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Hosted by top 5 banking and fintech influencer, Jim Marous, Banking Transformed highlights the challenges facing the banking industry. Featuring some of the top minds in business, this podcast explores how financial institutions can prepare for the future of banking.

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How Microsoft Embraced Data Transformation

Data is the foundation for the entire digital transformation process, allowing organizations to make better real-time decisions and enable the movement from being product-led to being experience-led organizations.

Firms that have invested in data transformation have seen tangible results through improved business models. But, data transformation is challenging because of the structure of current data, the distribution of insights, and the lack of skills and resources to commit to a holistic data strategy.

Our guest for the show is Karthik Ravindran, GM, Enterprise Data at Microsoft. He discusses the data transformation journey at Microsoft, and why data maturity is the key to success in a digital world.

This Episode of Banking Transformed is Sponsored by Microsoft:

See how Microsoft can help to unlock new opportunities at speed through innovative business models, deliver differentiated customer experiences across channels, products and services, and redefine new ways of working.

More at Microsoft.com/financialservices

Jim Marous:
Hello, and welcome to Banking Transformed. I'm your host, Jim Marous, founder and CEO of the Digital Banking Report and co-publisher of The Financial Brand. There is no doubt that data is the foundation for the entire digital transformation process, allowing organizations to make better real-time decisions and enable the improvement of experiences across the entire customer journey. Organizations that have invested in data transformation have seen tangible results from improved business models, driving efficiency, and new revenue opportunities, but data transformation is often the hardest part of digital transformation, because of the structure of current data, the distribution of insights and the lack of skills and resources to commit to a holistic data strategy.

Jim Marous:
Our guest for the show is Karthik Ravindran, GM, enterprise data at Microsoft. He discusses the opportunities and challenges that have taken place as part of the data transformation process at Microsoft, and why data maturity is the key to success in the digital world. Welcome to the show today, Karthik. Before we begin the interview, could you provide a short backgrounder on your role at Microsoft?

Karthik Ravindran:
Hey, Jim, thank you. Very happy to be here. So first and foremost, thank you for having me on the show, [inaudible 00:01:32]. My name is Karthik, and I work at Microsoft. I've been at Microsoft now for roughly 22 years. In my current role, I lead a team that's called the enterprise data team within an organization called Microsoft Digital. Think of Microsoft Digital as Microsoft's internal IT organization. And we lead all of the technology investments within Microsoft for our own internal digital transformation. And my team is focused on building what we broadly speak to as our data platforms and our data foundations to power our digital transformation investments.

Karthik Ravindran:
And prior to my current role, which I've been in now for roughly about two years, I've also led data transformation investments in Microsoft Office 365 and Microsoft News. So the last decade of my career at Microsoft has been very focused on data modernization investments. And then prior to that, I held a variety of roles in product management and engineering and different Microsoft products. So very happy to be here, and thank you for having me

Jim Marous:
Well, it's interesting. So your role really is very similar to a lot of the people that would be listeners who are trying to transform their organizations. So we're coming up on two years of delivering the Banking Transformed Podcast, and every show we have done has touched on the importance of data analytics as a foundation for successful digital banking transformation. Despite the universal understanding of the importance of using data and analytics for better decisions and experiences, our research continues to show that banks and credit unions continue to be challenged by building a strong data strategy. How should we think about data transformation in the context of digital transformation?

Karthik Ravindran:
That's a great question. And I think I would start with the driver because even within Microsoft internally, prior to starting out on the data transformation journey, the first question we had to ask ourself was why, because we had reasonably good data systems that were running the business and we knew that something was missing, we had to change something, but we couldn't quite put our thumb on the why. So we actually went back to the drawing board and put a deep thought into it. And where we landed was the recognition that digital transformation, data transformation are all very fancy buzzwords. If you speak to like five people, you'll probably get five different definitions.

Karthik Ravindran:
So we really had to take a step back and think about, what is it that we were actually trying to transform, evolve, change, whatever you want to call it? And what we landed on is the opportunity for us to be able to apply data in ways that we've not done in the past. What I mean by that is, in the past, we had applied data primarily to your point, to gain insights into how our business was performing, like to operationalize our metrics, to look at our metrics and to be able to adjust our day-to-day decision making and business processes. But we weren't truly applying data to its fullest in terms of being able to, A, connect data from across the enterprise.

Karthik Ravindran:
So a lot of our operations was about applying data in specific business contexts, while with the evolution of Microsoft to being a cloud company, it became increasingly aware to us that we actually needed to not just look at data in silos, but look at data as connected data where we can bring data from across the companies to share and create it, generate connected intelligence about our customers, about our employees, about our products, about our operations. And not just use that insights and intelligence to understand how the business is performing, but actually use it to transform the experiences of our products for our customers, for our employees, as well as optimize our internal operations.

