The Future of Utilities: 2024 wasn't just another conference—it was an electric convergence of minds reshaping the energy landscape. When energy innovators descended on London this year, the air crackled with possibility. Among them was Gorilla's own Joris van Genechten, who stepped onto the stage to unravel the impacts of the data explosion on cloud infrastructure in energy retail.

Introduction: The new energy landscape

The energy industry is undergoing major changes that are making energy usage patterns more unpredictable. Here are the key numbers that highlight the scale of the change:

• 4x more solar and wind generation spread across many locations
• 18x more electric vehicle (EV) sales, changing energy consumption patterns
• 33% increase in energy efficiency

Much of the focus on changes in energy demand is on increases from population growth, data centres, and other areas. Still, variance and unpredictability will be just as affected by changes in supply and falls in demand. All of that extra renewable energy? It’s more decentralised, unpredictable, and sometimes consumer-generated. Intermittency will be huge. 

Electric vehicles will potentially be 60% of vehicles by 2030. This will upend traditional demand profiles with much more overnight demand from charging stations. The projected increase in efficiency will be essential for the grid to be able to adapt and for systems to be able to cope with these changes.

Challenges for energy retailers

Every part of the industry will need to make changes to meet the needs of modern consumers, and energy retail is no different. Gorilla have been working with energy retailers for years so we have seen these challenges first-hand. 

In a sense, the core issue is not a new one. Like all businesses, retailers need to make a profit, while still delivering products and services that satisfy their customers. The problem comes with a growing sophistication, both in the residential and industrial & commercial (I&C) segments. Customers expect personalised and flexible products that can take advantage of their access to EVs or renewable generation. However, the added complexity of these developments makes it far more difficult to maintain sustainable margins. With supply and demand shifting so quickly, forecasts need to be more accurate and pricing a lot more flexible to be able to be competitive and still profit-making.

At the same time, regulatory demands are only growing larger. The burdens of compliance and reporting will add even more complexity to everything that a retailer does.

Legacy IT systems and architectures can't keep up with the rapid change. Data is fragmented and spread across many sources and systems. If a sales team can’t access necessary customer data, forecasts, and industry data, they can’t produce accurate prices and won’t be able to attract customers. Operationalizing data insights into new products and services is the ideal, but there needs to be a transition to smarter, data-driven systems.

The central role of data

Retailers are clear on what needs to be done: a shift from simply accessing data to intelligently analysing and operationalising it. But it’s not simply a case of finding a new system and being done with it. The entire data pipeline will need attention. Sources of data are going to be more numerous and complex to deal with: smart meters will create a cascade of new data, weather forecasts are going to be more detailed as climate change leads to greater variance in conditions, and consumer behaviour is already changing.

At the other end, key transactional systems like billing and CRMs need to be decoupled from this mass of incoming data. While this extra data is useful, these tools are not meant to process data, and trying to force them will slow down everything, delaying innovation.

The ideal for energy retailers is to have a data layer that is separate from your application layer, socialising access while also providing the necessary processing power. It’s not a case of simply dumping everything into a data lake, as more often than not this will result in a data swamp. There has to be a plan for transformation.

3 Steps for data transformation:

• Distribute Data Beyond Source Domains: Ensure data is accessible and usable across departments.
• Enable Flexible Access: Develop governance and metadata strategies to enhance usability and accuracy.
• Operationalize Learnings: Scale insights from pilot projects to full-scale operations, integrating learnings into core business processes.

The future will be cloud-based, with a shared ecosystem that enables consistent and efficient decision making. Gorilla aims to be a key part of this puzzle, providing the data cloud that sits at the heart of a data management strategy and enables retailers to unleash the potential of their data. Gorilla’s applications are built for pricing and forecasting teams, but the true potential is even wider. For example, a unified data layer can enable synergy between departments like finance and forecasting, fostering alignment in ways businesses didn’t even expect.

Final word: Driving change

There is an urgency to transition to robust data infrastructures and processes to meet the demands of a rapidly evolving energy landscape. Retail teams are driving an old Ford Fiesta while the company wants them to drive on the highway at 300 miles an hour. Current systems and the architecture that's in place are equivalent to a used car with too many years and too many miles on the clock. With changes forcing cars to put their foot to the floor, accidents are bound to happen.