{"id":8594,"date":"2024-02-21T09:32:45","date_gmt":"2024-02-21T09:32:45","guid":{"rendered":"https:\/\/members.dataiq.global\/?post_type=article&p=8594"},"modified":"2024-03-14T10:36:00","modified_gmt":"2024-03-14T10:36:00","slug":"edf-sustainability-unexpected","status":"publish","type":"article","link":"https:\/\/www.dataiq.global\/devstage\/articles\/edf-sustainability-unexpected\/","title":{"rendered":"DataIQ 100 Success Series: EDF \u2013 National sustainability and preparing for the unexpected"},"content":{"rendered":"

Aylward and his team have been working diligently to implement new tools and operations to evolve the standing of data within EDF, to achieve difficult targets and to ensure the data team becomes central to all decisions within the business. As a leader, Aylward has worked hard to find ways to develop strong relationships with decision makers in the business across departments and to support his growing team to deliver consistent success \u2013 sometimes unexpectedly \u2013 that has improved business efficiency, safeguarding for vulnerable customers, and greatly impacted the bottom line.<\/span>\u00a0<\/span><\/p>\n

Prior to Aylward leading the team, EDF was actively seeking to improve its data capabilities across its retail business, but the solutions being implemented were not delivering the results desired. After taking the lead, Aylward centralised the data teams that had developed over the business and set out evaluating the value required for the business.<\/span>\u00a0<\/span><\/p>\n

\"Martin
Martin Aylward, Head of Data and CRM, EDF.<\/figcaption><\/figure>\n

\u201cThere were about ten different data teams spread across the business \u2013 in IT, marketing, operations, etc \u2013 and I brought those teams together into one central data operation focusing on delivering value for the business,\u201d said Aylward. \u201cPreviously, the platform team would have been chasing the next piece of tech but not examining the value it could bring, so EDF moved to starting with use cases and building backwards. We examined the various areas where we thought we could derive value from the new cloud-based platform and then make sure the platform is always able to deliver valuable use cases for EDF.\u201d <\/span>\u00a0<\/span><\/p>\n

Aylward places a lot of the success of this approach on the shoulders of Alex Read, Senior Manager of Data Platforms, EDF, who has been instrumental in supporting Aylward\u2019s vision and execution since joining two years ago. \u201cMartin pulled on me to remove blockers and instilled a mindset of ruthless prioritisation around the mission statement and the value add,\u201d explained Read. \u201cWe would always steer our resources, and you could easily get lost if you did not have that relentless and ruthless prioritisation of aligning our people and our work packages up alongside those highest impact use cases.\u201d<\/span>\u00a0<\/span><\/p>\n

\u201cEverything we do within EDF in the UK is motivated by helping Britain achieving net zero. The goal is nice and clear, and everyone understands it,\u201d said Aylward. This is hoped to be achieved with new near-real time updates every five minutes from UK-based wind farms and renewable energy sources, demand flexibility services which help balance the national grid as well as the data provided by smart meters and forecasting across the energy market, where a 1% difference can mean savings of tens of millions of pounds. <\/span>\u00a0<\/span><\/p>\n

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Leading the data charge at EDF<\/span><\/b>\u00a0<\/span><\/h4>\n

Prior to leading the data team, Aylward had worked at EDF for more than a decade and had always been involved in roles that required data-led decision making and he wanted to bring the diversity of the roles he had played into his data team. \u201cWhenever I build a team, diversity in all its forms is really important,\u201d said Aylward. \u201cWhat we did in the data team was gather a bunch of people with different and complementary skills to ensure we had diversity of thought and experience.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

As an example, Aylward described pairing up Rebecca Vickery, Lead Data Scientist at EDF and 2022 winner for the DataIQ Award for <\/span>Data Champion (Data titan) in 2022<\/span><\/a>, with Gavin Hurley, Head of Residential and Commercial Business, EDF who holds a PhD in Data Science but has worked in commercial roles across the business, so there was an understanding of how data would be utilised and valued by different aspects of EDF. <\/span>\u00a0<\/span><\/p>\n

