Making AI-enabled energy operations a reality
At its most basic level, energy trading is a risk management exercise and, yet, operational risk creeps in by stealth.
16 November 2018
4 Minutes read time
"The importance of data as the underpinning foundation for business transformation"
At this year’s ETOT (Energy Trading and Operations and Technology) conference, keynote speaker Andy Singleton from Baringa emphasised the importance of data as the underpinning foundation for business transformation. Combining data with talent within the business was, he said, the ‘secret sauce’ and failure to do would create sand. And sand would bring a previously well-oiled machine to a halt and prevent monetising flexibility across assets.
Energy and commodities markets are complex and only getting more complex and this has created its own challenges to extracting data easily. Yet, an expanding set of asset classes, an increasingly distributed delivery model and an ever-changing cost base means firms have to be flexible to survive.
As a first step to creating adaptability, we’ve seen a lot of adoption of RPA tools across firms as they attempt to reduce manual processes and drive out inefficiencies. But this is playing out in the same way in the energy sector as when the banking industry did it – returns and efficiencies are not as high as expected. Not for the first time, we are hearing comments such as ‘we’re under pressure from our Board, the RPAs have not delivered the ROI that was promised.’ Not only are RPAs limited in what they can automate but they are also far from ‘plug and play’.
If data and talent are the secret sauce, then RPA should be considered a single base ingredient. Used for the right task (and task it is, not process) that handles largely structured data, they can indeed deliver some of the efficiencies being sought. It’s a matter of the right tool for the right job. But you also need enterprise level automation technologies that can handle semi-structured and unstructured data and can re-imagine complex processes.
Businesses are trying to juggle an overabundance of data sources, complex and onerous processes and lots of data types with systems and methods designed for different times. They need a clear direction to find a way out of a seemingly impossible mission set by the powers that be. By putting data at the heart of their strategy, a clear and coherent approach to building a flexible, digital backbone can be fostered.
Data sits at the heart of any intelligent automation. There continues to be lots of hype around artificial intelligence and lots of confusion of how and what to automate. Where do we start? What do we keep? What do we replace? Some believe the answer is to rip everything out and start again with AI as the panacea. They then discover that AI systems can be complex, requiring specialist skills e.g. data scientists, taking a long time to implement and not being very adaptable etc. So where to start? With that secret sauce. Always with the data.
Digital transformation through intelligent automation is a journey. It starts with use case pilots that deliver rapid ROI and moves to high priority use cases that eventually roll out to a programme building long-term capabilities. Data sits at the heart of the transformation. It’s about intelligently connecting the right data, in the right format, at the right time. Data needs to be extracted and transformed and processes overhauled and optimised.
The energy and commodities sector can learn a lot from the path already beaten by the banks. No single automation tool, such as RPAs, can deliver the transformation that is needed to deliver the change needed. As the proverb goes, ‘it takes a village to raise a child’ and it needs a community of automation technologies interacting with each other to raise a digital organisation that can adapt to ever-changing surroundings.