Guild Contributor:

Patricia Carvalho, Solvay

Patricia Carvalho, Solvay

Group Head of Data and Analytics, Solvay

One person can only be humble enough to acknowledge that we know very little; what's important is to be able to understand what we do not know.

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Laura Bineviciute, Head of Community Data Leaders

Patricia Carvalho, Group Head of Data and Analytics, Solvay

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Laura Bineviciute: Today I am joined by Patricia Carvalho, Head of Data and Analytics at Solvay. Hello Patricia.

Patricia Carvalho: Hello Laura, how are you doing?

Laura Bineviciute: I'm very well. I'm very pleased to have you joining us and I look forward to discussing your role at Solvay, as well as just the role of someone who leads data and analytics overall. So, let's dive straight in. Your role at Solvay, what is it exactly that you're responsible for and what does data and analytics look like in your organisation?

Patricia Carvalho: That's a big question and I'll try to be succinct.

So, I am indeed responsible for the data analytics function at Solvay Group. The key scope we have is to ensure that we drive the data agenda. Data is very important as a general asset to the whole organisation, even without going into the worlds of artificial intelligence, which I know is also a key part of the discussion here today. But first and foremost it’s the responsibility of ensuring we enable the business with the highest quality and highest availability of data. So, that's definitely a big one for us.

The second one is enabling the business with anything, encompassing small, simple reporting to the most complex solutions and analytics that we can do using traditional AI, generative AI and so on and so forth. So overall, that's the key scope.

We also, of course, are responsible for educating the business with regards to what all of this means, because it's quite important to be very connected with the business, understanding what they want, but also educating them on what they need to do next.

It's not their job to understand data analytics from the mathematical and the algorithms perspective, but it's important for them to understand a little bit what we are talking about when we're trying to answer the questions. So that educational piece, as well as trying to bring in new trends as to what is going on out there.

Laura Bineviciute: We started with a broad question, and your responsibility sounds quite broad as the role of a CDO or Head of Data and Analytics would suggest. So, very interesting. Thank you for explaining that.

What do you feel presents the biggest opportunity, not just for you and your role, but more broadly for CDO's around the world in the next, let's say, twelve months.

Patricia Carvalho: All in all, I think it's a very exciting area to be in. And this is not because I'm in it. It's truly because anyone you talk to, business oriented, non-business orientated will want to know more about digital, you know, either because of technologies, infrastructure or the data, the analytical part of it.

I think the big challenge is, of course, to be able to make decisions, because you cannot go into every single piece. You need to be able to look into how quickly you can take benefit of some of these new technologies or new trends.

But at the same time, you've got a business to run, you've got costs associated to it, and you need to think about how that's going to bring some long-term value to the organisation as well. So those pieces around short term and long term are where I believe CDOss will have a very interesting role to play in their organisations.

Laura Bineviciute: That's a very interesting take on it, or perhaps a very interesting point that you have highlighted balancing the short term and long term. What the majority of people in the industry would see today is that many businesses are jumping on the hype of Gen AI but CDOs also need to deliver longer term projects that will deliver value without necessarily using Gen AI.

How do you manage those priorities between short term and long term?

Patricia Carvalho: You cannot just be focused on your belly button, particularly on these things that are moving, you know, every second. So, the idea is to be able to look at it a little bit from a pragmatic perspective in terms of what quick wins we can get out of some of this – and I'm talking about quick improvements in productivity or certain tasks where you could bring in Gen AI and support the different teams in a very pragmatic and almost immediate way.

There are several Gen AI applications that could definitely reduce some of the less interesting or maybe more time-consuming tasks. But then also looking at it from the perspective of, if we are doing that, then there's of course an opportunity for those people to release some of their time to think about what Gen AI can bring in the long term.

So, you need to take a little bit of the benefit from one side: - how can we apply Gen AI in the immediate term? Obviously, the cost element is important, but then how do you use the talent? You need to look at the people and think about how they are going to use the time to think more holistically about how things are operating around them. So that's one element: talent, okay? And making sure that you don't focus just on technology, you focus on the people, on the brains that you already have because they'll give you a lot of input in terms of what could be the next steps.

And then on top of that, of course, you need to think about some of these different implementations you're going to do, how they are going to bring new ideas because people now have more time to think about things they didn’t consider before, and now you can start thinking more around what can we do next? What are the different challenges that we can face that we can potentially predict?

I think that's a sweet spot that every company wants to be in. Obviously, this is very theoretical. I think the challenge is really, that everybody's asking for immediate impacts. Of course, everyone wants to increase their productivity across teams and there's certain things that you can actually implement quite broadly. So those immediate benefits are quite important.

At the moment, we're looking at the tools that can bring those benefits and trying to understand what capabilities we have that can answer the immediate impacts.

But we also want to understand how much those roadmaps are orientated to the future, and can help us not just, for example, in the labs with the different formulae and different documentation, but also potentially in an HR function.

Procurement is another one that could be quite easily implemented. Legal is starting to talk about how to evolve its use. I think that's how you can get a sensible balance. I hope that helps. Laura.

Laura Bineviciute: It certainly helps, and it also just showcases how much there is to think about. And you are certainly not alone. Many CDOs are in a very similar position, and I think many organisations are just trying to navigate short long-term gains and the hype versus what actually can help the businesses in a long term, tangible way that can be calculated.