\u201cOne of the benefits of this approach and utilising people from different areas of the business is that they held really good contacts throughout the company,\u201d said Aylward. \u201cPreviously, the central part of the data team was quite divorced from the rest of the business. This impacted the standing of the data team and meant that, often, there were examples where people would go off in different directions due to reduced communication.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

Read explained that, when he joined, there were an overwhelming number of use cases to get to grips with, but Aylward \u2013 as a data leader \u2013 educated him on examining the top ten most impactful ones and leveraging the value delivered with the aims of the business, as well as the aims of the data department. \u201cMartin had a massive impact on me through weekly catchups, particularly when we were in the midst of getting the platform off the ground, and appreciating the multiple projects I was tasked with juggling,\u201d said Read. \u201cThis was instrumental, particularly as I was new to the organisation, and helped instil that laser-sharp focus that has led us to success.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

\"Martin
\u201cWhenever I build a team, diversity in all its forms is really important,\u201d said Aylward. \u201cWhat we did in the data team was gather a bunch of people with different and complementary skills to ensure we had diversity of thought and experience.\u201d<\/figcaption><\/figure>\n

Aylward inherited a siloed data culture and a structure that meant different teams did not have the same access to data as others, which led to a situation where there were pockets of brilliance across the business, but it was either not fully utilised or simply overlooked. Through hiring across different departments and improving the centralisation of the data team, the communication greatly improved under Aylward\u2019s stewardship, and the pockets of success became more useable and shareable across the business.\u00a0<\/span>\u00a0<\/span><\/p>\n

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Implementing change<\/span><\/b>\u00a0<\/span><\/h4>\n

There was a clear goal underpinning the purpose of net zero \u2013 delivering value to our customers, our people and EDF. \u201cThis goal became our North Star,\u201d said Aylward. \u201cPut into the context of having a large team with numerous projects on the go at any one time, this is why we needed to implement a mindset of ruthless prioritisation.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

Aylward explained how there was a period where the trust placed in him, and his team was so high that they were given almost unlimited freedom over the solutions they wanted to implement. \u201cI had built up my position into a place of trust within the top level of EDF and they explained that I should go ahead and do what I think is needed and to contact them if there are any blockers,\u201d said Aylward. \u201cI provided regular updates on progress and successes, and the faith they placed in me and the team paid off.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

One of the main benefits that could be provided to customers is a financial one, and this in turn plays its part in achieving the overall target of net zero as reduced energy use means reduced costs and therefore reduced environmental impact. \u201cThe EDF Energy Hub app, for example, has been developed to provide smart meter customers with detailed insights into the amount of energy being used\u00a0throughout their home, as well as implementing a level of gamification for users to compare themselves against similar houses in the area,\u201d said Aylward. \u201cThis has improved the customer experience in addition to helping customers reduce their energy consumption and in turn their bills \u2013 as well as\u00a0their carbon footprint. We have armed people with tools to make better decisions quicker and receive the value of those decisions faster than ever before.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

This was one of the examples shown to decision makers at EDF to continue investment in the projects that the data team wanted to undertake and demonstrate the power of data in real-world business metrics, such as customer experience, helping vulnerable customers and providing financial benefits.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cWe split our time between achieving the easy things and applying ourselves on big projects that will deliver the largest value,\u201d said Aylward. \u201cThis meant a split between working on the actual platform and the use cases. It involved a careful balance between making the platform improvements that were needed, but not spending all our data engineering time and effort to the detriment of use cases.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

One of the first platform implementations after bringing the teams together was the migration to Snowflake for the Data Warehouse plus one of the world\u2019s first implementations of Snowflake\u2019s Snowpark Data Science Platform. The responsibility of the platform was given to Read under Aylward\u2019s leadership and was such a success that it enabled Snowpark to go into General Release and EDF implementation was mentioned in Snowflake\u2019s US earnings statement. \u201cHistorically, the Snowflake platform acted as the data warehouse platform, but there was an additional, separate platform acting as the machine learning operations (ML Ops) platform which meant we would have to remove data from one platform to put it into another and then bring it back again,\u201d explained Read. \u201cThe new approach provided the ability to run all our ML Ops on a single platform and breathe a new lease of life into it. It removed the need for that separate platform, separate data storage and easily supported skills to keep it administered.\u201d<\/span>\u00a0<\/span><\/p>\n

