Fighting the Climate Crisis with Data

Jean-Pierre Pélicier:

We work from production to retail, but we also store electricity and gas. We trade, as you know, and we also offer energy solutions to customers so that they can optimize their consumption. And in all of those activities, today we have projects. to deliver business value with data.

Adel Nehme:

That's very exciting and I really appreciate that example and how you contextualized it with the disruption of the energy market and the war in Ukraine. You mentioned the value chain of energy, right? So maybe we're going to discuss a few use cases of data science and energy. Maybe it's good to also demystify what that value chain looks like. Maybe walk us through what that different what the different sides of energy production looks like, what that value chain looks like and where does energy where does energy sit in that value chain?

Jean-Pierre Pélicier:

And so really, so NG is a global player of this value chain. And it all starts with production, you know. So it can be production of gas or production of electricity. And so we have assets of... let's say facilities which can be gas thermal plants, which uses gas to produce electricity, but they are the, let's say the old way to produce electricity. And on the other hand, we now have assets of renewable energy that use solar or wind or biogas to produce as well that can produce electricity. that's one part. The other part is that we transport after the mid-gas or electricity, we have assets to transport those components. It's a molecule in the face of gas or it's electrons for electricity. We transport that and we can transport that on very long distances on thousands of kilometers. For example, you might... And after, once they are transported on those very long distances, there are networks of distributions, secondary distributions that go to your house or industrial facilities where after that electricity or that gas is consumed. And so we have companies to produce, we have companies to transport, we have companies to distribute, and we also have after companies that cell, the electricity or the gas, each time there are separated activities. Now you have to understand that each time it's different entities, different business organizations. And that's, let's say the primary value chain. And then as I said, we also have other activities which are complementing this value chains like the storage. Sometimes we... And we have seen that in the winter in Europe this year. There were a lot of attentions given to where are the levels of storage of gas? Do we have enough gas storage to go through the winter? And actually, ENGIE operates such facilities of gas storage, its cavities that we have on the ground, and where we inject gas when the consumption is not so high. and where we consume the storage of those gas when it's needed, for instance, in winter. And finally, we have an activity of energy providing advice to industrial customers about their energy management. And we install some solutions to optimize the energy consumption and to reduce their energy footprint and carbon footprint also, by the way. And I would say finally, yes, there is the trading activity. I've mentioned it in the introduction, but that's also an important part of the company. We have about 3,000 people working in the training activity today. Globally,

Adel Nehme:

Okay.

Jean-Pierre Pélicier:

Engie is a 96,000 employee company to give you, and we are present in 31 countries today.

Adel Nehme:

So this is thank you so much for this really detailed overview and you know what's very interesting is how complex the value chain is You know, there's definitely a lot of different activities here And moreover, having leading data at a global organization definitely gives you a lot of unique challenges, I assume. As a chief data officer, what are the most critical success factors for organizations, especially in the energy sector, but especially in global organizations such as Engie, when you want to harness data effectively for value creation?

Jean-Pierre Pélicier:

Wow, you know, data is effective when it can be shared, when it can be easily accessed, and when after it is computed

Adel Nehme:

Yeah.

Jean-Pierre Pélicier:

to create analytics and value. So I would say that what is really critical to unlock that is first to have a culture of collaboration in the company. And it's true that at NG, we are coming from an a culture of decentralization where we have many, the local organizations have a lot of autonomy. And so that is a situation where data is not easily shared between organization and entities. And so one of the critical part of our action is to ensure that now we have, we make people understand and that, yes, of course, they... They have their perimeter to manage data, but they could benefit from data coming from other functions. Like if you are in the industrial production part, of course you need finance data to operate, to understand how your P&L is going. But also it could be useful to have access to some HR database when you need some trained people to hire in certain fields. And after you, this is an example of if you are, yeah, to say that if we unlock the data, we create more value. It's a Let's say it's a very basic assumption that I'm saying, but that's not so easy to achieve in a white company. I would say that the very first thing is to ensure that this collaboration mindset, this data sharing mindset is there. And so after what we've done is that we've, the second part I would say is that we have infrastructures that are effective and easy to use. So that people, when they are convinced and they are say, okay, I'm ready to share my data, but if it's cumbersome, if it takes hours or to share it, I will not. So we have to build easy to use infrastructures. And I will, so that's the second part. And the third one I would say is that maybe the most important one in the end, once you have those fundamentals of infrastructure and mindset is that you focus your your actions on only what is the most value creation, because otherwise you can waste your energy and efforts on things that are useful but not providing the much value that you can. And that's where having a strong governance is essential.

