The need to clean data for effective insight
There is more data today than ever before. In fact, the total amount of data created, captured, copied, and consumed globally has now reached an incredible 149 zettabytes. The growth of the big mountain is not expected to slow down, either, with it expected to reach almost 400 zettabytes within the next three years.
Whilst more data generally 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 too − is paramount. Yet, keeping data clean and true can be a real challenge. This is especially true 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.
Getting smart to mitigating the issue
Smart meters are key to a more sustainable future for all. They allow the industry to better manage what energy is needed, and where. That means reducing energy waste and us being less reliant on traditional fossil fuels.
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Smart meters are also instrumental in helping mitigate the issue of bad data. Yet, despite the well-publicised benefits of smart meters in helping consumers better track their energy usage and get more accurate bills, their adoption is still behind initial projections across many regions. 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 do what they can 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 be instrumental in accelerating adoption rates and encourage more to transition to smart meter technology.
Knowing your customer
However, it is not just about knowing what energy is being consumed at 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. Whilst good on paper, 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. It used to come down to time. Thankfully, there are innovative tools now available that can help, so that bad data does not impact others further on downstream. Using such tool, plus ensuring that the right steps and measures are in place so that everyone would input ‘David’ correctly in the first place can help keep the data error free.
Transforming data into actionable insights
Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of transforming data into actionable insights for the energy industry. 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 the stability of 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 from the off to maintain data integrity, reduce noise, and avoid biases that could skew AI analyses and decisions further down the line.
As AI continues to permeate throughout the entire industry, the future looks bright. Not only is the technology 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.
We are turning a corner
Unfortunately, bad data and inconsistent data management practices is hampering the industry’s progress towards a greener future. 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 typically arisen from the data either being fragmented or when the systems used to capture the data have been unable to cope. At a time where data regulations have become increasingly stringent and potentially crippling fines have come in for noncompliance, it is not the time to take chances.
The good news is that we are turning a corner. Technology has become instrumental in reducing bad data entering the data chain and mitigating the effect should any slip 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 across 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 for sure. But there will always be nuances within the data than only a human can correctly interpret.
