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How to navigate the complexity of enterprise data supply chains
In this episode of the EY Microsoft Tech Directions podcast, we discover how advanced analytics, AI and a data-first strategy are transforming finance, accounting and assurance services at an enterprise scale.
Host Susannah Streeter sits down with two trailblazers shaping the future of enterprise data: Michel Porter, EY Partner and Global Assurance Analytics Leader, and Arun Ulag, Microsoft Corporate Vice President, Azure Data. Together, they explore how a robust data-supply-chain approach—encompassing advanced analytics, artificial intelligence (AI) and data science—is revolutionizing finance, accounting, and assurance services.
From harnessing billions of records across global organizations to integrating low-code solutions and enhanced security, Michel and Arun share firsthand insights on collaborating at an unprecedented scale. They reveal how a “data-first” mindset empowers business analysts and IT professionals alike, all while meeting strict regulatory requirements.
Listeners will learn about the cultural shifts and technical breakthroughs that make real-time data exploration possible—even within highly complex, heavily regulated environments. Michel explains how EY’s approach to enterprise analytics creates value for over 100,000 clients, while Arun highlights the innovations in Microsoft Fabric and Azure that enable speed, scalability and seamless security.
Whether you’re curious about the future of AI in structured data or looking to simplify massive data estates, this discussion offers a closer look at the ever-evolving partnership between EY and Microsoft—and how it’s driving innovation across industries.
Key takeaways:
Understand how advanced analytics and AI, combined with a data-first strategy, can transform enterprise finance and assurance processes
Learn why balancing “discipline at the core” with “flexibility at the edge” helps organizations scale data solutions securely and efficiently
Discover how real-time data visualization and collaboration enable deeper insights, faster decision-making, and better client experiences
Gain perspectives on building a data supply chain that anticipates future AI capabilities while meeting today’s regulatory requirements
For your convenience, full text transcript of this podcast is available below.
Susannah Streeter
Hello and welcome to the EY and Microsoft Tech Directions podcast. It's great to have you with us. I'm your host, Susannah Streeter, and in this episode, we're focusing on navigating the complexity of enterprise-level data supply chains with EY Assurance and Microsoft. We're looking at how advanced analytics, data science, AI, over-structured data, and an overall data-first strategy are shaping the future of finance, accounting and Assurance services. And to do this, I'm delighted to say I'm in a double act who've designed and implemented a cutting edge data science supply chain for EY Assurance, which houses some of the largest and complex data sets in the world for use within Assurance services. We're going to hear from them about the enterprise-level focus required to do this, the scale of the technology and to learn from some of the use cases which are pushing the boundaries of new technology. But before I introduce them, please do remember conversations during this podcast should not be relied upon as accounting, legal, investment, nor other professional advice. Listeners must, of course, consult their own advisors.
But now I'm really delighted to welcome Michel Porter, a partner at EY LLC and EY's Global Assurance Analytics leader. Welcome to you. Michel, where are you talking to us from today?
Michel Porter
I'm in San Diego. Great to be here.
Streeter
Great to have you with us. And also, please welcome Arun Ulag, Microsoft Corporate Vice President for Azure Data. Welcome, Arun.
Arun Ulag
It's great to be here Susannah. Thank you so much for having me.
Streeter
So let's get to it. And I want to put the first question to you, Michel. Tell me about the data supply chain and business intelligence needs for EY Assurance right now.
Porter
Yeah. So let's maybe start with why for that question. Because I think it's very complex. Overall, what we see within the market is that companies are getting more complex than ever. And within that, when we're performing Assurance services for all of our clients, we need to have more transparency and insight into clients' processes and data as fast as possible. As we all know, that's a that's a moving target over time. And as the individual vendors and markets are providing more capabilities to us from a data and business intelligence concept, we're able to kind of push the boundaries over what we can actually deploy there. And so if we kind of look at that from the way that an individual company may do that, and then I'll get to our scale here in just a second. Companies have data that sit in a multitude of different locations in a multitude of different formats, and they need to bring that into a lot of times a common layer, and they need to be able to access that data and gain insights or value from that for a variety of purposes.
