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How to rethink the power of your data and unleash new value with AI
In this episode of the EY Microsoft Tech Directions podcast, we look at how developing broad data strategies is crucial to transforming operations and driving innovation. |
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In this episode of the EY Microsoft Tech Directions podcast, hosted by Susannah Streeter, the focus is on harnessing the power of data and artificial intelligence (AI) to unlock new business value. Joining the conversation are Anna Stamatelatos, Americas Leader for Analytics and Data at Microsoft, and Edward Bobrin, Ernst & Young LLP, leader of Microsoft Data and AI Competency. The episode explores the critical role of data as a strategic asset in today's digital era, emphasizing the necessity of high-quality data and effective governance to derive actionable insights.
They discuss the challenges businesses face with data silos and integration, stressing the importance of a broad data strategy and the use of hyper-connected systems for efficient data management. They highlight how companies can overcome data bias by continuously monitoring data sets and incorporating third-party data for a balanced view.
The discussion showcases real-world examples of AI driving new revenue streams, improving operational efficiency, and help enhancing customer personalization across various sectors, including retail, health care, and manufacturing. Practical strategies for starting on this journey include defining clear use cases, investing in scalable data platforms and leveraging collaborations for knowledge.
Looking ahead, the episode predicts continued advancements in AI and natural language processing, with AI becoming increasingly embedded in everyday business tools. The importance of responsible AI practices, data security, and ongoing employee training is emphasized to help ensure ethical and effective use of AI.
Key takeaways:
- Understand the role of AI in transforming data management and generating actionable insights, emphasizing strategies for overcoming data silos and addressing data quality
- Appreciate AI's potential to create new revenue streams, improve operational efficiency and personalize customer experiences across various industries
- Recognize the importance of training and help empowering workforces to utilize AI effectively, highlighting the shift from routine data management to strategic decision-making
For your convenience, full text transcript of this podcast is available below.
Susannah Streeter
Hello and welcome to the EY Microsoft Tech Directions podcast. I'm Susanna Streeter, and in this episode, we're focusing on how to rethink the power of your data and unleash new value with AI. In today's digital era, data is king. Its role as a value creator has become increasingly pronounced across industries, particularly with rapid advance advances in AI capabilities. Big data has transformed how businesses operate and compete by providing a wealth of insight for decision making for those that know how to manage it as a strategic asset. Businesses are navigating a landscape where data serves as the linchpin for securing competitive advantage and fostering innovation. To unlock its power, we need hyper connected systems and standards to support interoperability. We know that ensuring the quality of the data analysed and translated to insight is absolutely crucial. As data science becomes more important in business operations, leaders are recognising the need to prioritise data governance and ensure they use the right technology to surface the best insights from their rich resources. So on this podcast, we're going to discuss examples of how companies have done this most effectively and highlight how the right strategies, technology and data can be harnessed to drive tangible business outcomes.And I'm I'm very pleased to say I'll be joined by leaders who will be providing lots of insights given their vast experience delivering AI solutions. But before I introduce them, please do remember conversations during this podcast should not be relied on as accounting, legal investment, nor other professional advice. Listeners must, of course, consult their own advisors. But now, I am delighted to welcome Anna Stametalatos, America's leader for analytics and data at Microsoft. Anna, great to have you on the podcast. Where are you today?