Karthik Ravindran:
The big aha moment for us was taking stock of how little we were actually doing that, in the sense that we applied data a lot to look at metrics and dashboards and reports, but we weren't really fully pushing the envelope on, how can we take that intelligence and feed it back into the systems that we are building, whether it's our customer facing products, whether it's our employee experiences or whether it's our internal business operations. And then looking at it through the lens of grit, what could we actually transform here? And then landing on key goals, which all anchored around this whole concept of personalization and contextualization.

Karthik Ravindran:
So, whether it's customer experiences, whether it's employee experiences, it's really about how can you make those experiences more personal, more contextual, to do which you really have to understand, not just the macro linked data points, but a lot of the micro signals that you get about day-to-day like use of your products and services, whether the customers, whether employees, whether internal business processes, there's a lots of rich telemetry and data that's being emitted at all of these edges, which we hadn't truly brought together, connected, stitched to generate that cohesive intelligence that we could then take and apply it back into our products to make them truly personal, contextual and efficient.

Karthik Ravindran:
So that was at the outset what we set out to say, look, as a part of this data modernization and data transformation, we really have to break out of our business intelligence silos, figure out how can we connect enterprise data and doing really well, go beyond just metrics and dashboards to actually apply the intelligence that we get from the data into our products and experiences, and transform the culture of the organization most fundamentally to get into that habit of becoming more data driven, not just in terms of understanding the business, but also looking to how they can apply data to improve their products and services.

Karthik Ravindran:
So it's the combination of the business driver, the technology challenge, as well as very importantly, the cultural transformation needed to bring it all together, which will make the pillars of what we now broadly speak to as our digital transformation journey and in context, our data modernization and transformation journey.

Jim Marous:
What's interesting is one of the dynamics we saw during the pandemic and now after the pandemic is that our research found that financial institutions actually rate themselves lower on data analytics maturity today than they did before the pandemic. Now, do you think this might be because the overall tide rose and what is needed or because FIs may have overestimated what their data capabilities were before the pandemic and then were caught maybe a little bit flat-footed?

Karthik Ravindran:
That is such a great question. In fact, I would say the pattern that you just described is beyond just the financial industry, I think it cuts across cross verticals. And the reason I say this is because a part of my role is also speaking to our own customers from across multiple segments and industries on a regular basis. And what I just described stuck the chord as a common theme that I've heard over and over again. And I think it comes down to this, prior to the pandemic, the organizations and businesses weren't really start off in a place where they had a forcing function to think about a different way of operating or a different way of running their business.

Karthik Ravindran:
And it's very key to make the distinction between the term digitization and digital transformation. Digitization is a lot about automation and what IT has traditionally been known to do, versus digital transformation is more fundamental, it's not just automation. You can apply the modern and you can automate all you want, but you still may not digitally transform. It does require a deck of fundamental step back and think about what cultural changes do you need to drive? What process transformations do you need to make happen? How do you change the mechanisms through which your products are delivered to your customers? How do you engage with the customers in the world where you no longer have physical contact, which is what the pandemic put us instead.

Karthik Ravindran:
So it fundamentally requires you to reimagine your products, reimagine the channels to which your experiences are delivered, reimagine your customer interactions. All of which I think to a large extent, really woke up organizations to first and foremost ask the questions, "Great, where do we go next? Where do we innovate? What do we do? Do we have enough data to make the right decisions?" And I think that ends up opening up the insight into a huge chasm in terms of not having all of that fine-grain data, let alone fine-grained data, but even aggregated data that's truly connected from across all of the business to drive that kind of investment decision making and prioritization of investments.

Karthik Ravindran:
So I think the digital transformation truly brought upon us this aha moment which is like, "Wow, there's so much we need to understand." Take marketing for example, marketing in the pre-digital transformation world, and the same in say the pre-pandemic world was largely about sending some specific emails to your customers, or maybe marketing to them in your brick and mortar stores when they walk into the store, fancy banners, the fast detention of customers and so on and so forth. But what do you do in an era of the pandemic like the one that we just are living in right now and trying to get out of?

Karthik Ravindran:
The way in which you have to be able to interact with your customers, the channels that are involved, being able to understand your customers signals from every point of interaction through every digital channel is significant. Being able to connect all of that rich data to truly understand what your customers are trying to achieve and guide them down the right paths of discovering and using our product has evolved. So a lot of these are always on the minds of leading technology companies like in the past, but now I think the pandemic and the answer to the pandemic has in a forcing function way driven every organization to think about how are they going to fundamentally change for this new world, which I think my perspective going forward will continue to be a world of hybrid work and hybrid living.

Karthik Ravindran:
If there's one thing that we've all learned is you can actually get a lot done even without having to be physically present always, and how do we create the right experiences for a hybrid world like that is going to be hugely informed by data insights and intelligence.