That could be for financial statement reporting, for controls purposes. For enterprise level value, there could be a whole bunch of different things that they need to do with that data. But luckily for them, they normally just need to do that one time. And so when they build an individual assets or need to extract value from, say, their general ledger or their trial balances or sub-ledgers and whatnot, they really need to design that kind of one time and then go forward and deploy it for the multitude of those purposes. Now, the joy that we have for an EY concept is that we have over 100,000 clients within Assurance. And so we don't need to do that one time. We need to do that level of complexity 100,000 times. And for multiple different data sets of all different sizes and complexities in that process, that can range from 10,000 individual line items up to 10 to 20 billion line items. And so what we need to do is we need to take the individual data from our clients, bring it into our EY ecosystem, make sure that all of that data is classified and structured in such a way to where we can consume it through the exact same business intelligence patterns over and over and over.
And so what this means is that at scale, we have upwards of 500,000 plus data sets that we need to maintain and own simultaneously and need to be able to utilize those across our Assurance capabilities and primarily audits as well in that process to extract value from those and gain insights into client processes. And the one thing that I would highlight that is a little bit different is, you know, we're not the only company in the world that needs to perform analytics or consume data, but the way that we need to do that is very, very different. A lot of times within the market, when companies think about we're going to use analytics or automation, they're thinking about citizen developer, fairly simplistic business intelligence dashboards that are maybe used for management reporting or C-suite reporting type concepts. Ours is a little bit different. We're fundamentally building investigation platforms. And that brings with it a whole host of different complexities that Arun has been at the forefront with me in our partnership in terms of solving over the years.
But it starts to bring complexities in that we need to be able to operate with billions of records for one company at a sub second speed. We need to be able to ensure that the totality of all of those data sets are consistently secure. We need to make sure that it is always in line with what our regulators expect. And so, you know, the sheer size and scale of that supply chain and business intelligence needs is really where our complexity comes in. And then in the future, we're very much so looking forward to things that we can do from benchmarking and implementation of AI and other concepts as well.
Streeter
Complexity certainly is the key word here, isn't it? I mean, I'm going to hear from Arun in a moment, but first of all, Michel, before I talk to Arun, just tell me a little bit about the alliance and relationship with Microsoft from your point of view. How is Microsoft helping EY Assurance push the boundaries in terms of enterprise scale?
Porter
Yeah. So, you know, I took over this role a couple of years ago in one of the first things that we talked about internally at EY was, you know, we need a vendor, a partner, an alliance that is up for the challenge of the complexity that I just mentioned. And the sheer size of the datasets, the compute necessity, the concurrence of multiple different users, all doing it simultaneously, means that that's a very large challenge. So, you know, when I think about Microsoft's role in that process, you know, they're not just necessarily an alliance partner, but it is very much so a partnership at the development level. And we have come to them with challenges over the years that fundamentally have not been solved in the market. And we have teamed with them to make sure that that at our enterprise scale, they can help us solve some of those more complex problems. And that's most definitely not a finish line that we have crossed. That is an iterative process over time to where we're consistently challenging them, and they're consistently rising to that challenge. And overall, that's allowed us to to achieve many of the things that we've done so far.
Streeter
Yeah. So clearly, as Michel was saying, Arun, you were up for the challenge, but can you reflect on your experience with what Michel and EY Assurance are building?
Ulag
Yeah, absolutely. Susannah. First of all, the partnership has been really exciting for us because the scale at which EY operates is just stunning. Michel called out some of these numbers, but 100,000 plus customers, 500,000 plus data sets. Each data set could be hundreds of thousands of rows or billions of rows. So you're talking about massive amounts of data, but we're also talking about the fact that each customer that EY deals with is completely different, right, so they're dealing with a massive variety of data formats, data types, some clean, some not clean. So we see a variety that frankly, nobody else sees because you're seeing the data that comes from 100,000 different customers. And, you know, with this kind of scale, this kind of complexity, you also need to take security very, very seriously, right? You need clear boundaries of isolation from one customer to another so that the data never mixes with each other. You also need to be in a position where you're very precise about who gets access to work with which data, and these boundaries need to be consistently enforced.
