipment management)
Â· Client or customer surveys
Â· Efficiency gains in the number of employees required to complete a job
Â· The number of BI resources per total user count (low ratio is best)
"Usage analytics can help to measure the adoption of solutions, generate usage stats and identify strategic information," Cohen notes. "Once measured, utilities can begin to apply value to information."
But don't beat yourself up if you aren't there yet.
In its 2012 Utility Industry Survey, BRIDGE asked an audience of over 14,000 utility employees about their experiences in analytics, business intelligence and/or big data
Â· 53% of respondents admitted that their organization had immature BI/Analytics capabilities â€“ placing them on the side of knowing about the potential of BI but not necessarily having firstâ€hand experience.
Â· 29% indicated that they are planning major BI/Analytics projects in the coming two years
Â· 62% are planning minor BI/Analytics projects
And those findings suggest many are heeding the advice of Jeremy Oosthuizen of Origin mentioned earlier: "Think big, but start small."
If you have insights you'd like to share on successes your utility is having with analytics, please use the Talk Back comment form below.
From industry sourcesâ€¦
2012 Utility Industry Survey on BI, Analytics and Big Dataâ€“ BRIDGE Energy Group
California ISO: Bringing State-of-the-art to California's Gridâ€“ Space-Time Insight
An Introduction to Situational Intelligenceâ€“ Space-Time Insight
Data validation for smart grid analyticsâ€“ Utilicase
6 Big Data Liesâ€“ InformationWeek
From Smart Grid Newsâ€¦