Jim Marous:
What are some of the key drivers you hear from organizations that invest in a data transformation strategy?

Karthik Ravindran:
The first one that we always heard about is... We've got all these red signals, like how do we bring these signals together from across the company and how do we truly connect them to generate intelligence? The number one challenge that a lot of organizations struggle with is we've got data in silos spread across the company, and we know we need to bring that data together and integrate the data to truly get those connected insights that we need to transform our products and our experiences. And we're not quite sure where to start, do we build a single data infrastructure where we bring all the data together? Do we keep the data rather what it is, and then figure out some federated way to enable access to all of that rich data?

Karthik Ravindran:
And then once we start building our data applications, how should we deploy them? How should we operate them? So most of this I would say broadly categorize and be described as the technical challenges to the organization space. So there's a core technology element around building the right data foundation, the right data infrastructure, which tends to be top of mind for the CDO, as well as the CTO or the CIO, depending on who the CDO partner is. I think the CDO's role goes beyond just the technical component is the other two dimensions, which are also top of mind for our customers, one being the business and the other being the cultural transformation.

Karthik Ravindran:
So from a business perspective, top consideration is, great, how do we decide? Where do we prioritize? Where should we put our investments to truly transform? Because digital transformation is not an overnight journey, you have to be able to break it up and incrementally navigate a journey with full understanding that it's going to be a night trader, virtuous cycle of learning and doing from learning. So I think our own experience have taught us that just get really comfortable with living in ambiguity and using as much data as you can to guide some informed decision making, but also be prepared for data to teach you things that you may not necessarily know upfront.

Karthik Ravindran:
And then when you learn those outcomes, be ready to pivot and adjust your investments accordingly. So adopting a really high trade of mindset to prioritizing and evolving the business like transformation priorities is I think very, very critical because it's not as simple as turn off the switch office and turn on a switch and now you are digitally transformed. So within Microsoft for instance, we picked a very specific domain to start our journey in. We really doubled down on taking our sales and marketing like domains, which are front and center to how we communicate with our customers and engage with our customers.

Karthik Ravindran:
And we really had to start to rethink how we do it really well in this day and age of not just the cloud services, but also on the pandemic journey that we've discovered over the last year. And say the marketing is also one of those domains where we need to bring together data from across the enterprise, from sales, from marketing, from finance, from operations, customer support, service, product usage. All of that rich data needs to be connected to truly understand the customer's journey, what's the next best recommendation to make to a customer, how to nurture a customer, and then target, the right communication through the right digital channel to drive mutual outcomes.

Karthik Ravindran:
So we picked out and we picked up a very specific domain and then we came down with a very specific set of experiments that we wanted to run in the domain, and then gradually ran projects that helped us build those experiences, trade on those projects, and evolve from there. And as we did that, we learned a ton. And then we took those learnings and started applying it into other domains, which were in various states of digitization to digital transformation. And some that are in between, and in some cases purely in automated states, but not really in the transformed states.

Karthik Ravindran:
The third key dimension is, I would say, the cultural dimension, which a lot of people struggle with, which is, to truly make all of this work, we need teams across the organization to be data savvy, to be capable of applying data. And we need to get an IT department that has previously always locked all of the data to open up the data and make it democratized to all the different teams to go and apply. And by the way, we need to the CTO, who's stuck in the middle, to work across the CTO, the CIO, as well as like the business leaders to make all this magic happen. The cultural transformation, if any, has been, I would say, the hardest challenge of any journey.

Karthik Ravindran:
And people will be surprised to hear that even at Microsoft, we have those challenges and we still do because it's a massive change in mindset in terms of how you approach this. It's very easy to say that you want to be data-driven, you aspire to be data-driven and to actually be data drive, because at the end of the day, being able to apply data to inform your day-to-day decisions, to be able to infuse data into your experiences, as you build them into your products, all of these require an evolved mindset relative to how we have made decisions and operated our products in the past.

Karthik Ravindran:
And then recognizing that every function needs to have some data savviness or data competency to be able to do that really well also requires investments in terms of people, either developing our existing people to become data savvy or investing in data talent. Combined with the cultural shift of being able to responsibly democratize data, the keyword being responsible democratization. Because it's always a balance between access and control, and neither can be accused for blocking the other. Very often, you have the diehard IT folks saying, "Oh, access control is the most important and democratization should be an afterthought."

Karthik Ravindran:
And then you have like the chief marketing officer, or the more edge-facing business leaders coming in and saying, "Nope, for me, innovation takes top priority and you can unblock it. I'm going to figure it out my own solution." And the reality is, it's gat be built. You need a balance between access and control. You can't compromise access for the sake of control and you can't compromise control for the sake of opening up the doorway to everything. So striking that balance was a very core principle for us, which is recognizing that data has to be democratized, but it has to be democratized responsibly. And now, how do you build the right technology foundations to enable that? So to summarize, I would say technical, business, cultural, the three big areas to really think about in terms of forming a strategy for data transformation.