So it's been really exciting to see the scale of the challenge that EY has taken on. What's also been interesting is that at this scale, you need great capabilities for professional developers, which obviously we need to provide. But that's not enough. It's not enough because a lot of the folks that work with EY's customers on the EY side are not professional developers. They're business analysts. They're business people, right. So you need to provide low code tooling that helps them be very, very productive as well. So it's a combination of what is IT led with a strong foundation but also business enabled where business people can work with the data. Big changes. They evolve it, you know. Merge the data together. Cleanse it using no code tools. So it's really been a fascinating partnership because EY is pushing the boundary of how we can work with data, get insights, evolve the use cases and provide value back. So it's really been a highlight for me working with EY, working with Michel, pushing the boundaries of what the products are capable of, and really enabling a whole bunch of scenarios for customers that were never possible before.
Streeter
And I imagine Arun the relationship with EY Assurance really has evolved over time, given the complexity.
It definitely has. And we work with lots of customers. And just to give you a sense, my team and Microsoft, we work with, you know, we have about something like 600 or 700,000 customers in total. But some of our customers are very special, like EY, because they're deeply involved in that design process. So, as you know, Michel and I have been working together for many years, but, you know, the partnership with EY is at multiple levels because we want to learn from EY's use cases. We want to want to learn where our products fall short, and we want EY's fingerprints in helping us build the capabilities that are useful for EY, but also advances the state of the art of the industry that lots of other customers can benefit. So we've really been excited about the partnership because it allows us to have a trusted relationship, allows EY to influence the product roadmap and get us to a place in which all of our customers benefit.
Streeter
And, Michel, so how would you describe the synergies between you both?
Porter
I think it's been a very challenging journey over the past couple of years. But Microsoft has been there every step of the way. And, you know, I think that Arun and I first got introduced to each other two years ago, and I think that was because I was working with some members of his team prior to that for a couple of years before and eventually some of the things that we were asking for needed to actually be added to the roadmap of what Microsoft needed to develop, because a lot of the capabilities at that time were quite centric in relation to citizen developers, and some levels of complexity that we were bringing to that hadn't really been contemplated at the scale that we needed them at. And so, you know, some people within Microsoft say, hey, you should meet Arun and you should talk to him about the things that you want. And I think that that has very much so evolved and blossomed over the past couple of years to a very much so trusted relationship to where, you know, the first couple of years we were working together, the amount of times that I was literally told I was crazy with what I wanted was vast, right?
And in a joking way, obviously, right. But that's just due to some of the complexity that we brought up there. But, you know, I think that we've seen that relationship evolve to where when EY asks for something, it's taken very seriously by Microsoft. They know that if we are asking for something that it has been fully vetted on our side, there is a clear business rationale as to why it is needed. And it's not just some pie in the sky thought of, you know, it'd be nice if we could do this type deal. And so as I look at the way that that that's evolved over the past couple of years, the best way I can describe it is it's kind of been this this constant competition and one upping type scenario to where, you know, I think we initially came to to Microsoft and said, well, we need all of these things and kind of brought a level of complexity that needed to be added to the roadmap. And then Arun and his team consistently come in and say, okay, well, we saw where you wanted to go with that. And we took it one degree further and we actually built in all this extra stuff as well. And I'm like, great. You gave me more tools and toys to play with. I'm going to go test those things out. And then we start to build those into our product. And then we find, well, actually, with this new tooling and new capabilities that you've given us, we would actually like to push the envelope further. And now we want to do this, and we want to do this, and we want to do this. And then we go back to them and then they add it in. And you know, this is a very much cyclical process to where, you know, when I talk to a lot of our leadership internally, I view it as a very bidirectional value-based relationship to where we are fundamentally making Microsoft's products better. And they are enabling us in their core platforms and functionalities that they're providing to build the best in class technology amongst the major financial services firms.
Streeter
Sounds to me Arun that you're a demanding problem solver. Would you say that's a fair description?
Ulag
And all the demanding part is attributable to Michel and I do the problem solving. But no. But in all seriousness, I do think that data is a competitive advantage. It's a data. It's a competitive advantage for EY. It's a competitive advantage for EY's customers and providing the platform stack with Microsoft Fabric, with Power BI, with Azure AI Foundry, you know, that enables EY and EY's customers to leverage this enormous trove of data so that it translates into competitive advantage so that they can go win in markets and they can reduce expenses. They can move forward more quickly. It really is what the opportunity is. And it's been just fascinating to have a demanding customer that really pushes the boundaries of what products can do so that everybody wins.