Adel Nehme:

That's very great. And we're going to unpack a lot of these elements. You mentioned here a culture of collaboration. And essentially this connects to a data culture within the organization. You also mentioned the infrastructure component, making sure the data is easily accessible, and also prioritizing value creation use cases. So maybe let's focus a bit on the data collection side, on the infrastructure side. What's interesting and what's pretty fascinating about the energy sector is that the data sources... that often are used to create value and analyze data come from IoT devices, sensors, pipelines that measure the effectiveness of pipes, right? Walk us through the data collection and quality challenges in this space. What steps has NG taken, for example, to ensure data accuracy, completeness? What does a data governance framework look like in this particular context?

Jean-Pierre Pélicier:

OK, one thing I would mention first is that our journey is clearly not finished in that space. We are

Adel Nehme:

Yeah. Yeah.

Jean-Pierre Pélicier:

in the middle of the journey. So we are working very hard to harness this data and have a complete view on the data collection and the data quality globally. But that said, yeah, if we go back to the value chain, You're right when you say that we have a lot of IoT devices that will send us data. We have IoT to send us data about how a windmill is working during all its days. We have IoT. Now we have smart meters that are installed in many homes in Europe, in France or in Belgium, for instance. and they send you this information on how you consume electricity or gas at your home. And in the future we will have a device that monitors how electric cars are consuming. So yes, we have to deploy a very wide network of IoT to collect this data and that was a tremendous effort. the device and in addition you have to understand that we have a legacy industrial assets network which were not used for digital so we had to deploy a lot of new tools or upgrades of meters to be able to send the information in the end. So today I think we have a it's not complete when we say completeness we cannot say we're complete but we have a a fair view on how gas and electricity are now used at many in our production plants, but also at homes of our consumers, of our final consumers, but also in these facilities. And with all this data, now comes a phase of, yes, are we governing this data well? Do we have enough quality? when there are quality defects, do we identify them quickly or automatically? These are many challenges that we face right now. This is work in progress. But once we have this kind of information, we will be able to unlock a lot of value, of additional value to the company and to final users. Maybe I will tell you, we'll give you an example of that. You know, again, during this winter, we had to face some peaks of consumption because of days of cold. And we were able to forecast those periods of strong cold and to organize after campaigns, for instance in France, to tell consumers, beware, next Monday it will be very cold. And if we want to have a collective effort to ensure that we go through this, we offer you a kind of a challenge, is to say to limit your consumption below this threshold. And that was adjusted home by home. We knew that that home was consuming more or less, let's say, 100 of energy. And we were challenging those people to consume only 80 during that day. And we would reward the person with a financial benefits, like it would give you a five euro or 10 euro discount on your next invoice if you were able to do that. But if we can do that collectively on many homes, it actually helped to balance the system, the global system at the level of the country. And so you see, having the vision on how a home is consuming, we were able to target the companies very effectively thanks to... to our data tools and data systems.