Jim Marous:
We sometimes forget that massive, big tech firms, such as Microsoft have many of the same issues and opportunities internally when trying to execute a data transformation strategy. Where did you start? But most importantly, what challenges did you face that most organizations are going to face? You mentioned culture and leadership, that we talk a good game, but we don't always approach it the right way. So what challenges did you face and how did you address these?

Karthik Ravindran:
That's a great question. So our biggest challenge was largely cultural, and I'll break it down into the specific elements so that we can get more clarity on that. I'll have to give a little bit of history, because like about, I would say roughly 80 shares ago, we attempted one of these large scale data transformation journeys where we tried to centralize a lot of the common data foundations that we wanted everyone in the company to be able to align to and start using. But we went a little too far on the centralization where we tried to centralize both the infrastructure and the foundation, as well as centralized the applications of the data on that infrastructure.

Karthik Ravindran:
And that became very limiting to the teams, and rightfully so, because the domain knowledge and the expertise to adopt and use data exists in the individual teams. And truly good data transformation journey should enable an end-effect, the end-effect being common, scalable ways to operate your data manager, keep it secure and government compliant, combined with a federated agility where each of the business teams and engineering and can apply the data in their day-to-day investments. And our first foray into trying to come up with a modern data system about eight years ago did not succeed because we were trying to centralize everything.

Karthik Ravindran:
Then we took a hiatus, we took a gap. And then now we restarted the journey roughly about three years ago, since my team was formed, and we started up taking this challenge, which is like, "Great, what did we learned from the past and what should be preserved from the past and what should be changed from what they learned from the past?" And where we landed on was to say like, "Look, the users of the data are absolutely right in saying that they need flexibility, agility to be able to serve their own needs. The data owners are absolutely right in saying that they need to be able to have the governance controls and knobs over their data.

Karthik Ravindran:
The data consumers are totally right in saying that they should be able to access the data when they need it, where they need it to make their decisions in a timely fashion. How do we enable all of that goodness without compromising the fundamentals of operating a secure compliant and governed data estate, where we make it really simple for data owners to own, manage their data through common mechanisms, we make it really simple for data consumers to subscribe to and find and use the data that they need in compliance with the data on our policies, and we provide the common foundation and the infrastructure to enable all of that?"

Karthik Ravindran:
The reason I gave you this background was because the fundamental cultural challenges that we had to navigate were based on this. The first cultural challenge was, data owners did not want to go to a common infrastructural foundation because based on their past experience, they had lost the keys to their data, and we had to make sure that in the future state, that's not the case, that they are still owners of their data, they still manage their data, and they can do that now on a system where they don't have to invest engineering or operations cycles to build and run their systems to do that. Instead, they can focus on the art of owning data and governing data.

Karthik Ravindran:
Data consumers, we had to win them over as well because data consumers in the past were asked to hand over their keys to building their products and to building their solutions, and they were bummed by that, and they needed complete guarantee that in this new world that they would not lose that flexibility. So we had to help the data consumers get over the fence and seeing how they can still build the data solutions, but they could do that on an infrastructure that will provide them the guard rails to ensure that whatever they build is secure, compliant and governed, and that they would do that by accessing and using data that's governed by the policy as defined by the data owners.

Karthik Ravindran:
So creating the data economy, if you may, where we bring together the publishers and the consumers and give them all the guardrails and frameworks that they need to build and operate data in a secure way, but also not requiring them to become engineering or operations, supposed to do all of that, and instead focused on that differentiation and that data and data solutions, was the biggest aha moment that we had to navigate and learn. So a lot of our cultural transformation was around helping the data consumers and the data owners understand that they're not going to lose their flexibility or their agility. In fact, it's going to be further accelerated now on a common foundation that's going to help keep them safe, secure, compliant, and governed, all of which, by the way, are increasingly important now with all of the growing regulatory compliance requirements and demands of data management.

Karthik Ravindran:
And it's also about finding the happy medium where there's a pain point that everybody can agree to, the pain point being that none of our data owners or data consumers wanted to have to be responsible for the evolving regulatory compliance requirements around data. How do you keep data stacks compliant? How do you keep data GDPR compliant? How do you keep data privacy compliant? Complex challenges with constantly evolving regulatory requirements in the industry and from governing bodies, which is very difficult for all of these teams to stay on top of. So me giving the value prop and say, "Hey, all of these fundamentals are covered for you, and you can safely build and run your data solutions on a foundation that gives you these fundamentals." That's a win-win proposition.