Since implementation, there is now a rapidly maturing ML Ops capability at EDF that stretches across the retail space with the services being built up to accommodate artificial intelligence (AI) products and more. Clementine Whitcombe, Data Engineer, EDF, noted that \u201chistorically, the data was hidden away in silos and the implementation of this new platform has made the data more useable and accessible across the business. It is now much easier to provide access and monitor the data sets being collected.\u201d <\/span>\u00a0<\/span><\/p>\n

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Unexpected benefits<\/span><\/i><\/b>\u00a0<\/span><\/h4>\n

A really important part of the energy market is the hedging or forward purchasing of energy within the wholesale market and aligning this with when the consumer will actually consume it. This was made increasingly difficult following the energy crisis and global trade difficulties of the last 18 months, and the cost implications for EDF suddenly got exacerbated. \u201cThere is risk or opportunity measured in the tens of millions of pounds, and this requires data-led decision making\u201d said Aylward. \u201cWe manage trading of around \u00a310 billion of energy a year, and during <\/span>the peak of the energy crisis, the cost of wholesale energy went up eightfold.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

When the price of wholesale energy went up between 2021 and 2022, consumers were protected from the full impact of the increase by the Government\u2019s Energy Price Guarantee; and businesses by the Energy Bill Relief Scheme. The data platform enabled the complex mechanics of the schemes to be efficiently managed, ensuring that consumers and businesses were protected at a time when they needed it the most.<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

Additionally, Aylward\u2019s Data Science team created a Vulnerable Customer Identification ML model which enabled EDF to accurately target customers most in need of additional help under EDF\u2019s Customer Support Fund which has provided more than \u00a350m of additional support for vulnerable customers since its inception.<\/span>\u00a0<\/span><\/p>\n

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Knowledge expertise<\/span><\/i><\/b>\u00a0<\/span><\/h4>\n

It is one thing to create a team from a diverse set of backgrounds within a business, but it is another entirely to rely on them as experts on data and aspects that are specific to working in the energy sector.\u00a0<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cI need my team to be experts in what they do,\u201d said Aylward. \u201cIn this case, I need them to be experts in data, data engineering, data science and operations \u2013 but they must have a good understanding of the energy market and wholesale market services. Furthermore, when you get into the trading side, there are aspects like optimising wind farms and grid scale batteries that complicate matters while striving to achieve our net zero goal. To help support the in-house team further, I selected to work with Accenture because similar to my in-house team, they have a solid understanding of the energy market and the team there are fantastic on the technical data side of things with cloud delivery tools and support. It really is a match made in heaven.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

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An energised future at EDF<\/span><\/b>\u00a0<\/span><\/h4>\n

With the data platform built and fully operational within the retail business, the plan is to expand it across the whole company, including the wholesale market services business, where there are some huge use cases to be utilised.\u00a0<\/span>\u00a0<\/span><\/p>\n

One example of this use case is around volume forecasting \u2013 predicting the consumption of energy across days, months and years \u2013 as well as the generation side, such as examining renewables and how it ties in with hedging activities.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cHedging involves around \u00a310 billion of energy per year, so if we can forecast better, the benefits can be massive,\u201d said Aylward. \u201cThe price of energy goes up and down, so being able to better predict how much energy is needed means there are potentially tens of millions of pounds of risk or opportunity around purchasing the right amounts and at the right time. We are now receiving near-real time updates from wind farms in the UK about their production, meaning we can better anticipate the purchase of renewable energy in collaboration with the Met Office to narrow this down to individual wind farm locations. This is a huge step towards achieving net zero.\u201d<\/span>\u00a0<\/span><\/p>\n

Outside of this, the EDF team is working on developing a federated mesh approach to its data across the business, further increasing its ability to provide insights and contact to those that need it. \u201cOur focus has been on enabling the federated teams to deliver data product and data capability with minimal barriers,\u201d said Read. \u201cAn essential capability that can be achieved through this approach are examples like finding the optimal operations for contractual synergies for our commercial capabilities. We want there to be empowerment \u2013 empowerment and enablement to remove barriers to success.\u201d<\/span>\u00a0<\/span><\/p>\n