Adel Nehme:

That's a really great use case. You know, in a lot of ways we all remember, you know, I live in Europe as well. I live in Belgium, you know, the winter, especially with the, you know, challenges that came from the energy disruption that we mentioned earlier in our conversation. You know, there was a lot of conversation and chatter within the media, within government about how we need to ration energy to keep the system, you know, globally balanced and seeing data science providing value in that, you know, particular challenge is, is very useful to put it in context. You mentioned as well how there's a culture of decentralization and we need to be able to remove silos between datasets and provide access to datasets to the wider organization. This is oftentimes with organizations that have a history that is as rich as NG's. There's often legacy systems, as you mentioned. What are best practices that you can share when it comes to approaching this challenge of bridging the gap and creating that integration between different datasets, different parts of the organization, and modernizing these legacy systems?

Jean-Pierre Pélicier:

I mean, the question here spans a global data roadmap for a global

Adel Nehme:

Yeah.

Jean-Pierre Pélicier:

company. This is a multi-year work that you

Adel Nehme:

Yeah.

Jean-Pierre Pélicier:

have to achieve. You cannot break the silos and work in an integrated way in six months. It's a multi-year effort. But that said, the way we do it at Tengie is that we work first on the... It's not coming only from the data, to be clear. Actually, we are piggybacking on the global strategy of the company, which wants to be more integrated. And so it actually starts from that global energy strategy to where we went from about 25 business units, now only four global business units, showing that we are much more integrated. And we divested as well some. activities which were not related to our main focus which is low carbon energy, democratizing the access to low carbon energy. So having this global framework of the company is of course key because after our message is to say if we want to be an integrated industrial company the digital and data space needs to reflect that, needs to be aligned. And so breaking the silos will be part of the strategy of becoming more integrated. So having this global strategy is key. But after it takes then, as I said, we need to, as a corporate at corporate level, provide the infrastructure, the data lakes, and all the middleware effective enough so that people can actually break the silos while still retaining the ownership of the of the data and ensuring the cybersecurity of the infrastructure globally. And not least also to comply with privacy regulations, for instance, and other laws which are national. Even though we have a global platform, you need to comply with the national and local regulations. So Here at corporate level, we had to be very disciplined in understanding what kind of infrastructures could be really needed for the benefits of our entities, our organizations. And then it's a lot of effort about networking going to... working on the field with the people to co-construct the needs regarding the infrastructure that will be used after by the entity and to promote the adoptions. So I would sum up by saying that yes, if we have a corporate strategy, it helps. If you have after, and then it's building an infrastructure that is co-constructed with the entity and that requires a lot of... going to the field, going to meet people, a lot of time and energy of course to enhance and make people adopt the infrastructure. So you cannot be sitting only here in the head office and expecting that everything will converge to a global infrastructure alone.

Adel Nehme:

This is really great insight, Jean-Pierre, and we'll definitely deep dive into what that engagement looks like with the wider population and how do you build a culture of engagement. But I think now this marks a great segue. You mentioned the use cases that you worked on when it comes to reducing global energy consumption within the system. But I think also what's really important to discuss is how data science unlocks value for the renewable ambitions of ENGIE. We discussed before our conversation today really how ambitious the renewable energy goals are for NG. Maybe walk us through these ambitions and how does data science unlock value when it comes to the renewable energy transition?

Jean-Pierre Pélicier:

Yes, you have to realize that Engie's ambition is to be carbon neutral by 2045. I mean, being an energy producer and being carbon neutral, let's say in 20 years, a bit more than 20 years, it's a huge challenge actually.

Adel Nehme:

Yeah.

Jean-Pierre Pélicier:

But it also shows the the power of the ambition and of the visions that we all committed here in the community. And if we talk about renewable specifically, renewable will focus the main part of our industrial investment in the coming years. And we plan to be at 80 gigawatts of renewable energy production by 2030. So I mean, at the end of this decade, we want to reach 80 gigawatts of renewables. And that's... To give you a sense, today we are 34 globally. So we want to add 50 gigawatts of renewable energy production. You have to imagine that this is about 50 to 60 nuclear plants. It's the equivalent of the electricity production of 50 to 60 nuclear plants that we want to add by the end of this decade in renewable energy. And so clearly we have massive challenges in in the building and construction, in the project development. But once we've done that, we have to ensure that the production is done in the most effective way. And this is where data science today is helping a lot. For instance, to optimize daily the renewable energy production. And I will give you an example, again, in the solar field. in the solar field, you are dependent on, of course, the weather forecast. And you need to adjust very accurately the orientations of the panels to ensure the maximum output possible. If you are not well positioned, you will not deploy that effectively. And now we have data science algorithms that compares the actually that real almost real time weather data and solar data that is coming in a specific regions to forecast what should be the maximum output that you see and if locally the plant is not reaching or is really far from that production it will be detected by data science and that may trigger some maintenance or at least an investigation of why the site is not producing as it should, based on the observed weather conditions. And that's the same for wind. Of course, the production is dependent on the wind strength and on the wind directions. And again, we can use data science to ensure that, to understand whether the windmills have produced the output that could be expected from the specific and local weather conditions. That's in the field of renewable, how we are using today data science. After in the future, we will use also data science in fields of like maintenance of visual recognition. Because we can send the, you know, the R renewable assets are very often in remote places. And they are quite difficult to access. And we can use now drones to inspect the farms and to collect the visual images and visual recognition algorithms can help us detect some start some defects or some things that will need the maintenance without having to send some technicians on site and it saves a lot of time for our technical teams.

Adel Nehme:

I think these are amazing use cases that you mentioned here, Jean-Pierre, and especially when we start thinking about the climate challenges that we're about to face in the next 20 years, as well as food security challenges that we may face as well. When you mentioned these use cases, one thing that pops out to me is that a lot of these use cases require quite a lot of subject matter expertise within the energy space, renewable energy space. In many ways, you need to be able to marry data science skills with the subject matter expertise to be able to develop these use cases. How do you ensure that data science teams at Engie develop the subject matter expertise? How do you marry that technical skill set with the knowledge of energy? And how do you build that out as a capability within Engie?

Jean-Pierre Pélicier:

Yeah, what you're saying is a critical part. Yes, data scientists need to have a curious mindset to really understand actually the global value chain of where they are working to ensure that the algorithm they build are specific enough and really customized to the need of the operations. And we do that, actually, by really emerging the data scientists in the in the teams in the operational teams. Of course we have a center of excellence which is central but this center of excellence only provides let's say very first if I initiate some projects or helps organization to to think or to brainstorm and to start project but once we are in the real development phase the data scientists They are either internalized by the local organizations or they are sent for a long-term mission on site. So we really have this mindset of emerging the data scientists in their, as if they were other operational colleagues to the teams. So to me, it's a very important part of that. You know, in the end, data scientists are scientists. And

Adel Nehme:

Yeah.

Jean-Pierre Pélicier:

I think that they also have quite naturally, I would say, a curious man say. Because when you're a scientist, you want to understand and you ask the questions to understand. Most of the vast majority of data scientists I've seen are actually very proactive in going to meet their colleagues and to ask them, I mean, OK, I will work on this field. But. Please explain me a little bit more. How does your device work? What do you want to achieve? What is really you would like to expect before they start to develop models or things like that?

Adel Nehme:

So definitely learning by immersion is a great way to give that subject matter expertise. So we talked about how to give data scientists the subject matter expertise. Let's maybe flip it around here and talk about how to give the wider organization some data skills and the data culture. We mentioned that earlier in our discussion as well, how culture and opening up to collaboration and to data sharing is an incredibly important aspect about NG's data ambitions. Maybe walk us through. at the beginning, why is creating that data culture and data mindset so important? Right. And then let's discuss what are some of the initiatives that are done at NG to address that in every organization.