Streeter
So, let's focus on Microsoft Fabric and Microsoft Azure. In what way are they really integral to these current needs. But what is the future trajectory?
Ulag
You know, if I think about how the world is evolving, you know, everybody recognizes that AI is fundamentally transformative. And however, AI is only as good as your data. You know the best AI models, if you put garbage in, you're most likely going to get garbage out. So it's become incredibly important for customers to get their data estate ready for AI. It's a very challenging problem because, you know, I always like to say there's good news and bad news. The good news is there's been a lot of innovation in the data and AI space. The bad news is there's been a lot of innovation in the data and AI space because it has created so much complexity. There are literally thousands of products and technologies out there that in some way, shape or form are vying for customers' attention to help with this data and AI problem. And the opportunity we saw for Microsoft and for our customers is really to simplify everything. And the approach we took. If I were to create an analogy, there was a time when Microsoft was selling Microsoft Word for document authoring and PowerPoint for presentations and Excel and so on.
But then we realized that the opportunity really was to improve productivity through Office, you know. So we stopped worrying about these individual products and we really thought about the entire productivity experience. And that's what created Office. And we've taken the data stack in a very similar direction with Microsoft Fabric. We've brought together the core capabilities that most organizations need, whether that's data integration or data engineering or data warehousing or data science and machine learning or business intelligence, but brought them into a single unified product, just like Office brought all of these capabilities together. And we also introduced something called One Lake, which is, you know, the OneDrive for data to make data lakes really usable. But all of this translates to business value, because it means that every employee at EY, whether they're an IT professional or a self-service business analyst, can get value from the platform that they're working on, an integrated platform. They can bring their data together. They can get to the AI value or the BI value that pushes EY forward or EY's customers forward without having to worry about, you know, splitting up lots of services, integrating things together, tinkering with bits and bytes. That's a waste of time. They should really focus on business value. So that's kind of how we thought about the problem. And that's why we brought Microsoft Fabric to market. And it's really been exciting to partner with EY and Michel and team to be able to, you know, get after the use cases that are really transformative.
Streeter
And how does this all affect Michel, this need for enterprise-level focus? We hear a lot about this. But what does it mean in practice?
Porter
I think one of the core values that Arun just mentioned there is, you know, if I again, if I look at our trajectory over the past couple of years together and specifically before Fabric versus where Fabric is and where it's headed, the analogy when I address sometime, you know, a ruins population in a town hall was I said, you know, Microsoft historically did a very good job of providing me the materials I needed to be a successful artist. They said, here's your paintbrush. Oh, and here's your paint and here's your canvas. But whether or not I painted the Mona Lisa or whether or not I painted a stick figure, it was totally up to me. And I think what what they have done in relation to Fabric is they said, okay, well, here's actually the way that all of those tools fit together to paint the best possible picture that you can, right. And that has been tremendously beneficial to us because before a lot of the ownership was on the companies themselves and us to build the pieces in a very integral way and hope that we put them together correctly.
And stringing all of these different types of assets together across the Azure stack and across the Fabric stack now has kind of made that a much more seamless experience to where we can get directly into solving problems as compared to saying, are we putting all putting all this together correctly? And so, you know, I think as that translates to enterprise level, we're consistently seeing that again together we're kind of raising the stakes of what is that definition. And so, you know, if I translate that to what we actually need to do in the field right now, and then I'll come back to that scale, I need to be able to sit with a client when we're giving our Assurance services. And let's take an audit as an example. I need to be able to sit with them. That could be their C-suite. That could be various levels of management. It could be directors, it could be any person in that process. And I need to be able to have their data up and be able to show them what I am seeing live and have that be highly interactable, and to have an actual discussion regarding what we're seeing in their data.
That is only possible when you have things like Fabric built. That is only possible when you have all of the underlying pieces put together correctly, but it enables a dialogue with end clients to where we can get to that answer much faster and get to the right questions much faster, and allows us to give much more kind of quality procedures. But then it becomes to an enterprise level, that's not that hard to do if you need to do it one time with one data set and one deployment, if you need to enable 120,000 plus professionals globally to be able to do that on demand across all the time zones, across all the geos, and across all the different data centers and clients that we have. That's a very challenging task. Specifically, if you start thinking about the size of data that we're talking about, not just the volume of data sets, to sit down and have a dialogue with a client like that, you have to have things operating in sub-second speed. You know, the way that technology is working right now, if anything takes longer than three seconds in order to operate well, this must be broken.
