Data analytics and the utilities value chain


Posted on: November 14, 2016 by Franck Freycenon

Until relatively recently, nobody really thought about a utility value chain at all.  Electricity was a commodity. It was generated and distributed to the people who needed it. It was consumed and billed according to energy consumption. All over the world, distribution has historically been through a mainly radial top-down grid. When electricity always flowed from the transmission grid to houses and businesses, these “blind low voltage” networks needed little instrumentation.

It’s certainly not like that anymore. The center of gravity is shifting away from the main grid to both on-site generation and storage and distributed generation. Thanks largely to changes in regulation and the emergence of new players; to the rise of prosumers and, more recently, to the serious arrival of electric vehicles, players all along the value chain need to ask what role they are going to play. The challenge is how to avoid commoditization now that traditional business models are under threat.

The world was certainly simpler when utilities were monolithic – and in many parts of the world, under state control. Now we have a vastly extended eco-system of contributors – which includes not only new corporate players but prosumers too -  the domestic and industrial consumers themselves.

And with this new eco-system, we have a new value chain, in which the pressure is on to create maximum value at every stage – not just at the point of consumption.  For the established utilities, many of whom have until now operated across the entire value chain, it is time to examine each phase of operations and ask how best to compete effectively against new entrants – and indeed to collaborate with them too, in the creation of new value-added services.

Data as a differentiator against new competition

The new data revolution is inseparable from the value chain. Every member of the wider ecosystem now recognizes that the latent intelligence hidden in the data available to them is central to the quality and profitability of their business. Collecting, processing, interpreting and converting data becomes central to the ability to offer knowledge-based and value-added energy management services.

For established players especially, the greater the range and volume of data available, the greater the potential advantage it gives – at least in theory.

The challenge for many is that although they know that data and new data analytics are key to success, it’s just not clear how or why. Utilities need to think differently about how to leverage data as both an operational and market enabler.

In part, this is cultural. The utility sector has a long and respected record in the industrial analytics needed to manage and ensure supply. The new data revolution has a clear impact here, for example, in the need to balance grid supply in the face of locally produced renewables. The sector does not, however, have the extended experience in putting analytics at the heart of business and commercial operations.

From my previous experience in the telco market working for industrial and financial services clients, our client propositions were always crafted on the basis of extensive analytics. What the telco and retail sectors have been doing for years is still relatively new in utilities especially with regard to customer engagement.

But in many ways, this is good news. It means, for example, that utilities can fast-track their own learning and adoption in analytics by building on the experience from beyond the sector. It can be complicated, but no less possible for that.

Think for example about the way that a utility company does business with a major industrial consumer – such as a railway company. The relationship is generally between the TSO and the railway, and because the railway runs on timetables, demand can be predicted with such accuracy that the cost can be factored right down to the impact on ticket prices, if desired.  With the power of analytics now available to us, this degree of granularity can become a routine part of business thinking for every forward-looking utility.

Similarities and differences – geography matters

In the years ahead, there will be more incentive than ever before to share ideas and best-practices between sectors and specialties. At a global level, utilities do share many common objectives and challenges. They are, for example, in the vanguard of education for sustainability and environmental responsibility. Similarly, they share the challenge of managing the shift from centralized to decentralized production.

Despite shared characteristics, however, local geographic behavior and challenge vary greatly between countries. In Scandinavia, for example, utility companies and consumers are much further advanced in managing renewables and dynamic tariffs than in other parts of Europe.

What next?

Managing these many changes in utility practice is inseparably linked to innovation in data analytics. At Atos, we are already active in numerous practical initiatives which combine our learnings in analytics with our specialized experience in the utilities sector. These include using analytics to radically improve detection of fraud and exploiting smart meter data flows to offer consumers greater control over personal consumption and domestic management.

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About Franck Freycenon

Expert Sales - Codex
Franck Freycenon began his career in the IT sector focusing on improving performance of IT Systems of Telecoms Operators. He then spent 10 years as Account Manager at Orange Business Services for industrial and banking clients. In 2011, he joined a consultancy and digital agency where he managed the development of the relationship with the energy companies and their ecosystems. Franck joined Atos early 2015. Today, its mission is to enhance the digital transformation in the energy sector and Atos value proposition on this topic. As such, he is responsible for business development of the platform for new services in the energy, based on Atos global platform initiative in Big Data.

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