Participants at a recent Utility Week roundtable about data analytics, produced in partnership with IT firm Cognizant, agreed that an “explosion of distributed generation” and the introduction of smart metering, among other things, are responsible for an ever-increasing amount of data being collected. While this is positive and creates opportunity, it also makes it more difficult to “join the dots” and extract actionable insight from data.
Delegates observed that an important part of overcoming this data blindness is having a strong idea of what the business wants to get out of its data. Without this, it is difficult to understand what kinds of data to focus on collecting, or how that data needs to be structured.
Companies also need to be prepared for the speed and volume at which data is generated by increasingly intelligent assets and to adapt the way the workforce responds to asset information accordingly. Whereas before, problems with assets used to “trickle in” as fast as a technician could inspect equipment, the introduction of technology such as Lidar means maintenance teams now receive an influx of data all at once.
Delving further into the impact of big data on the workforce, roundtable attendees explored the skills and cultures utilities will need to exploit the so-called internet of things and the sea of data around them. A growing challenge was identified in the pipeline of talent for data and information management. Furthermore, there was clearly some confusion about what an ideal information professional “looks like” and how a company can incentivise its workforce to improve the way data is collected and recorded.
As with so many debates concerning innovation and new technologies in the utilities industry, the discussion at this roundtable also touched on the role of regulation in enabling utilities to build new big data-enabled business models. Some speakers said the regulatory framework is “not ready” for big data and that the regulators seem uncertain how they can use data to support their duty to keep customer bills low.
This led to a new line of debate over the importance of customer data – as opposed to asset data – in delivering new utility business models and services. It was agreed that this is a vital area, but one that is bound up with issues around trust and consumer engagement.
Utilities need to think carefully about how data can lead to service improvements and clearly communicate these potential benefits in order to convince customers to share valuable information about themselves and their lifestyles.
Views from the table
Gary Ashby, data services manager, Yorkshire Water
“We’ve got to open the datasets up, we’ve got to share the data that we as an industry are using increasingly with things like smart cities and integration between utilities – that’s more and more important.”
David Salisbury, head of data and business change, National Grid
“We know analytics is a game changer, but how do we value the benefit of having analytics? It’s a really hard one because benefits are generally measured as tangible and cashable i.e. what is the direct saving as a result of having this new capability? With analytics, one insight may save you millions, another may show your assets are in worse condition that you thought and require you to do more work. Another still may just prove you made the right decision. It’s therefore quite hard to value it and we need to build up some case studies to help us with that.”
Robert Murray, head of asset intelligence, Scottish Water
“For me data isn’t that valuable until it has been analysed and converted into patterns and models that provide decision-makers with more meaningful and useful information. Analytics converts data into stories. These stories have the power to excite and change the beliefs of decision-makers. Businesses that do so effectively have driven massive improvements in service and efficiency. This is where the value of data is best realised.”
Steve Kaye, head of innovation, Anglian Water
“In the water sector, I think we’ve got an issue with ageing assets, so I think we’re moving towards a cliff edge.”
Richard White, assistant vice president, Cognizant Business Consulting
“Data degrades when people interact with it. Companies should look at how frequently datasets are changed – for example, by people updating them, or by new data being added to the set. That can give you an indication of whether the confidence level you can apply to that data is degrading as well.”
Stewart Reid, future networks and policy manager, SSE Power Distribution
“One of the big concepts in the utility industry is the criticality of assets and the criticality of data. For example, the position of a switch on the electrical network is critical. You can be very confident that it is accurate, and if it’s not accurate, you know it’s not accurate. In our industry there’s a clear scale of criticality in terms of data.”
Five points to take away
1. Collaboration
Data allows assets to be collected together, which brings with it both benefits and challenges.
2. Predictability
Data allows companies to predict faults on the system before they create problems.
3. Complexity
An influx of distributed generation and the introduction of smart metering has caused data to become more complex.
4. Innovation
Innovation is driven by goals. Innovation must be used to improve data collection, finding ways of joining datasets together and managing data in real time.
5. Regulation
The regulator may not be ready for big data. The industry must keep up with data, and the regulator has a role to play in facilitating this.