And so, you know, you're going to think it's either broken or you're going to call IT or, you know, you know, you're going to return it or whatever that is. And, you know, our practitioners and our clients view it the same way. They don't care if their data sets are billions of records or if it's a thousand records. They expect the same performance. And behind the scenes, that creates all sorts of challenges that Fabric is, is solving and is going to continue to solve in the future for us. But just to call out, you know, a couple of those examples that we've kind of solved together over the years. Even if you get down to an individual query that runs within Power BI, historically, there were memory per query limits. And I came to Arun's team, and I said, that's great, except for this limit. I'm trying to apply against a multi-billion record data set to have this discussion live with a client. And we're running out of resources. What can we do about this?
So we worked together for about 6 to 12 months, and they came back to me, which I really appreciated as a customer. And they said, what do you want? How would you actually design it? How would like how much do you need? What do you actually need to do in the interaction as compared to just saying, okay, we increased it for you. So they showed that they very much wanted to understand my problem and come up with a solution that actually solved it in a team environment as compared to just saying no, that's just another customer request and we'll put it into the backlog, right. And so we were actually able to increase that limit by 4X compared to what it was historically. Another example in that process is fundamental API limits. Very challenging when you have a platform such as Fabric that Microsoft has to be able to build and maintain of just how many API calls are you going to have? What's your level of concurrency? How many people are people are actually going to do these actions?
And, you know, they've been very helpful to us in that process because we say, okay, well, we see where your limit is at, but I also need 50X that size. And again, that's where it comes back to this very iterative design process, where we are fundamentally building IP on top of their innovation. So it's kind of a dual innovation, if you will. They innovate. And then we get to innovate on top of that, which allows us to bring some of these things to market for our clients. And that's what really creates a transformative experience for us within the market.
Streeter
Well, it certainly sounds like that. Let me bring back Arun. Arun, clearly, we've seen all these challenges about the design and application of Microsoft Fabric and Azure. Tell me a little bit more about some of the use cases in the finance and accounting sector. How are they challenging all of this design?
Ulag
Absolutely, Susannah. You know, the interesting thing about finance and accounting data is it's the lifeblood of the company. You know, everything depends on it. Everything from how you pay your sellers to how you measure profitability to what you report back to Wall Street. And the way we think about this is that we think about a paradigm where we talk about discipline at the core and flexibility at the edge. And let me tell you what that means. Discipline at the core basically means that you have a set of reliable data products that everybody can use. It might be a core finance data. It might be a core sales data. It might be a core marketing data, your supply chain data, etc. but these data products built by professional teams can be widely used across the company. But if that's all you did, it's not good enough. Because eventually business people need flexibility as well. They need to be able to do their own analysis. And so we combine this discipline at the core with what we refer to as flexibility at the edge, giving no-code tools to business people so that they can do some of their own analysis without having to bother the central IT teams that produce the corporate data products.
So, what we think is a core organizing paradigm that allows customers to scale. And a lot of these capabilities are built into Fabric. Another one that I wanted to call out is just Excel. Finance and accounting people absolutely love Excel. I love Excel; you know, Excel continues to grow even as the world becomes more data-driven. But it also has its challenges because if you export the data out of your data stack into Excel, then the connection is broken. You know, the data is no longer fresh. It can grow stale, your permissions are broken like you can send it to people who shouldn't have access. So we did a lot of work jointly between Fabric and the Excel team to make sure that Fabric is just built into Excel. Excel discovers the data artifacts that are shared with that business user through Fabric, and you can build pivot tables. It now acts as a canvas on top of Fabric. And the beautiful thing about this is that your permissions now flow seamlessly, so you can only see as a business user what you're allowed to see. You know, your row-level security or column-level security. Everything just applies in Excel. But it even goes further because each time I open Excel, the data is always fresh. The other opportunity that we see is really the opportunity to enable business people to just be able to chat with their data, to be able to ask business questions and have Fabric and Power BI figure out how to answer the question so that they can get the answers they're looking for without having to drill through data or work too hard to get to the outcome. And this is one that we see a lot of opportunity because, you know, we have to be able to empower business at scale with trusted data that they can just ask questions and get it answered. And, you know, while it's easy to do a quick demo, it's hard to do it in real life because one of the critical things about Power BI and Fabric and Excel is that we can't be wrong. We have to be right because people take what they see in Fabric, what they see in Power BI and go make business decisions. So these are some of the areas where we see enormous opportunity with, you know, especially in the finance and accounting sector, with the critical data that is literally the backbone of pretty much every organization.