Anna Stamatelatos
Thank you so much for having me, speaking to you from Dallas, Texas.Streeter
Fantastic. Great to have you with us. And also, please welcome Edward Bobrin, who leads Microsoft Data and AI Competency at EY. Hello, Edward. Where are you talking to us today from?Ed Bobrin
Hi, thanks for having me. Talking to you from Philadelphia.Streeter
Well, great that you could join us. So I want to start with you, Edward. I mean, what extent do you think companies are data rich but insight poor?Bobrin
Data rich. Everyone's data rich now. There's a proliferation of data being generated by everything from mobile applications, sensors, Imaging vehicles, it's a staggering amount of data that people are inundated with. If you think about how you might translate that to specific industries like financial services or health care or life sciences, where data is changing by the second, it can be overwhelming. To try to bring that data together and synthesise it into meaningful insight is formidable challenge for sure. When you consider there's that massive amount of data, there's a lot of computational resources required to process it and bring it together. There's cost associated with it, obviously. The way they're collected in today's world is very siloed. It makes the data very difficult when you collect it to pull it together in a way that drives the insights you're looking for. It's really important that you, one, pay attention to data quality, have an effective procedure and policy around how you're going to manage that, and also have the governance practises in place for converting that raw data into something that's going to be actionable and bringing value.Streeter
Absolutely. Anna, it's talk there about the massive amounts of data that companies are dealing with and the silos where it can be lost. To what extent would you say that customers really are at varying stages of their journeys here?Stamatelatos
Yeah, for sure. Many enterprises are, and as I had mentioned, just the amount of data that is coming at everyone. Some of the challenges that enterprise face are the disparate data sources coming in, as well as challenges with data silos. Then they're looking at how do they consolidate that, reduce performance constraints, as well as how they're going to govern their data and have efficient data quality in order to gain insights from it.Streeter
Why do you think so many are struggling to find the guidance they need to really use their data most effectively?Stamatelatos
Yeah. Having a data strategy is key for enterprises to remove the silos and really get their enterprise ready for AI. Many enterprises are looking at data lakes as they're forming their data strategy so they can have one location for their data, and then they're able to provide a solution so they meet their business needs and demands without relying on data analysts to go through their data sources, and now they'll be able to generate accuracy of insights for their enterprises.Streeter
Ed, what's your take on this?Bobrin
I'll pile on and add to Anna's observations. I think centralising data in a data lake is certainly a foundational step in simplifying access to the data, but you're probably not going to use all of it. You collect it. We said massive amounts of data, but there's a strategy of how that data should be evaluated and transformed and modelled into something that's usable. That's typically where you're going to be looking at specific business use cases that will help drive a specific outcome. Now, the guidance in which of the types of data I need to pull from the lake to develop a model to actually deliver the value.Streeter
What would you say is contributing to data bias? How can companies compensate for this, Ed?Bobrin
Yeah, recognising data bias is critical when you're trying to really validate any of the insight you're using and leveraging for an outcome. For example, in healthcare systems, specifically, where they have inherent bias based on the demographics of the patient population for which the system serves. It's sampling bias. They work in a big city. There's a certain demographic. There are certain illnesses that are targeted to the demographic potentially. If you want to set up new programmes or new therapies, you want a more balanced view of what the patient population should look like in order to serve those new potential solutions. In order to get around that historical bias, you can use third-party data to enrich the data to have a more balanced view. When you have historical bias or measurement bias, you need to bring in third-party data to help balance the view where you actually do your modelling and calculate the insights that you want to base your new programmes on.Streeter
Yeah, that's really interesting. Anna, picking up on that point, to what extent should companies almost be acting as detectives going out and sniffing out more data to ensure they're not going to be landed in a bias situation?Stamatelatos
Yeah, sure. I mean, it's a constant monitoring of data sets to improve and correct biases, as Ed rightly pointed out, as you bring in other sources of data, you want to ensure that your decisions from the data is more based on comprehensive data set, both from recent and historical insights, but continuous monitoring of your systems to ensure that you are removing biases and having equitable experiences for all.Streeter
Ultimately, what should companies be aiming for, Ed? What differentiates a company with an effective data strategy?Bobrin