If we are to summarise what you just said, how would you say Solvay is embracing AI today?

Patricia Carvalho: Very well. They're embracing it from all fronts. In Solvay, I think we don't have any issues with business and the digital teams understanding the benefits of AI. Obviously, there's always space for us to educate a little bit more and that's something I'm really keen on.

I’ve started but there's always more work to be done to ensure that we educate people. On one side, we’ve got traditional AI that is already answering a lot of the questions that we have, for example, sustainable applications for sales where you want to be able to not just predict the demand for the coming years, but you also make sure that you optimise inventories.

All of that, traditional AI is already dealing with and that's something that people need to be reminded of because it's very easy for them to forget and think, “oh, now we have Gen AI, it will answer all the questions. This is the silver bullet,” when in fact, you know it will not be.

And this is something that's happening in Solvay and I'm assuming in many other businesses. We'll have the same challenge of managing expectations and educating people on where we should use generative AI versus the traditional techniques that we have already in place.

There's another very important element here which is that these tools are available, public instances, that anyone can use. And this poses other risks for organisations, which go beyond my remit, but that I need to make sure that we are fully aligned on in Solvay. So, it's important that we also educate the business to say, “listen, we know the need, we know that you need to take the most benefit out of, ChatGPT, for example, but we need to do it in a compliant way. We need to make sure that we don't put the company at risk.

The enthusiasm is way too big and sometimes that can lead to rushed actions or decisions that we need to be a little bit more conscious about and work across teams to put the right setup together and ensure people know how to use it, for what they can use it, and how to discover all the full capabilities.

The whole piece about education is also very important here.

Laura Bineviciute: Very interesting. I think it’s captured within the theme that we have at Data Leaders Guild which is from AI Tsunami to Augmented Organisation. It really feels that organisations are being hit with a lot and it's just finding varied ways on how you help people with it. And, to your point, don't get to the place where any part of your organisation is put at risk due to poor use of some of the tools that are widely available.

Speaking of the Data Leaders Guild, it is of course our flagship Chief Data Officer, Think Tank, that you're joining us for this year. Why do you think it is important for CDOs and data leaders to connect with each other and exchange in a format like that?

Patricia Carvalho: I'm always adamant that sharing is caring, and this is not just because it's one of those statements out there, but because you do learn a lot with the experience from others.

I've worked in other companies than Solvay and have brought in some knowledge that I- acquired. But one person can only be humble enough to agree, to understand, to acknowledge that we know very little. We know what we know so far. What's important is to be able to understand what we do not know, which becomes even more critical, because you will not be aware of it, you will not be thinking about it. And guess what? Others are going through that at the moment or have gone through that.

So, for me, it's bread and butter in business. We should be talking to each other. In fact, we understand each other very well, I believe, because our agendas are quite similar, and everybody's hit by the same kind of topics and themes. So, I think there's no other way of doing it than just exchange ideas, exchange opinions, exchange experiences.

I'll be honest, I've seen throughout my career that you do learn a lot, but you also give others experience and information that can be quite valuable to them as well. I think that's the most important thing.

Laura Bineviciute: My final question also maps very nicely to what you just said, because I'm going to ask you for a little bit more advice that you can share. The final question has been posed by one of our previous interviewees, Laia Collazos, CDAO at Coca Cola Euro Pacific Partners. And she didn't know that you would be the person answering the question. So it's a complete coincidence.

The question is, what advice would you give to someone who is new to the Chief Data Officer role?

Patricia Carvalho: So, you know, my advice will always be very limited. The first piece of advice I will give is, please speak to as many people as you find relevant. Not just in terms of AI, but in terms of how we enable the business, how do we deal with important assets as data in a company? So we need to understand a little bit where we are. That's the first thing, because despite the fact that everybody's looking at Gen AI, it could well be that where you are and what you need at the moment is not Gen AI, it's something completely different. So, I would listen to others while maintaining a good balance of what we really need as a company.

This will help on a couple of things. In Solvay, I can give you the example of where I am at the moment, which is completely different to where I came from.

We have a baseline to put together, but, of course, we need to make sure that we have as much presence as we possibly can across some of these new topics, because you are seen as the expert, the advisor on artificial intelligence, on technology, on how to proceed when it comes to analytics.

But at the same time, I think it's important that we set up the baseline in Solvay, and the baseline is data, it’s to organise our house, get the businesses aligned in terms of ownership of this data, to have a clean slate so everybody can feast from the banquets of data that we can put together.

So many questions that people are trying to answer with very complex analytics can be given by a simple report as well. So, for me, it’s always trying to simplify as much as we can until the point where we cannot. What sometimes feels like a very complex question most of the times in reality, when we're looking at it, there is a whole simplification to be done to the problem.

Don’t lose sight of what's going on out there but ensure that you know what you need and are able to tell your people, your businesses, and explain to them the reason why we need that and move towards that direction.

Sometimes it can be quite daunting - everybody's pulling you in all directions and but don’t lose your focus. That’s the advice I would give.

Laura Bineviciute: Thank you so much. It's been a great chat, and I look forward to continuing this conversation at the data leaders Guild. Thank you so much, Patricia.

Patricia Carvalho: A pleasure, Laura, thank you so much. See you there.

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