Streeter
A lot of opportunity ahead. So Michel, what are some of the most exciting examples you've come across? I mean, you've talked about what it's like being in front of clients, showing them in real-time. But how else is this cutting-edge data supply chain really revolutionizing the client experience?
Porter
Well, I can verify two of the things that Arun just said, and that is that they do have to be right 100% of the time because we are a very highly regulated environment, and we are reliant upon them being accurate all the time. And that's a large burden that we kind of take on together. And the second one is that accountants and finance people do love Excel. And so, you know, I think that Excel integration and the ability to really utilize your underlying data for a myriad of purposes has really been kind of at the core of some of the exciting examples that we've been able to bring to market. So, you know, if you think about our dialogues over the years, a lot of people think about business intelligence tools, with Power BI being the end state of that supply chain of, you know, you've gotten the data, you've gotten it into the right spot. I now see it in an analytics interface. I can interact with it and that's the end of it. Now I take that value and I go make a decision somewhere else.
And so one of the things, you know, that we very much have worked with Arun and his team on is that Power BI sits right in the middle of our supply chain, not at the end of it. And because people within accounting, finance and specifically our Assurance services do love Excel. Excel is actually the documentation location of choice for a lot of our core conclusion for Assurance services. And so we actually have to be able to seamlessly move information from Power BI into downstream applications, which are inclusive of Microsoft Excel, are also inclusive of PDFs, are also inclusive of integration into some of our other platforms, such as such as EY Canvas. And then it could also be integration into other downstream assets that need to take that underlying data set and perform additional actions coming off of it, right. And so by exposing kind of that underlying data layer for multiple purposes and providing us lots of APIs in that process, I can fundamentally use, you know, the technology that Arun is providing us as a data store and not just a business intelligence endpoint.
And that is very much so transformative to us because it allows me to dual purpose multiple of the things that, as an example, that our auditors need because an auditor may need to, yes, investigate something that's sitting in Power BI, but they may also know the exact way that their data needs to look, and they may need automation coming off that process that automatically translates to a work paper. I can give them both simultaneously. And why is that? Because in things that Microsoft Fabric is providing, I can see the underlying data source and I can have multiple different work streams coming off of that simultaneously. And these are the types of things that can optimize the overall supply chain and really get us to the answer in documentation substantially faster. So, you know, I did talk a lot about the experience in terms of sitting and working with clients. But, you know, even without the clients involved, that underlying data supply chain and data access layer provides us immense value to get things into the into the actual state that we need to be able to conclude.
Streeter
How would you describe being right at the heart of the EY Microsoft Alliance? I mean, specifically for EY Assurance. What's it been like?
Porter
Well, I'm in my 14th year. I'm aging myself a little bit with EY. EY is the only company that I've ever worked with as well. I'm not an engineer by trade. I'm a CPA. I very much grew up in our in our audit practice and in our Assurance practice. And I kind of, for lack of a better term, fell into the realm of engineering just due to the fact that I saw a lot of these problems that we needed to solve in our day-to-day business requirements, right? So, I started to pick up a lot of these business intelligence tools, analytics process automation tools, and basically big data tools in general to solve some of these problems for our largest clients. And that led to, you know, this kind of Microsoft alliance. So if you would have asked me, you know, when I first started at EY that, hey, someday you're going to be on a podcast with, you know, the executive vice president of Azure. I would be like, what are you talking about? I'm probably going to be working in auditor or accounting or finance somewhere. But that most definitely was not in the cards when I first started. But I think that you know, it's been very much so a self-interest and self-taught kind of journey because these are the things that the market is actually eating at the moment and solving these problems faster, more efficiently. And you know why we're always focused on quality first in a quality manner. That's what has really kind of excelled us to the forefront of that. And so this was not by design to clarify, right. But I do think that what I really enjoy is solving problems and solving them effectively and efficiently. This alliance really allows for that to take place at a very large scale to impact change to 100,000 plus people, not to mention all of our clients that are actually benefiting from our services, right? So, it's been an excellent journey over that time. On a daily basis, I have access and Arun has provided this to me, to his product leads to his core lieutenants, to people that are actually designing the features. I have direct access to the engineering teams, and if I have a question about a new product that comes up, within an hour I have a call on my schedule with that product team to talk through the individual intricacies, and that would take place within a couple days. So that level of access, I think, is kind of unparalleled. And what really enables this partnership to kind of be propelled forward? Right. So, long story short, it's excellent, and it's a lot of fun at the moment.
