The need to keep data clean and true in the energy sector

Keeping data clean and true is a continued challenge, especially for regions where the number of deregulated utility companies means that electronic data interchange can be slow and inconsistent, writes David Sheldrake, Global SVP of Sales360 for POWWR.

There is more data than ever before. In fact, the total amount of data created, captured, copied, and consumed globally reached an incredible 149 zettabytes in 2024. The growth of the big mountain is not expected to slow down, either, with it expected to reach almost 400 zettabytes by 2028.

Whilst more data leads to more insight, the quality of that insight can only be as good as the quality and relevance of the data going in. Ensuring that the right data is collected − not just in terms of volume but in context and accuracy − is paramount. Yet, keeping data clean and true can be a challenge for the industry. Especially in regions where the sheer number of deregulated utility companies means that the electronic data interchange (EDI) can be slow and inconsistent. Once this bad data infiltrates the system, problems begin.

Mitigating the issue

There is no doubt that smart meters can help mitigate the issue of bad data. However, despite the numerous benefits of smart meters, their adoption remains slow in many regions due to high initial costs and consumer resistance. Here, it is still common for staff from utilities having to visit premises to physically read meters.

In these regions it is imperative that energy companies continue to educate consumers about the long-term benefits of smart meters and implement pilot programs to demonstrate their effectiveness. Regulatory support and incentives can also play a crucial role in accelerating adoption rates and encourage more to transition to smart meter technology.

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Know your customer

However, it is not just about knowing what energy is being consumer and any one time, but by whom. Know your customer (KYC) guidelines require a supplier to verify the identity, suitability, and risks involved with maintaining a business relationship with a customer to help with debt and fraud prevention. Yet, it is not infallible. Human error, or confusion, at point of input can be common. For example, the same customer could be entered as dave, Dave, david, David, Davd or more.

Each business needs to decide how aggressively it scrub its data to help eliminate false positives. Thankfully, there are innovative tools available that can help with this, so that bad data does not impact others further on downstream. This, coupled with ensuring that the right steps and measures are in place so that everyone would input ‘David’ correctly can help keep the data error free.

The future looks bright

Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of transforming energy data into actionable insights. These technologies excel in analysing complex datasets, identifying patterns, and predicting trends that would be indiscernible through conventional methods. For energy suppliers, this means the ability to forecast energy demands and adjust supply with unprecedented precision, ensuring efficient use of resources and stability in energy grids.

Its ability to analyse complex datasets has also made AI the perfect technology to help scrub data of any inaccuracies. To work effectively, it does need to be trained correctly, though, so that it understands that the same data field can be called different things in different databases, i.e. surname, family name, or second name. Therefore, energy suppliers must focus on implementing a robust data governance framework to maintain data integrity, reduce noise, and avoid biases that could skew AI analyses and decisions.

As AI continues to permeate throughout the industry, the future looks bright. Not only is it being used effectively to clean up bad data, but it is being used to improve customer service due to increasingly sophisticated chatbots, and smooth onboarding by accelerating the credit check process.

Not the time to take chances

As the industry moves towards a greener future, bad data and inconsistent data management practices is hampering its progress. Never before has there been more of a need for precise and reliable data from the point of energy generation through to final consumption. Challenges have arisen from the data being fragmented or when the systems used to capture the data have become outdated. However, we are turning a corner.

Technology has become instrumental in reducing bad data entering the data chain and mitigating the effect of any that slips through the net. The likes of AI, ML, and the Internet of Things (IoT) have all reshaping the landscape of energy data management. Their integration into the energy sector signifies a pivotal shift towards more sophisticated and reliable data handling mechanisms, essential for the effective management of renewable energy resources.

However, this is not to say that humans should be removed from the process completely. In my view, there should always be a human layer to any key business process. Automation is important, but there will always be nuances within the data than only a human can correctly interpret. Either way, with increasingly stringent data regulations needing to be adhered to and potentially crippling fines for noncompliance, it is not the time to take chances.