Companies Instead of employed and effective data strategy, you have the potential to really unlock opportunities for net-earned revenue streams. They can find efficiencies and processes that manifest themselves in data. They can leverage data to improve employee experiences. Everything we're doing on a day-to-day basis, we're having conversations in teams chats, or we're interacting with one another, how our levels of productivity can be measured and help us to do better day-to-day and save time and be more productive. These are some of the examples of how an effective use of data can be a real differentiator.Streeter
Anna, it's a point there about net new revenue streams. What else do you see as the potential here?Stamatelatos
Leveraging the power of data to make better informed decision and also innovation that can, as Ed rightly said, point out to net new revenue streams, giving them a competitive advantage. With all of that, again, back to data governance and quality of the data. It's to ensure accuracy for better insights. Then it also will enable cross-functional collaboration across the enterprise. Again, breaking down the data silos that we spoke about, being able to collaborate with business bonus units that will then enhance the decision making and lead to new revenue streams. You basically translate your analytics and AI for Actionable Insights from their data sets, and you're able to then predict, personalise, optimise, optimise, right? Being able to stay ahead in the market and continuously learn and adapt based on the data and demands of their businesses. So remaining agile and resilient to the market. Then, of course, with all of that, and back to the governance and remaining secure of the data, and then, of course, always operating on an ethical use of data and privacy.Streeter
I mean, you work with a huge number of businesses in this realm, Anna. What sectors would you say leading when it comes to leveraging their data for true business transformation?Stamatelatos
Yeah, sure. Ed mentioned health and life sciences, so that's definitely one. Financial services, retail and consumer goods, as well as manufacturing. All of these are able to leverage real-time data for their decision-making, some potential use cases that we've seen that are most common with data quality, fraud detection in the financial sector, real-time decision-making across all, and then, of course, better personalization within retail and consumer goods, and even health care, too, from providers and payers. Then just the collection of data across all of these from all of the disparate data sources, as Ed mentioned earlier, allowing the enterprises to react and respond.Streeter
Which industries, would you say, have seized the opportunities more than others?Bobrin
We're pretty consistent in a perception of what industries are really making strides here. I would agree technology, health care, finance, banking, retail. There's a long list of them. I think one of the good examples that we all see in our daily lives, and Adam has mentioned personalization, I think is now known as hyper personalization in the e-commerce space, specifically, is something that jumps out. There's capture of data around our purchase history, our search history, often funnelled back to a data platform and data lake that we talked about earlier, and then enriched with third-party data that adds in demographics, knows a little bit about our spending habits in general, what are even a little bit more insight on our financial picture, and they know exactly when and what to target, and or when and what the next action should be for a sales rep or a service agent, depending on the context of the interaction you're having as a consumer or a buyer or a patient, put yourself in whatever persona you want. And while that can feel a little bit creepy at times, it's a convenience factor, right? And most people, from a convenience perspective, like not to have to answer a question a second time or a third time, but have that answer already at the service reps or the salesperson's figuretips, and you're It's the only way that your needs are being anticipated.Bobrin
That translates typically to a positive bottom line for the client or the provider in this case, where that hyper personalization benefits both parties.Streeter
But despite the need for this hyper personalization, we often hear that legacy systems or disparate systems make the modernization of data estates challenging. How would you say that AI is changing the game here?Bobrin
Yeah, that's a good call out. You think some of our traditional clients that have legacy data platforms and have put off modernization, and they tend to feel like they're falling a little bit behind on the AI, the Gen AI curve. Well, now we certainly have the opportunity now with Gen AI to leverage its capability to compress the effort and timeline to modernise legacy data warehouses into a cloud-based modern data platform. So the use of structured and unstructured data that clients have in more ecosystem, like a data catalogue, or they have schema diagrams around their traditional data warehouse, or they've defined a business glossary that the business units have been using to understand what's in that legacy data warehouse. That could all be leveraged today by Gen AI to help translate legacy code to modern code, and more importantly, using natural language to translate that code into documentation that can help facilitate the understanding of requirements and improve engagements with the business. That compression of time and that use of natural language actually allows the developers to focus on the most important things, the code that's the most complicated, where low-hanging fruit easy code for migration can be handled by Gen AI, and better understanding of the requirements can be facilitated by documentation that's generated by Gen AI that saves developers a lot of time, and that compresses the effort and timeline for moving from legacy to modern.Bobrin
That enables all the AI use cases and modern use of data that we've been talking about.Streeter