Karthik Ravindran:
So the approach we had to take, obviously, when we first started out, it was, I would say a 50-30-20 equation. What I mean by that is, 50% of the teams were super excited, "Let's go," 30% were like, "Okay, I'm going to sit on the fence and wait to see what happens to the first 50%." And 20% was just like, "Nope, we're not going there. We're going to continue to build and run our own infrastructure. Thank you." And here's a key lesson on this, because we could have sat and span cycles and cycles of time trying to bring over the 30 and bring over the 20, but we did not do that, instead we focused on the 50.

Karthik Ravindran:
And then we'd pick a strategic partner in the 50 who was willing to move ahead and who had a real use case to go demonstrate this. And then we worked completely with a partner, landed a killer showcase of what can be done with the modern architecture. And that in turn, by doing and telling and showing, both the confidence and the community, because when your customers don't tell that story to their peer teams, there's nothing better than that versus you trying to go sell your own wares. So it's about recognizing that you don't have to be a completist to have it backed. And this is a very, very famous quote by our chief digital officer, Andrew Wilson, who always says this, "You guys beat yourself up so hard on trying to be completists, you don't have to be completists. You can have a lot of impact by picking something focused, going and nailing it, and then building evidence that helps you then iterate and do more."

Karthik Ravindran:
For me personally, that was a mindset shift change, internally as well, because from being a perfectionist to being an uncompletist, but really focusing on the impact that you could achieve by even selective investment was an aha moment for me, and I learned that and I enjoyed that. And then from that, I traded, and then gradually, the 30% that were on the fence started becoming pro, started coming over because their partners who they work with had success with us and were telling our story for us. And then today, I would say they're more like in an 80-10-10 state, so we've shifted the needle to 80% being onboarded into our new story, there's 10% that's still on the fence, still figuring out when they move, and they're still a 10% that we're continuing to work with and trying to bring over the fence from being like the folks who do not want to adopt or embrace to hopefully change them soon at some point in the future.

Jim Marous:
It's interesting, organizations in the past have used data and analytics more for a look backwards to see what had already occurred and how they had hit their KPIs, things of that nature, as opposed to using data as a tool for proactive business planning and customer engagement. How did Microsoft make this paradigm change from using data to measure things, to using data to plan and to project things?

Karthik Ravindran:
Absolutely. So I would say the biggest shift that actually made us get there was the fundamental shift of our business model from being a box product software company, to being a cloud first company. I would say like roughly about two decades ago, 25 years ago, Microsoft was all about packaged software that shipped on CDs that would get distributed through the physical supply chain. And in that day and age, they'd had to look at your business performance numbers as an afterthought or reactively was good enough to run the businesses. Yes, you needed some level of predicting and forecasting, but the business was fairly standardized in the sense that, hey, you would sign like five-year volume licensing deals with enterprise customers, they're good to go for five years.

Karthik Ravindran:
And then as the five-year mark approaches, you'd have to figure out, what's your renewal strategy, upsell strategy and whatnot? But then with the shift to products like Azure, Microsoft 365, X-Box in the cloud, and now almost every Microsoft product, which is in the cloud, the equation changed. And the cloud economy is very different than the box product and the brick and mortar economy. Your customers today have an opportunity to switch to a competitor like this. There's no more five-year contracts and multi-year deals to actually abide by. You earn your credibility and your right to serve the customer every day that the customer uses your product.

Karthik Ravindran:
And that's a key word, every day your customer uses your product is not just an opportunity for them to use the product the way they've used it, but it's also an opportunity for you to influence them, to use it in ways that they have not used it yet. It's also an opportunity for you to show them what else do you have in your portfolio that could augment what they're trying to accomplish and thereby grow a mutually beneficial cross sell and upsell motion into your customers. And none of that would be possible if you don't need to know and understand what your customers are doing, if you really do not understand who your customers are, or what do they do, what are the priorities they're trying to solve?

Karthik Ravindran:
And this is where the data becomes critically important. If you took a modern customer journey today, and I'll give you a real example, I would say 20 years ago, a customer of Office, an that we all can recognize is about using a particular Office app. It is about using Word or using Excel or using Outlook, versus today, a customer journey is not just about when they are in the app. When a customer lands on the office.com website, that's a touch point. When the customer navigates the videos, the tutorials, the content, and the documentation of Office, those are all interactions. When the user chooses to sign up for a trial and use a trial for seven days, that's an interaction.

Karthik Ravindran:
Then the user gets contacted by a salesperson or a partner to educate them on what the product can do for them, that's a touch point. When the user buys a product, and then after they buy the product, the ongoing engagement to keep nurturing the user to not just the product, but through mobile channels, through social channels, through email, through the web, through the online experiences, all of those are touch points. One of the fundamental shifts in this cloud economy and cloud products is you have touch points on not just your product, your touch points are pretty much omni-channel, as well as the life cycle of your customer is an ongoing, what's your cycle. You no longer have that, what do you call this? That sticky business model, where once you close a deal, you're good for the next five years, you've got to earn your customers right every single day.