Jean-Pierre Pélicier:

OK, yeah, it's a great question, Adel. And to me, I think this is the most difficult challenge that we are going to face at NG and I think in other industrial companies, in every organization exactly, is that if we really want to leave the digital era and the data era, let's say all the employees of the company should be at least a minimum. level of awareness about how digital data works and how it can create value. And then it means to have a little bit of understanding of what is statistics, what is the minimum mathematics that is behind if you really want to use effectively analytics tools even if they are low code or no code. Today, we know that we won't have enough people trained in computer science or in mathematics to really fill all the gaps of the harnessing data and creating value with it. So we need to upskill our workforce. And it's going to be a massive need of training for all our employees so that they... You know, when you are an accountant and you use Excel, it's great. And you may be many acquaintance are very expert in Excel, but they, maybe they are not aware today that you have local tools that make their work they do in a, in a bit cumbersome way, but expert way, you know, much easier way and unlocking a lot of. other functions with advanced analytics. We need to make these people understand that those tools exist and train them to do those local tools. And conversely, maybe there are some technicians, maintenance technicians on the field, which have a low, let's say a low awareness of digital and data tools because they were not trained in those fields in the first place. But still, in their private life, they are using digital data tools. So they know how to use apps. They know that somehow they understand a bit of the data sharing, what it means, and so on in their private life. And so we need to upskill them and make them understand, yes, in the professional life as well, we will provide you with app tools and data models that you will use to. to improve your work and the effectiveness in your professional job. Just to give you this example, I'm quite sure that many people understand today how Netflix works and they understand that, yes, if they look at these kind of movies or series, they are some algorithm that works in the background that after will prompt them with other series or films. So the... Maybe they are not connecting the dots with machine learning and so on. But if you explain to them this example in their private life, you will make them understand their professional life, how it works.

Adel Nehme:

I couldn't agree more, especially on connecting. The way data is around us in our daily life, in our personal life, data literacy is something we all engage in. And in a lot of ways, carrying that over to the professional life is a great message. You mentioned in our conversation is you can't be a chief data officer that just sits in the head office where you need to expect things to happen. You need to talk to the organization, you need to talk to the people within the organization. Maybe walk us through what advice would you give chief data officers when they're trying to engage with the rest of the organization? And trying to build that momentum for a data culture and a data mindset

Jean-Pierre Pélicier:

So what we've done at NG is that we have built a network. It's very important to build the network because of course, even if you cannot sit all day in the head office, you can be everywhere at the same time. So you need to have relays. And one of the very first things that we did is to build a community. And to grow the community afterwards. And so that means yes, working the data officers in the first place should start evangelizing, but trying to identify people that has this attractiveness to work for the data space and that will become data champions or local data officers sometimes. So one of the work that... That was, I haven't done it for NG, because I arrived only six months ago. But prior to my arrival, that network and that community was built over the last three years. And today we have a network of 40 data officers at NG and almost 2000 people, which are let's say data friends. We call them data friends, meaning that they are people that have that... in one way or another in their job have an important data component. They are maybe data owners or they use some local tools like Power BI or Data IQ and they are part of our network. And that's, you see, after three years of work, it's about 2000 people that we have. So, and that network after has to be, we are animating this network with monthly webinars and with some events like Data Science Challenge and some hackathons. So yes, there is a global community program around data that has been built at NG and I would really encourage any data officers of a global organization to have that you would demultiply your energy in the company.

Adel Nehme:

I couldn't agree more. This is really great insight. I definitely agree with the importance of creating a learning culture, the importance of creating an evangelist network within the organization. As you mentioned here, data friends, this is something that we've seen also data come for business customers use quite a lot as a tactic to engage the wider community. As we close out our conversation Jean-Pierre, what I'd love to learn from you, as a data leader for many years now in multiple industries, driving value in organizations in this space, whether manufacturing, energy, et cetera. What would be your advice for leaders looking to gain an impact within the first six months on the job, within the first year? How would you structure that approach?