Streeter
You certainly are a problem solver double act. But I mean, Michel, your passion really does shine through as you're talking about your involvement over time. And Arun, how has this relationship and alliance really had an impact on your thought process and strategy and even your career?
Ulag
Yeah, it's been exceptional. Let me start off with that. You know, we talked a lot about the partnership between Michel and myself, the teams and so on, and that's been absolutely fabulous. I'm grateful for the partnership and the fingerprints that EY has left on our product. I wanted to take a slightly different angle. You know, there's tens of thousands of employees now at EY. I think we're getting close to 100,000 at this point. Who are users of Power BI and Fabric today. While it's easy for us to listen to Michel. We could talk to him and his folks. One of the things that we have done with Fabric and Power BI is really listen to everybody at EY that uses our products. And, you know, there's a public website, it's called Ideas or Fabric at Microsoft.com. So any person in EY that's using Power BI or Fabric can go and tell us what ideas that they want us to go build. They don't have to necessarily call Michel. They can literally go to our public website and create new ideas or vote on each of those ideas. And, you know, I was just pulling up some of the data before this meeting, and I was blown away to find out that I had over 3300 ideas that came from employees at EY who use Fabric and Power BI. 3300 ideas. Okay. And there are tens of thousands of votes across these ideas. And I was like, hey, how many of these ideas have we already shipped? Okay. And it turns out we've already shipped features that cover over two thirds of the votes from these employees of the ideas that they submitted. And the reason I'm sharing that is look at how the world has changed. You know, we used to be in a world in which cloud vendors or vendors, software vendors, rolled out software to folks like Michel and others, and then they rolled it out to their end users. And then some things users will love, some things they won't love, and maybe they'll call Michel and tell him, and maybe he'll call our sales guy and tell them, and maybe the sales guy will call me and tell me, and maybe he'll do something about it. Or maybe I won't, and maybe I'll shift something back, and maybe I won't. That was the way the world used to work. And in the way the world works right now, their developers, their users go directly to our website and tell us what they want us to go build. We are constantly listening to them. We are shipping every single week. And you know, Michel and I in many ways don't need to be involved in every one of these feature decisions. And that really warms my heart because it really shows that the collaboration is not just at the leadership level, it's at the grassroots level, and we have massive channels where Microsoft is listening. And the Fabric team is listening, we're learning, we're reacting, and the products are getting better and the customers being more successful. So anyway, that's the part that I feel really is a huge highlight for me because the partnership is not just at the leadership level, but it's at every level of the organization.
Streeter
That passion, that's where your passion lies right now. But I mean, Michel, where is this alliance and innovation going to go from here? We've heard all about all of this collaboration, all of this innovation. What next?
Porter
I think I'll probably hit on it from two separate angles. One is the culture side of it. And then two, where do I want to take the technology? I hope that Arun is accompanying us in that journey. But to circle back to something that he just mentioned about, you know, the broader community at EY consistently providing feedback, that's very much so a cultural change that we have tried to enact over the past couple of years. I think that just as Arun was alluding to, that the historical relationship with vendors and partners and whatnot has been, well, vendors and partners provide us something and therefore we try to use it and hopefully it works. And one thing I very much so have tried to change, specifically associated with the people in my team, when they tell me that something doesn't work, or they tell me that they wish it worked in a certain way. My first question to them is, have you asked Microsoft for that? Have you told them what your limitation is? Have you told them what you want to achieve? Have you clearly documented to them how you want it to work? And if the answer is no, then don't complain to me when it doesn't work. Because culturally, you need to be able to communicate and clearly articulate your ideas to to the companies that that you are partnering with to enable and enact change at a high level, right? And so, to me, I really want to to double down on that kind of cultural frontier, if you will, to make sure that there is a consistent, cyclical and iterative process between EY and Microsoft to make sure that we are getting what we need from them, and that we consistently make their products better in relation to that process.
