The energy industry could create an extra $1.3 trillion in value over the next 30 years for every 1% of greater efficiency within the business. So, why isn’t it doing more to get there?
Companies within the sector are facing huge challenges - working to become more balanced energy providers as they strive to hit their own net zero targets.
Across the sector, there is an appetite to digitise and utilise data to its full potential. However, getting there will be costly in an industry with archaic infrastructure and operating models not ready for change.
Only a radical overhaul that embraces AI can address these issues head on.
Mesh-AI has been working with several energy companies to drive forward these changes and here’s why we think the industry needs to act now.
The energy industry knows that it needs to innovate. It’s watching on as other sectors get huge amounts of value from data initiatives such as artificial intelligence, advanced analytics and machine learning.
The issue within the industry isn’t lack of data. In fact, most energy businesses are sitting on a huge amount of data, much of it real time. The problem is that it’s hosted on legacy platforms and being managed in traditional ways that are decades old in some cases.
The majority of energy companies remain very engineer and process-centric rather than being data driven, leaving them struggling to bring advanced data capabilities to life.
There’s also the very real and imminent danger of losing key knowledge from within the business, as the more experienced people move on or retire from the industry.
The current lack of data expertise sitting within the industry means that any attempts to build tools for automation often happens in silos. These data tools are created without inputs from teams across the business who could benefit from their insights on a day-to-day basis. A tool is only as good as your underlying data management and data governance practices so without that solid foundation there will be no significant improvement to working practices.
While there may be pockets in every organisation who are doing it reasonably well, the majority of data is inaccessible to most in the business and therefore unscalable.
Many within the industry fear that any decentralisation of the current infrastructure could lead to vulnerability from cyber-attacks or complete system failures.
A reluctance to innovate means that much of this work stretches back years. Yet without access to the relevant data, businesses are struggling to use analytics to help prioritise this.
The situation is something that regulators haven’t failed to notice. Many companies are risking millions of pounds in fines that could even run into hundreds of millions in cases where a regulator has to step in and stop operations.
If they can't create good quality data that's stable and accessible by everyone, they won't be able to move up the chain to get real value from machine learning.
Addressing the quality of their underlying data is fundamental not only for them to thrive today, but also for the future of the industry and the planet.
This fresh approach will allow energy companies to push out data to teams working in areas such as maintenance or inspection, who can use it on a day-to-day basis to prioritise their work.
There’s currently a disconnect which means that there are some engineers and technicians who don’t understand the downstream implications of the data they're inputting into the system.
It’s not just about how you manage data but how data affects your day-to-day job and the overall benefits for the business.
Data governance across the organisation is also critical for transformation. It's really important that businesses understand who's accountable for the quality of data and that they take ownership for how accessible it is to every part of the business process.
There’s often a gap in terms of understanding why data is so important for business processes so these data owners need to educate and drive the cultural change that needs to happen.
In many companies there's still a distinction between system and operational data versus analytics data. Yet the data that engineers are inputting in their everyday roles can be used to provide game-changing insights.
Take the maintenance cycle as an example. A piece of equipment may be getting serviced annually but analytics reveal this isn’t necessary because it isn’t going to fail for the next three years providing instant value to the business.
Moving further towards data-driven insights that are powered by automation is a huge shift for an industry that has traditionally thrown more people at problems.
But while more people and offline processing may get the job done to some extent – eventually – it won’t speed up the process, eliminate human errors or offer the opportunity to scale.
This has finally been realised by directors who are saying no to more headcount and telling business leaders to sort problems out from a system perspective. Optimise the process and make it more efficient.
A self-service data platform that presents data products, accompanied by an upskilled workforce who know how to use it, offers higher quality, accessible data, allowing organisations to see true transformative results.
Overcoming challenges posed by infrastructure and approaches that are decades’ old are not without pain points - but the benefits are business value that could run into hundreds of millions of pounds.
Embedding data product principles such as value, feasibility and usability and the platforms and automation to support this will enable businesses to introduce an end-to-end automated data governance framework.
Robust new central platforms will make data a much more valuable asset for the organisation, while also greatly reducing the risk of human error.
Reporting accurately to regulators on important issues such as greenhouse gas emissions and carbon footprint will not only play a key role in tackling climate change, but will also help businesses avoid heavy fines.
The industry also needs new capabilities to allow it to move at pace when investigating the integration of new sustainable power sources.
The BNEF estimates that without intelligent data insights to provide greater flexibility, power system costs will increase by a massive 6 – 13% over the next 20 years.
Using AI can help to control and balance this intelligent flexibility with innovations such as AI-enabled electric battery charging, which helps to lower costs by reducing the reliance on fossil fuel plants for backup.
AI can also be deployed to help businesses keep transformers within optimal operating ranges which could save $188 billion in replacement costs over the next 30 years.
Additionally, improved prediction accuracy can help to anticipate energy demand and safeguard consumers from widespread outages.
Energy companies have traditionally struggled to attract young people so by utilising data science to transform, businesses can help to make it an exciting industry to be a part of.
These changes will help to drive the recruitment of really good data engineering specialists into the industry which can only lead to further transformation.
The challenge is big – but then so is the opportunity.
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