Picking up on that theme about natural language, Anna, how else do you think it can be leveraged? What's your take here?Stamatelatos
Yeah, just to expand on Ed's comments there. For natural language, enterprises are seeing huge productivity gains, right? Whether it's from the business analyst or the developer, as Ed was speaking about. The developer is able to use natural language within the AI models to not assist them through various code development, but also to check code and refine their code, which then results in productivity gains. Then the same with business analysts. They can use natural language to help them with reporting and to build query insights without having to know how to code whatever technology they're using. Then the efficiencies and accuracies that are across the enterprise, both from the developer and the analyst. Then the The other thing that AI is built into many platforms or cloud platforms is within the security features and encryption of the data so that they can remain compliant as they're leveraging insights from their data. They're able to extend and get those insights quite efficiently.Streeter
Ed, if a company is starting out on this journey of finding solutions to unify data, what first steps should they take? I mean, it can seem a bit overwhelming, can't it?Bobrin
Certainly. I think Businesses need to bring their leaders together and start with a use case, a business use case, a pain point, an opportunity that they see. Maybe a leader sees a unique opportunity in the market where they think Gen AI can help. It's important that you pull the leaders together so you can have an evaluation of those different use cases. Understand what's really going to drive value to the firm. Don't try to tackle them all at once, but take it one at a time based on a business priority. To do that, you focus on getting your data state in order for the inputs that are needed for that specific use case. Identify any risk or other considerations that need to be thought through around that use case. What is the type of data that you're bringing in? Have you thought about the security, the use of PII, and things like that? Standing up a pilot that can help with the quick validation of your hypothesis in that specific use case. Typically, the architect architecture that you're doing in that MVP can be leveraged for the next use case, making the path to value much shorter as you're maturing your data state.Bobrin
If you do one use case and you bring in a certain set of data, the next use case, there's probably some percentage of overlap. You're incrementally starting to build your data state, and you're also starting to identify different points of value using generative AI or traditional AI.Streeter
Hannah, how would you go about prioritising and developing a strategy?Stamatelatos
I would just tag on to Ed's point. Certainly, having a starting point, a focused use case, aligning with a strategy to bring your siloed data together for that particular use case, and making sure that you have the shared goals across the teams in order to align around your common goals and the centralised data sources. I would say invest in a data platform that's able to integrate your data sources services from various systems and is cloud-based for scalability and flexibility. Then I'd also say leverage partners to assist with your journey and plan past the initial use case to continue to leverage those efficiencies.Streeter
It's really interesting to look at that, the prioritisation of strategy here. Ed, can you give me an example of an organisation that really has created new value from data-driven insight? I mean, you work with so many. Is there one that stands out?Bobrin
Yeah, definitely. I think most people are aware that traditional AI or machine learning, I think traditional AI has become a phrase to make sure people are aware that Gen AI isn't the first time AI has been around. Machine learning is a form of that. And very early on, EY developed a comprehensive solution for a retail pharmacy client that balanced the idea of having a balanced inventory. So optimising their inventory, reducing their working capital, and did that with a demand planning solution that also balance customers' service levels, making sure that you always had exactly the inventory you needed to satisfy a customer order without carrying too much. Balancing that inventory versus service level allowed them to save tens of millions of dollars over the first couple of years of their project and continues to drive value for them to this day. That balance is using machine learning models to understand the supply chain and making sure the product is delivered in the time that a customer is going to anticipate that. That translates to higher dollars in savings and also higher levels of customer service.Streeter
Thank you. Anna, is there a company that really stands out for you or at least a sector?Stamatelatos
I'll tag on to a similar theme with EY and Microsoft with a smart retail solution. The ability to bring complex data sets like whether order history Industry gives a new level of analytics for the retailer, really enriching how they're going to service stores at local levels. Through the use of what needs to be stocked at a local level based on buying patterns, whether which product needs to be on certain end caps, really helping position products on store shelves at the right time based on the analysis of the data. Then you're able to really see if your promotions are working at a local level. You're able to move ageing products or make sure that you have the right ones in those locations. The information also helps with more automation of restock and ordering based on buying patterns. Then each store is optimising goods and services that are shipped rather than having ageing products on shelves. Really helping at a local level be more precise with the data that they have.Streeter