Karthik Ravindran:
So that transformation, that transformation to the cloud economy and the cloud way of building and operating products across our product line, really gave us a wake up call, which is like, how do you, for instance, know when your customer of your cloud subscription is going to churn? How do you know whether your trial customer is going to convert to a paid customer or not? How do you know whether your customers who have signed up for a particular subscription have opportunities to use something else that you might have which have not discovered yet? And to gain those insights, you have to go beyond just looking at data as an afterthought, you have to intrinsically capture those signals and actionable signals back in the same experiences that your customers are using day to day, to be able to nurture them, to grow value for the customer, as well as grow your business.

Karthik Ravindran:
And that is a fairly common set of aspects that applies to any business, it doesn't just apply to Microsoft. Every industry this day is in some shape or form in the cloud. Every industry, including traditional industries, have online digital presences and cloud-based channels through which they engage with their customers, both owned and operated by them as well as owned and operated through the broader industry. And so being able to adapt to that new world of business and to do which data is super crucial and then to do which, you have to fundamentally rethink how you set up and apply data beyond just the metrics and dashboards and reports.

Jim Marous:
So it's interesting what you've just said there is enlightening because it also, as you said, applies to every industry. Microsoft went from a product sales model to an engagement model because it wasn't just about, can I sell another Office suite or can I get one more year subscription? It's really, how much can we engage? How can we get dialogue going? We talk about PayPal saying they want to build a super app because they want to have more engagement on an ongoing basis with the mobile app. Well, it's no different with Microsoft, it's no different with the financial services industry. You're really looking and saying, "We don't keep the customer unless we get them to engage more frequently over time, and in effect they get value." It's a value exchanges, isn't it?

Karthik Ravindran:
You're absolutely right. In fact, you summarized it so beautifully from a product and sales model to an engagement model. I like that so much better. It truly is the engagement economy. Where I think the industry has shifted from the notion of micro transactions to micro transactions. In fact, I would even change it to not even looking at it as a transaction, but I would look at it more as micro engagements, to use your term. And every engagement, every touch point is vital because not every engagement and touch point is going to generate some business outcome that you might aspire for explicitly, but every touch point influences a business outcome that you're driving towards, every touch point influenced as a customer value outcome that you want the customer to realize so that there's a synergy between the customer value realization and the growth of your business.

Karthik Ravindran:
And then recognizing the various edges where you have opportunities to have those micro engagements. That's I would say the golden nugget of digital transformation. Because when you really look at it, as every engagement has an opportunity to grow your product, grow your business, grow your customer loyalty and customer experiences, I think the opportunities open up pretty materially. And then now it becomes a question of how do you know where to invest, which channels have the highest impact channels.

Karthik Ravindran:
And even then, even once you've identified that, how do you personalize and contextualize the engagement because these days consumers and customers, the attention span, the ability to retain their attention, and especially in the digital world, it's fairly low, and you've got a window of time within which you need to be able to pop up something that is meaningful, contextual, and it's going to grab the customer's attention. And to be able to deliver those types of micro, meaningful interactions and engagement like messages, you really do need to understand your customer and what they're doing. And guess what, the fuel to do that is data, because without data, you really can't do that.

Jim Marous:
It's interesting because we talked about this, and one of the things that we keep on saying, and almost everyone on our podcast is that in order to keep pace with what the needs of the consumers and businesses are, many organizations need to partner with a solution provider. It may be a cloud provider, it may be a provider such as Microsoft. In some way, we don't have the ability to build everything we want. But I'm wondering, Microsoft, do you partner with outside providers in order to up your game? And on the reverse side of that, how do you partner with financial institutions to help facilitate and escalate their data transformation process?

Karthik Ravindran:
Absolutely. I think every organization will be really involved by trying to truly put some thought in to discerning, like, what is that core IP versus what is not that core IP? So Microsoft, for instance, we have a very simple model for choosing when we invest in our partner solutions versus when we build our own. And it really comes down to the notion of what we consider as being core IP versus not. And then internally for our own internal digital transformation and ID investments and application investments, that's the front and center pillar that we apply, and truly understanding.

Karthik Ravindran:
And data is a very tricky one because there'll be our vendors there who will try to convince you the data is not a core IP, and we have a different point of view on that. We truly believe that every brand and organizations data is unique, and truly, truly understanding the nuances of the data, whether it's customer data, employee data, operations data is very contextual to the brand, the operations of the brand because brand differentiation and personalization is, I think, a core factor that drives the difference between the truly successful impactful brands and everybody else.

