Transforming utilities with data

Utilities are changing. The pace of innovation has increased dramatically, especially in the past year. As consumer behavior, sustainability pressures, transportation electrification, distributed energy sources, and more rapidly emerge, the utility landscape is a brave new world and will continue to evolve.

Increased infrastructure for storage, generation, and transmission expansion is painfully needed, and it is imperative we get better at maintaining and managing what we already have. While more detailed usage data from smart meters can help forecast demand, even the use of additional information from sensors and actuators throughout generation, distribution and storage can be complex. Correlating all the information into meaningful insights is not as simple as data gathering and locating trends. Instead, highly precise predictions are necessary.

For instance, it doesn’t take a lot of analysis to know that more energy is used during halftime of the Super Bowl with current tools, but how to predict a sewer flooding or a sudden influx of EV drivers is much more subtle. Layering diverse data sources such as engineering data, asset design, weather, geological information, and consumer behavior can all impact key insight discoveries. Along with the right set of skills to extract meaning, a myriad of data sources is required.

The challenge for utilities is that they were not built around data, but assembling meaningful data is imperative to building proper data models but it’s also the most difficult part to attain. The primary issues faced by utilities are:

• insufficient data: Much infrastructure is underground making it almost impossible to collect data. And data is often not what you really want (the nearest address of a burst pipe, rather than its GPS coordinates). Imperfect data sources must be interrogated in some innovative way to develop the required resource.
• difficult to locate data: Much legacy data is found in blueprints, maps, and handwritten notes that must be located and translated before they can be fed into models.
• practical data: Sometimes even available data is not what is required such as measurements of physical systems like pipe corrosion and mechanical stresses, not consumer patterns. Engineers may worry data teams will ignore the real-world context and just seek correlations. Understanding different types of data and creating conversations between the two are important to remedy this shortfall.
• embracing new data sources: Complex techniques like machine learning must be explainable and understandable before they will be embraced. The priority of the functionality of the business can sometimes override this area where the data industry has often fallen short.
• data specialist shortage: Data science teams are usually small and overstretched and can be distant from the business itself. Teams may have expertise, but not the range of skills do everything needed. A combination of data management, data engineering, and modelling is necessary, but you also need people who understand engineering data and what it represents in the real world. Critically, teams must include people who can bridge the gap between subject matter experts and data experts and the wider business.

The challenges of drawing insight from complex data are significant, but these can gradually be overcome with the right approaches to data management, modelling, skills, and collaborations. And organizations who get it right are well rewarded.

The key is to start small. Identify viable use cases where you have good data and can quickly deliver valuable insights and focus there to begin. Use the process to identify gaps in data collection, data management, skills, and communication structures, and start to plug these. Gradually move onto slightly harder projects while building on learnings to advancing digital maturity.

Getting all this right will eventually allow utilities to become insight-driven and to gain mastery over its data. Each cycle of improvement builds a platform of certainty to consistently enable the launch of data projects, building of predictive models, and use of the insights to make more effective decisions on managing and upgrading infrastructure. This is a continuous journey, not a single project, but it all begins with one step.

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