Jean-Pierre Pélicier:

Yeah, I would say that now we are in the phase where data is not just in the innovation phase. I mean, let's say that what I've seen is that in the years 2000, 2010s, digital and data were very often linked to innovation. And it was like, oh, let's be innovative and let's create a digital product. In the Now in the years 2020s, we have gone out of the innovation space. Data is now a mainstream tool or a mainstream department that needs to be structured as professionally as I would say an HR department or quality

Adel Nehme:

Finance or any, yeah.

Jean-Pierre Pélicier:

or finance. Exactly, data has to be a full-fledged department in your company from now on. Because if you don't do that, you will never unlock all the value of data at the level and the scale that you need to. And so to do that, at least I share the way I've done it for NG, it's to go very quickly in building a roadmap to show how you will deliver business value with data. Because this is now what companies expect. It's no longer, again, innovation. It's business value. And your executive committees, they will want to see how data contributes to the growth or the EBIT growth. And you have to build a roadmap and a framework of actions around that. And once you have that, you can start this North Star of how do you create business value, then you can have various flavors of actions like how do I govern the data so that the quality is strong enough to create business value. How is my infrastructure ecosystem going? Is it user-friendly enough? Is it flexible enough so that anyone in the company can have access to it. And also in the culture part, how do I engage all the company, all my employees in the company to focus them on business value creation with data? So my feedback would say that, again, it's never forget this North Star of Data is now here to create business value. It's not just an innovation or communication matter. So it has to be structured in a very professional way. And that's why you need governance program, culture program and infrastructure program. And even in the end, a strategy or data strategy that matches the global strategic objective of your company.

Adel Nehme:

That's really great insight Jean-Pierre. And maybe as we close out, the final thing, one thing that you mentioned here that is key is choosing the right use cases that deliver business value, right? You know, if you're a chief data officer that just joined a new organization, how do you prioritize those business use cases? What are low hanging fruits? What makes a low hanging fruit of a business use case? And what makes something, what makes a use case that is a more long-term strategic objective? How do you prioritize and make the trade off between both?

Jean-Pierre Pélicier:

As a data officer, you need to speak to other C-level people and that across the organization. You cannot speak only to the IT family or IT and digital family because you need to have the attention of people that have the money to invest. And to invest big in projects that will generate return on investments and that will help. the PNL of their entity. So a lot of things that you do during the first month, it's to go to speak with C-level people and their teams and their executive teams to understand what are the real strategic objectives, what are the real pain points of the company. And even if it's not in the digital part, you have to work in the digital and data team. framing projects or products that will help solve those pain points or achieve those business objectives. So it's really spending a lot of time in talking with your business partners, because I know that sometimes when you arrive as a data officer, you can be drawn in meetings with your fellow colleagues of IT or with vendors or with that or with the... the digital and data ecosystem, I would say. But be careful of not being disconnected from the real business objectives of your fellow colleagues in the business organizations.

Adel Nehme:

I couldn't agree more. Now, as we close out our episode, Jean-Pierre, do you have any final words or call to action to share with our audience today?

Jean-Pierre Pélicier:

I mean, we live in a... First, I would like to thank you, Adel,

Adel Nehme:

-Thank you as well!

Jean-Pierre Pélicier:

for inviting me and giving me this opportunity to voice this. I mean, we live in a so exciting era regarding data and digital, where you have innovation in the data space, meaning here I say innovation in the field of systems that can help you. build data products or data solutions in such an easy and powerful way. Quantum computing is on the verge of happening as well. We live in an era where we live these data revolutions. And in your organizations, for the people who listen to us, I'm sure that you are witnessing that. Don't be afraid to go and... Act boldly, trying to deploy the most massive energy that you can have in your space with your community. It takes a lot of energy to deploy a change culture and to make people savvy with data, but it's so interesting and it creates so much value that it makes all this journey very rewarding.

Adel Nehme:

That is very well said Jean-Pierre. Thank you so much for coming on DataFramed. Really appreciate your insights.

Jean-Pierre Pélicier:

Thank you very much Adel and very honored to have been here with you today.

Adel Nehme:

Likewise.