What’s left to say about data management and analytics?
In our opinion, while much has been said about analytics, the industry still has a great deal to do. In a 2017 utility industry survey from Accenture more than 90 percent of utilities executives interviewed said that it was important to buy or build advanced analytical and statistical tools and assure the availability of data scientists in the company. Yet, despite this enthusiasm, the same survey found that more than 60 percent saw the need for significant improvement with their data quality, as well as statistical analysis skills. With more than half of executives confirming the need for progress, executives continue to stall.
With our feet on the ground at global customer sites, we combine domain, data, business, and technology expertise with proven methods, models and frameworks to put utility data to work for a greener, stronger and more diverse energy system.
Analytics in the real world
We take an evolutionary approach to combining the best of traditional systems and new solutions.
Our Energy Insight services include controlling existing data through traditional means, such as process integration projects and data quality initiatives. Once these are mastered, utilities can enter new territory, integrating new sources of information they previously did not fully understand or dare to touch.Read the brochure
We bring together teams comprising data scientists, engineers and IT experts to uncover, interpret and act on correlations. There are three phases to the process:
- Generating the analytics results.
- Running the false positive analysis.
- Verifying the value of the analytics again.
The value verification stage is critical to delivering value with analytics. Analytics needs to be grounded in the real world, where the value of predictions is verified. In our experience, this understanding requires highly skilled practitioners from analytics and data science domains to collaborate with field experts.
Big data with little data
Utilities should keep in mind that they already have most—if not all—the data they need to improve decision making. This is one area where wealth prevails. Meanwhile, they shouldn’t lose themselves in their big data. What’s important is starting small and scaling, constantly reviewing results for patterns and correlations, with a view to narrowing in on what’s of interest. It’s no longer about data processing, but rather about understanding data.
Value-verified analytics in action
OMNETRIC partnered with KELAG, a leading energy service provider in Austria and its distribution grid operator, KNG, which serves around 300,000 customers via a grid network covering over 7,000 transformers, 18,000km of power lines and 65,000 poles. The partnership, which commenced in 2016, required a joint team to analyze grid data from KNG and combine it with publicly available environmental data. Using the data intelligence generated, OMNETRIC developed different outage prediction statistical models for KELAG and these statistical models are integral to the OMNETRIC Planning and Outage Intelligence application.