We mentioned at manufacturing earlier, as a sector some of the biggest advances that have been made. How have you seen data-driven insight really helping with operational efficiency in this sector in particular?Bobrin
Yeah, that's a good one. One of my first projects that we delivered at EY was an optimisation solution for a manufacturing client where they have data they had been collecting in a data lake for some time from ERP systems, so supply chain data, they had warehouse inventory data, and customer demand in orders, and of course, a master production schedule. But the client also, I guess you could say, had the benefit of having a product that was sold out for two years. In other words, they could run production 100% of the time every day, seven days a week for the next two years and deliver product that had already been sold. What that means, it's a good position to be in as long as you're confident that your machines are going to run all the time. Anytime a machine was down for a day, that was a potential revenue loss of probably close to a half a million dollars. Anything we could do to optimise that schedule, optimise the run time of the machines, changing over the machines less often, schedule maintenance at the appropriate times that can minimise that downtime would be an effective use of pulling all that data together and optimising the schedule of which products should be produced when.Bobrin
Every time that we could save an hour of downtime, there was a very specific dollar value we were optimising from a revenue perspective for that client. If for some reason, if we weren't optimising and a machine went down and they could not deliver a product that was already sold. Basically, you're in violation of an agreement you have with a client. That client has the capability to either cancel that order and go buy it somewhere else or having to make more drastic changes in your production line. Being able to optimise that schedule really drove a lot of confidence in that the firm would recognise the revenue they expected over the course of those two years and beyond.Streeter
There are clearly huge potential opportunities, but also risks. Anna, what has EY research thrown up about the potential for AI and automation? What's been the result?Stamatelatos
We spoke about productivity gains earlier in the conversation, and EY and Microsoft partnered together to look at their own business on how could AI and automation actually reduce the number of man hours where auditors need to go through documents. This is a really popular use case of leveraging AI across industries. EY leveraged it on, okay, if we find ways to help the auditors be more efficient with leveraging AI for document search, they could then spend more of their time with their customer base versus searching through documents to get their answers. The use of AI with Microsoft in their business was able to automate, I think, close to over 200 processes globally, really freeing up over, I think it was 2 million human hours, which is incredible, the power of AI and automation. Then actually putting the auditors back in place where they could service their clients But the automation was able to give them accuracy, improved quality, and time of their overall operations.Streeter
Would you say it has potential benefits for the tax function as well?Stamatelatos
Yeah. There's another enterprise that leverage leveraged AI to assist individuals that were preparing their own tax forms. Back to that use case of querying documentation and getting answers more efficiently. The AI solution in this situation was able to enable the user to get answers to their tax-related questions in a dynamic fashion. They could put a query in, get answers back while they're performing their own tax forms without having to phone call or research. Really adding to the efficiency of their time and getting answers.Streeter
Do you think this is a bit of a slam dunk for other organisations as well? I'm referring to the NBA here because I understand that they've even been able to benefit.Stamatelatos
Yeah, this is a super cool use case to how you can leverage AI in analysis. With the NBA team, wanted to be able to help their team perform better Specifically to the players, analysing their specific body movements, how they're dunking the ball, how they're moving on the court, even down to passes and dribbles of the ball. Then They're also able to look at how they're on the court to hopefully reduce injury risk as well. With 30 NBA teams and over 500 players, they're able to really leverage the power of AI in real-time to help improve game performance and also player stats and how they're able to perform in the game and improve performance overall. The solution was deployed and is operational. Data is continuously being collected to better understand the player's strengths and weaknesses and to also help improve overall performance. So think of the new way for coaches to help their team in real-time and in analysis.Streeter
That's fascinating stuff. I mean, the return on investment for those teams is clear. Net new value can develop pretty quickly, can't it? I mean, this is crucial, isn't it? After all, some companies are understandably cautious at a time of high borrowing costs set.Bobrin