Karthik Ravindran:
And a brand truly needs to own their data and own their insights and intelligence from the data to be able to apply it in the ways that are most meaningful to their customers. But the difference is, not every brand needs to invest in the underlying infrastructure to be able to do that. Every brand trying to build their own infrastructure, trying to build their own data lake or their own data warehouse like underlying clumping or trying to build their own relational database technology, or trying to build their own [inaudible 00:34:11], NoSQL database technology, that I think every brand does not have to try and solve because there's a lot of those hard problems that have been solved by the technology companies that offer those solutions.

Karthik Ravindran:
So really being able to discern like, what's your core differentiation, but also not pushing it too far to the other extreme where you take your data and all of the data assets and put it completely into the hands of like a sales provider to not just operate the infrastructure for you, but to also apply the data for you. That's the part where I think there's a fine gray zone where you really have to take a step back and think about what you should hold on to. So from a pure data perspective in terms of what we do for our FSI customers as well as I would say broad cross industry domain customers is continuing to inculcate this notion of saying like, "Hey, you are the brand, you own your data, but you need to have a great technical infrastructure to be able to operate, run and operate that data."

Karthik Ravindran:
And that's an infrastructure where I think we can help you in terms of giving you all of the platform foundations and tools and techniques to be able to do that. But we do suggest and recommend that you own your data as well. You own your data products and solutions that's built and operated on an infrastructure. And that you treat that as core IP differentiation, which you don't offload and hand off to, let's say for instance, a vendor or a solution provider. And this is why you'll hear us often describe ourselves as a platform company. And today we still hold true to that. It's just that in my particular team, since my team is more internal ID we also happened to be the first customers of the platforms that we build at Microsoft.

Karthik Ravindran:
So our team is also referred to as customer zero which is any and every tech that comes out of our products is first and foremost applied within Microsoft ID, and its enterprise tested is enterprise readiness validated before we look to take it out and push it out more broadly to the universe? So really thinking about the differentiation versus what is more commodity for lack of a better term that you could be leveraging from someone else who's a better provider of that is key to decide where you invest.

Jim Marous:
From your perspective, and you've been in this industry for a long time, and you've been working with data transformation, actually digital transformation for a long time, a lot longer than most anybody that I know of in our industry, because a lot of our industry has been focused so much on the risk and fraud issues within data transformation and digital transformation. So overall, in every industry and especially at Microsoft, what do you believe are the traits of a data transformation winner? I would imagine it goes well beyond the size of the organization and technology alone.

Karthik Ravindran:
Totally, totally. I think we should take a step back from tech. In fact, your point is not about tech at all, it's more about the principles that you really want to align on in terms of the building blocks of your data strategy, because the actual deck, you could make your choices once you've aligned on your principles. So if I have to pick the top three, I would say the first one is to emphasize the importance of whatever I would broadly describe as shared data foundations, for the very point that you just made which is in FSI industry, you spoke about the regulatory compliance, the standards that you have to comply with, and just the rigorous that you have to practice on fundamentals like data privacy and GDPR and consumer data production, especially with emerging laws that are coming out.

Karthik Ravindran:
And these are all very complex problems to operationalize at scale. And in fact, when you're operating at the velocity that we're operating at the stairs in the cloud and in the digital age, you cannot solve for these requirements by just having human governance and human data stewards and human processes. It just won't scale. In fact, that will become the fundamental blocker to be able to innovate and accelerate your digital transformation. So if you're only thinking about common foundations, share foundations, that is ideally a mix of human intelligence, but it's powered by thought machine automation and data-driven intelligence.

Karthik Ravindran:
So Microsoft would like to speak to it as it's human reinforced intelligent automation. That's a term that we use. To scale any business process foundationally, we need to have automation. The automation needs to be intelligent because it has to constantly evolve to adapting needs. And we have to ensure that the human IP, which is the signals that the subject matter experts can feed us is constantly being used to optimize the model and improve the automation. So human reinforced intelligence automation for your shared foundations is one of the principles. And shared foundation has been key because you do not want multiple teams going and trying to figure out how to build and operate, what can and should be common services to manage your data.

Karthik Ravindran:
Because if you get that wrong, then you end up having a lot of other challenges to face in terms of, to lead responsible applications of the data which convince that you back several years, but you want to try and avoid. So shared foundations is one. The second one is embracing the culture of responsibility democratization. And this is really encouraging technology teams and ID organizations who in the past have put very hard shackles around the technology infrastructure and the data. And at times have even locked out like access to data on the account of control and privacy and security and all the other, essentials. Being able to open up that point of view to recognize that data democratization is a must to be able to accelerate innovation at the edges.

Karthik Ravindran:
And the key word being, and creating them to think about how to make that responsibility to democratization. So it's really about pushing the technology team in a healthy way to think about not blocking democratization, but making it responsible by applying great technology to do that. The third big principle I would say is around, it comes to the application area, the applications where it actually trying to drive the digital transformation. And we like to speak to this more as the progression over perfection principle, where adopt the mindset of recognizing that digital transformation or any transformation for that matter is not an overnight undertaking.

