Data analytics for utilities in Australia: What, why and how

In the Australian energy utilities sector, you've likely heard that some of your competitors are investing in the science of data - putting great stake in the internet of things (IoT) and business intelligence tools to gain detailed customer insights and get a leg up on the competition. Many see big data analytics as the future of the industry, but what does it mean for utilities in Australia, and how can they use it?

What could data analytics mean for Australian utilities?

Companies around the world are gathering a host of information - sensor and network data, customer attitudes and demographics, billing histories, energy demand forecasts, and more - in an effort to create a more personalised, flexible service.

Customer satisfaction is the next major battleground for Australian utilities.

Customer satisfaction, according to EY, is the next major battleground for Australian utilities. The company's Digital Australia report suggests that Australia is lagging behind the rest of the world when it comes to innovation and customer happiness.

But yet, the solutions are already close at hand. EY goes on to recommend that local utilities adopt analytics in order to provide a "deeper, more intuitive service."

4.    Apply appropriate scientific rigor

While extremely powerful, machine learning isn’t a magical, self-correcting analytical panacea. In practice, developing a machine learning application is an experimental and iterative process. This is true even when using well-known algorithms. In every case, the algorithm must be trained on and tuned for the business context and data at hand.

Whether you’re a machine learning beginner or an experienced practitioner, a healthy dose of skepticism and solid statistical reasoning, scientific and data analysis is critical.

5.    Make it just complicated enough

Generally speaking, more data wins. But do more features (aka attributes or data points) also equate to better outcomes? Not always. The catch is that larger data sets beget larger variations in data and a larger potential for predictive error.

In many cases, weaker models with more data do better than more complicated models with less – even if the data is dirtier. This is the basis of ensemble modeling techniques that use the “power of the collective” to predict results.

Deploying machine learning artfully is a balancing act. One in which the incremental predictive value of complexity must be weighed against interpretability, ease of use and applicability.

6.    Actively engage business users in validation

Data scientists must apply due diligence as the model is created and tuned. To do so, they must effectively communicate and collaborate with data and business domain experts to validate and vet the model.

This is critical to ensure the team has, among other things:

• Validated that all the options have been considered.
• Accounted for potential bias.
• Identified the impact and implications of putting found insight into action.

Examples of how utilities are using big data analytics

Let's examine two case studies of utilities companies in Australia and around the world utilising big data analytics to improve their service.

Forecasting future energy demand in an increasingly renewable
energy world isn't exactly easy.