But from a technology front, I mean, Arun called it out earlier, and I think that a lot of the talk now is around AI. And what I commonly see happen in the marketplace is that many companies jump directly to we're going to implement AI, and you ask them what is your data strategy? And they say, what do you mean, right? And so it's kind of a sprint before you can even crawl type mentality when it comes to structured data and AI. And so what I really want to focus on in the future and what, you know, we're very much so teaming on right now is that they are building in lots of AI capabilities, and that's been at the forefront of the market, and, you know, very well publicized. And I want to make sure that we have the correct intersection point of when those features are ready for us to be able to consume that our data layers and our enterprise data strategy is at a point to where I can immediately implement those and start getting value from it. You know, I talked a lot about we are a very highly regulated industry. We have regulators all throughout the world. There are differing viewpoints in relation to usage of AI. Usage of AI over external client data in a bunch of different views on that. But at some point, AI is going to be at the heart of what is done over structured data. And so where I very much want to focus on in this alliance and kind of innovation going forward is making sure that we have our data layers and underlying architectures in the exact right format at an enterprise scale, so that when it is time to be able to implement these things at scale, we are ready to do so.
Streeter
And we've heard from Michel there. How far do you think advanced analytics and data platforms could take us? How much is AI part of that future?
Porter
It's a huge part, Susannah. We've been investing in it aggressively as Microsoft. We've been investing aggressively as the Azure team. We've been investing aggressively as Fabric. But when I think about the opportunity we have with data, even today in a day in which tools are common, tools are widespread, I would say still more than 95% of the data is still not used by business, you know, because it's still too hard. The opportunity we see there with AI is to really democratize this. You know, to give this to every single businessperson where they can make much better decisions, much better actions, much better choices, a much deeper understanding of the state of their business, a much deeper understanding of where their customers are at, a much deeper understanding of what they need to go do to be able to accelerate their competitive advantage. So we just see enormous opportunity ahead for us to be able to leverage data, leverage AI and really empower every single business person on the planet.
Streeter
And just as a final thought, what would your key takeaway be, Arun, for anyone listening to this podcast? What should they leave with front of mind?
Ulag
I do think that it is just a sense of the opportunity ahead. You know, we are at such a pivotal time in the industry where AI and data are now truly transformative to what people can do. And I would say in many ways, you know, we're still early. There's still more problems that are unsolved than problems that are solved today. So I would encourage everybody to get curious to try things out, to get their fingers dirty. It's not as hard as it used to be. So I would just say be curious, get involved, try things out because there's just massive opportunity ahead.
Streeter
And Michel.
Porter
I would just echo everything that Arun just said. And I think what is what is very exciting for me and what I would want everyone to take away, is a final thought, is that sometimes leadership within various companies and various different industries think about the data platform, data strategy and AI journey as if it has a finish line, right? And it doesn't. It is an iterative and cyclical process that will continue to be evolved over time, and I very much so look forward to continuing to solve very complex problems for EY Assurance and broader EY in general, and making sure that our EY data strategy and further consumption of that data continues to be at the at the forefront of what the market can offer at the moment.
Streeter
Well, thank you so much for those final thoughts on curiosity and problem-solving. It's been a really fascinating discussion. Some super useful insights and how to navigate these really complex data supply chains with advanced analytics and data science. So thank you so much for your time Michel and Arun.
Ulag
Appreciate it.
Porter
Thank you very much.
Streeter
And a quick note from the legal team. The views of third parties set out in this podcast are not necessarily the views of the global organization nor its member firms. Moreover, they should be seen in the context of the time in which they were made.
I'm Susannah Streeter, I hope you'll join me again for the next edition of the EY and Microsoft Tech Directions podcast. Together, EY and Microsoft empower organizations to create exceptional experiences that help the world work better and achieve more.