There are portions of EDF that are still early in their data journey with growing maturity levels, and under Aylward this will continue to evolve. With an ever-maturing set of data capabilities, the EDF team is eager to continue improving its offerings to customers, its efficiencies as a business and achieving its ambitious net zero target.<\/span>\u00a0<\/span><\/p>\n

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Top learnings<\/span><\/b>\u00a0<\/span><\/h4>\n

As he finds himself in a position of influence to the future data leaders, Aylward set out some of his top learnings for data professionals to take on board.<\/span>\u00a0<\/span><\/p>\n

\u201cStart with a purpose and a vision,\u201d said Aylward. \u201cAnd do not worry if you do not know how you will get there. Back in the 1960s, Kennedy said he would put a person on the moon at the end of the decade \u2013 he did not have a clue how it would happen, but if you do not set goals then you will never achieve greatness. This is the view I am taking with our goal of achieving net zero \u2013 I do not necessarily have the answer right here, but I know it can be achieved and I know we will find a way to make it happen.<\/span><\/p>\n

\u201cBuild a diverse team and always encourage people to be curious and set aside 10% development time for people in data teams to do what they think is the right thing to do for their own personal development\u201d explained Aylward. \u201cDeveloped individuals become empowered teams. I am not an expert on everything within data, so I rely on my team and their expertise and support their decisions. There must be the development of a performance culture with an emphasis on delivering value to business and customers.<\/span><\/p>\n

\u201cBe stubborn on vision and flexible on execution,\u201d said Aylward. \u201cCollaboration internally and externally, particularly with partners, is pivotal. Make sure everyone is on the same page about what is to be achieved, but how it is achieved and how the team gets there is completely fluid. We take care with selecting our external partners to make sure they not only have the necessary expertise but also share similar values, commitment to our goals and understand the importance of teamwork and collaboration to get the desired results.\u00a0 In our case we chose Accenture, who have been consistently excellent in supporting our in-house team. Collaboration leads to more opportunities for success and raises the profile of the data team within different departments.<\/span>\u00a0<\/span><\/p>\n

\u201cInvest in your data cloud platform, but make sure you are adopting and not adapting,\u201d said Aylward. \u201cI believe if you think you can build a better data product than some of the cloud systems that currently exist with their billions of pounds of investment and research and development, think again. For example, we managed to implement Snowflake\u2019s Snowpark and AWS\u2019s Sagemaker as our Data Science platform within six weeks and it has been running brilliantly for us and providing massive benefits. You must keep the focus on progress over perfection. If you are building a data team and they want to create the perfect data platform you will waste your time on trying to achieve the unattainable and fail to deliver for your stakeholders and miss your goals.\u201d<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

EDF\u2019s head of data and CRM, and member of the DataIQ 100 Martin Aylward, spoke to DataIQ editor Alex Roberts, about what data leaders need to succeed and how investment in data teams can provide extreme unseen wins.<\/p>\n","protected":false},"author":19,"featured_media":8595,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_searchwp_excluded":"","footnotes":""},"categories":[129,282],"tags":[391,119,392,356,421,422,236,393,425,423,424],"pillar":[195,194],"class_list":["post-8594","article","type-article","status-publish","format-standard","has-post-thumbnail","hentry","category-editorial","category-member-case-studies","tag-data-leader","tag-data-literacy","tag-dataiq-100","tag-diversity","tag-edf","tag-energy","tag-investment","tag-learnings","tag-savings","tag-sustainability","tag-utilities","pillar-literacy","pillar-leadership"],"acf":[],"publishpress_future_action":{"enabled":false,"date":"2026-05-21 07:44:28","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/8594","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/types\/article"}],"author":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/comments?post=8594"}],"version-history":[{"count":0,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/8594\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/8595"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=8594"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/categories?post=8594"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/tags?post=8594"},{"taxonomy":"pillar","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/pillar?post=8594"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}