When we talk about ROI on getting value from your data or using AI, it's one of those your mileage may vary type answers. Some AI projects require small investment, re value very quickly, and others have longer payback periods. I think When I talked about first evaluating business use case, whether it's just standing up your data state or actually putting Gen AI to work or traditional AI to work, you have to look at what is the perceived value of that use case longer term and estimate what the cost to stand that solution up will be. You're not going in blind. While it may vary from use case to use case, it's important that you do that calculation or that legwork up front so there are no surprises, and you can prioritise your use cases based on the business priority and the value that you're going to achieve from it. And in today's world, we're seeing new opportunities. We talked about net new revenue gains from having a modern data state. There's a lot of opportunity now to look at the value generated by these new use cases from a different perspective on how you may hire a service provider like a EY or someone similar to deliver those.Bobrin
And the idea of using a gain share model where you start to see the benefits first before you start to pay your supporting vendor for that work. There's a balanced approach. You make sure that you're delivering on what you expect. This is this fail fast mentality. If it's not working, pull back before you spent the money and didn't get the ROI that you expected. Having a partner in place that can take that journey with you is important and actually helps get leaders past their hesitation sometimes when they're thinking they're going to make this huge investment up front. But if they've done their legwork and valued that business use case prior and have some unique opportunities like a game share, that really helps lower the barriers to taking on these projects.Streeter
As you say, defying that use case right at the beginning seems absolutely crucial. Anna, the NBA was clearly using AI to invest in its training. But what investment should companies be making in training to empower their workforces, to take advantage of data and AI capabilities?Stamatelatos
Yeah, certainly Really, leveraging partners is a good step, as Ed had mentioned, being able to take training and leverage partners for responsible use of responsible AI and ethics, making sure that we spoke about earlier, your practises Are you're taking a look at any biases within your data sets, mitigating that, definitely practising around ethical considerations, ensuring your employees understand the impact of AI the use on not only on their enterprise, but on society and their business, and how they can improve productivity gains, support their customers more efficiently, and then look at how they can leverage what use cases, what are most common for their enterprise to set in new innovation and work streams and forge ahead for potential net new revenue streams, as well as, again, engaging a trusted partner that that can help with what are the patterns across their particular industry and how could they best leverage their data sets to move forward?Streeter
How would you define the importance of training here?Bobrin
I think it's important when you're establishing a foundational modern data platform where we're getting from legacy to something new, something that's foundational to be able to take advantage of AI and Gen AI and so forth, that you're building up the skills that you have on your existing team. A A lot of our clients that are using legacy platforms are used to using traditional tools. And some of the tools, obviously in the cloud, have matured, and there's new code, new languages to learn to optimise their experiences with data. So going through training is one thing. Having a partner help employ or deploy the first use case alongside of those employees that are going through training is really a great way for them to build that muscle and learn from a partner at the same time so that they can take the subsequent use cases forward on their own. It's not just about Gen AI, it's really unlocking lots of different use cases. It could be traditional AI, it could be just analytics in general. But once they've built the muscle of building a modern data state, they've put in the governance that they need, they're going to be able to unlock value in many different ways going forward.Streeter
We are nearing the end of the podcast. I just want you to let Let me know as final thoughts, really, how you see AI and Gen AI advancing over the next five years. Ed.Bobrin
We're already seeing the proliferation of large language models, small language models now, and Gen AI being embedded everywhere. We see it embedded in business applications. We're seeing it embedded into our phones, embedded into the latest laptops and PCs. It's becoming more and more ubiquitous. There will become a need for more standards and regulations to maintain the level of trust in the insight that Gen AI is providing. We've already seen the US government. We've seen that EU put forth, not legislation, but recommendations around the use of these types of tools. But as it becomes more ubiquitous, we're going to have to put a closer eye on those regulations. It puts our current focus back on the whole idea of responsible AI practises and the additional advances of cybersecurity protecting us from bad actors in this use. I think when we first got enamoured with Gen AI, we thought, We can just go do everything, and it would all be good because it's trained on real data. Well, there's things you can do to try to fake things out and make bad things happen, just like any other cyber attack that happens in Gen AI. We need to prepare for those things.On the traditional AI front, our trust and experience with AI will see more of a shift from what we've been doing from a predictive perspective to become even more We talked about hyper personalization and next best action. That prescriptive use of AI will become more and more prevalent as there's a new level of trust in AI-driven automation in our business processes as we mature.