Karthik Ravindran:
And also try to avoid approaching it as a big bank project, because if there's one thing that's constant, it's change. If there's a second thing that's constant, it's you needing to change from learnings that you get from doing. So our mindset here is progression over perfection. So identify the areas where you've got good opportunities for impact. It doesn't have to be fully validated, but there's some informed guidance, whether it's data or industry evidence or customer feedback, which shows you that you have an opportunity to do something different, experiment with it, adopt an experimentation mindset, get the data, learn, iterate, and evolve. So applying that progression over a perfection mindset will help you gain learnings at a faster velocity.

Karthik Ravindran:
And we personally don't like using the phrase fail fast. It's a very proudly used industry term. We prefer the notion of just learning, everything is a learning, whether it's failure or whether it generates the outcome we expected or not, it's still learning. And the sooner you can get the learnings that need you to pivot or change course the better because you end up having lots of savings and efficiencies that you'd otherwise spend a lot more on. So adopting that progression over perfection, continuous learning by doing and adapting mindset is super critical from an application perspective. Again, to summarize, think about common foundations for what can and should be standard enterprise data management solutions, think about responsibility, data democratization to enable your teams to apply data and do it effectively.

Karthik Ravindran:
And then from an application and scenarios perspective, adopt the progression over perfection mindset.

Jim Marous:
Okay. Final question, and we'll keep it short here, but I think it's important for our audience to understand, if they have to start this process from what I'll call scratch, where do they start? What's the first step?

Karthik Ravindran:
I would say the very first step would be to try and identify what you think are your lowest hanging opportunities to transform digitally. And starting with your customer is always a good idea, starting with your customer because the customer and the customer's outcomes are the common goals that we can align everyone in an organization towards. So rather than trying to tackle the more internal facing optimization opportunities or modernization opportunities, start with your customer, what can you do differently to help your customer have a better experience in this new day and age. And then work your way backwards from there? What do you need to make that customer experience transformed?

Karthik Ravindran:
You will most definitely end up on data and intelligence being one of the building blocks to do that. Now, incrementally, go down and figure out, great, what portion of data do you first need to bring into a modern solution to enable that? And then rinse, repeat, iterate. And once you've gotten to good success with customer, then start looking at your domain, internal operations, employees, and everything else that you can think about. And some organizations are so wasted, so bought in that they can probably start [inaudible 00:43:04] tracks too. There's no harm to do that. I'm not saying don't do that. If you've got the commitment, top down, bottoms up and the energy all aligned across the board, then you can have a track that's about employee experiences, you can have a track that's about customer experiences, you can have a track that's about internal business operations and run all three in parallel.

Karthik Ravindran:
But then running all three in parallel, you've got to make sure that you still stay true to what can and should be common, because the risk of running too fast batteries, you are going to end up duplicating and recreating what can and should be common instead, and then putting you back several years. So I think starting with a few select scenarios, focusing on your customer will put you on the right path.

Jim Marous:
Thank you so much for being on the show today. The look into how Microsoft has done, what every organization is involved in right now is really enlightening. And I think it's important to also, you said it early in the broadcast, to really view data transformation as a process, as opposed to a project, there's no a check mark on a to-do list. And then it doesn't stop. You're a great example of somebody that's been at this for quite some time, and you're far from done. There's no end point to this. And in fact, the overall data transformation train is moving faster than it ever has. So thank you so much for being on the show

Karthik Ravindran:
Yep. Thank you so much, Jim. Thank you for having me.

Jim Marous:
What a great interview. We don't often get the inside look at how a big tech firm handles digital transformation, and even more importantly, how it handles data transformation. It was great to find the inside look at how Microsoft has transformed from a product company to an engagement company, to an experienced company. Think about how you use your products and how often you engage in multiple ways, rather than just using the product. There's a true analogy here between financial services and Microsoft in the way they are addressing their challenges, how they keep moving forward, and how they're transforming what is traditionally a very stable, not changing the product very much, but changing the experiences to move with the times.

Jim Marous:
Thanks for listening to Banking Transformed, rated at top-five banking podcast. If you enjoy today's interview, please be sure to follow the show on your favorite podcast app, and don't forget to give our show a five-star rating. Also, give it a good review. The reviews are really the key to understanding how we can make the podcast better going forward. Also, be sure to catch my recent articles on the Financial Brand, and check out our latest research on the Digital Banking Report. This has been a production of Evergreen Podcasts.

Jim Marous:
A special thanks to our producer, Leah Longbrake, audio engineer, Sean Rule-Hoffman, and data producer, Will Pritts. I'm your host, Jim Marous. Until next time, keep learning and keep moving forward.

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