Thanks, Judith, for the confirmation. I will go ahead and start with, today's session. Thank you all for taking the time and joining the today's session. I'm Rajita Munugala.I'm the director at the enterprise data and analytics from the Western Alliance Bank. Since yesterday and today, we all heard a lot about the AI artificial intelligence. I would love to start with everything we are hearing about AI, the destination of it is unknown. The companies that do not yet exist will build products not yet conceived using ideas not yet in existence with methodologies yet to be defined with ideas yet to be imagined. That is a state of AI where we are today. As we wake up and sleep, we all keep hearing nothing but AI lately. What I heard is even the kindergarteners are going to their teachers and asking, where is AI in my rhymes?

And lately, they decided to change the Old MacDonald rhyme to Old MacDonald had a form AI AIO. That's how much of an impact of AI has been lately on each one of us. As part of today's session, I would love to walk you through the basic pillars of what what is needed to amplify about the AI, what we are talking here. I'm gonna talk about the AI value stack, the pillars. More importantly, there is a framework which we use called the quadrant framework, which we follow as part of the data product management team in our organization, which I would love to walk you through. And finally, end with a secret sauce as a leader, what defines us, what differentiates us compared to the other leaders who we are working along with.

So let's dive in when we all say that we are part of the IT organization. IT is nothing but the information technology. Information is nothing but the data. Let's understand how can we can get the data right, and more importantly, how can we get the quality data right in order to amplify AI and make a huge impact on what the AI is. So we'll start with what the data management is. It's all about transforming data into the insights and then making them to build actionable strategies. This data is a treasure trove that everyone in the organization should have access to. Today, we'll delve into the comprehensive approach, how data management, governance plays a crucial roles in this process. I'll be speaking from the perspective of the West Allianz Bank where recently we are in a rapid, rapid growth mode.

Over the past several years, we have significantly expanded our asset base, which has spurred the launch of our enterprise data program four years ago, the one which I lead. As part of this fast growing, we adopted both the offensive as well as defensive strategies. What I meant by defensive is in the sense that we need the robust controls and regulatory compliance measures in place for us to live with this turbulence market. I was fortunate enough to work on this program from the ground zero. In my experience, every time I talk about the data product, it is a reusable data asset, which is designed to deliver a trusted data set, which is tailored for the business needs. It could be for the business analytics. It could be for the and regulatory reporting. It could be for anyone.

At the end, we want to make sure that we make this data product a valuable resource for informed decision making and also the strategic planning. The process, what we take to aggregate the data from different sources, cleanse it, and engineering it is more crucial for us than the end product itself. We all get attached to the end product, but what I say, it is important to have the repeatable, trusted process in place that delivers as we strive for the excellence. We all want to envision what the end state of the product is, including how you want to think about it, how you want to feel the data to be, and detail that to the product strategy and then drive deep into the insights. Now let's go ahead and dive into the pillars which I keep talking about, which is the baseline for the conference today, the data governance, the data management, the security, and analytics. What does each of these pillars do? Everything and anything starts with the governance.

The data governance sets the rules, and they set the guidelines for the data usage, ensuring that the data is reliable, it's accessible, and it's secure. However, without effective data management practices, these rules are difficult to implement. Data management is the one that ensures that the data governance policies are put into action, and the data is available when needed. On the other hand, if you look at the data management, without the data governance, it lacks the controls, and it may result in inconsistencies and misuse of the data. Meaning, governance provides a framework for data management. It defines roles, responsibilities, and the process to ensure that the data is managed effectively and it is in compliance with the regulations.

The third pillar, the data analytics, it plays a very crucial role in deriving the insights from this data. Without proper data management and data governance, analytics efforts may be hindered. For example, data that is poorly managed or governed may lead to inaccurate insights, and it may require the extensive reconciliation effort to correct the errors. We all keep hearing that for AI to work well, if the data is garbage in, it will be a garbage out. If the data is of high quality, which is amplified, the efforts of what we are trying to achieve with the AI today. To summarize here, the point which I'm trying to make is the data governance without the data management will lack the actionability.

The data management without the data governance will lack the controls. And the data analytics without the data management or data governance can lead to the questionable insights and its increased reconciliation efforts To ensure the success of the data products and for the AI to be amplified, these pillars should work together seamlessly. And let's look at the AI value stack in this following slide where different layers needs to be orchestrated well. When we manage the data well, it builds a reliable and scalable foundation, and it enables the integration, and it will make its reusable component of a better value. With governance layer, it increases the trust and controls, and it reduces the compliance risk and increases our confidence in the insights itself. Analytics is where the actual transformation happens and the measurable outcomes are derived.

When all baseline AI can amplify, and we can create the efficiency, and we can increase the business value add. More importantly, to ensure that all these things are implemented layers has to be safe, secure, reliable, and compliant, and we need the security to be underlying and underpinning everything here. I'll try to follow the chart. Meanwhile, if you have any questions, please ping me in the chat. I'll spend last five minutes to answer any open questions on any data strategy or any pillars which we are trying to address today. In the next slide, I would like to introduce you to a quadrant framework, what we use for our data product management today, which provides answers to the questions like what we do, who we do, how, and when to solve every business problem and provide the value added and the viable solutions.

To start with the what phase, this is where we try to clarify the job which needs to be done. We do the problem discovery by understanding questions which business needs to answer. For us, here at the West Allianz Bank, it's very important to fall in love with the problem more than the solution. This phase involves understanding the business context and clarifying and defining what are the objectives and the key results which we are trying to accomplish here. This what is the phase that sets the foundation for the rest of the development and the engineering team by ensuring that we have a clear understanding of what needs to be achieved and why it is important. As I conclude the what phase, do not. I repeat, do not try to do that solution discovery without doing the problem discovery.

That's what the problem this what phase is. When it comes to the who phase, this is where, firstly, we determine whether we are addressing internal or the external stakeholders here. It's essential to identify the business owners as they are the one who define the requirements, and they are the one who arbitrate the priorities for the product. These stakeholders provide valuable insight into the problem domain, and they help ensure that the product aligns with the organization goals and the objectives. During this who phase, the focus is on gaining deep understanding of the problem at hand and the insights that can be derived from the available data. As a data professionals for each one of us, it's important to recognize that the same dataset can be used to answer multiple questions.

So the team must carefully consider the various ways in which the data can be leveraged to achieve the desired outcomes. Overall, the who phase of the data product discovery is critical for ensuring that the right individuals are involved in the project, and there's a significant alignment and collaboration happening among the stakeholders. I'll move on to the final how phase here, which is about the enhanced solutions. This is a phase of the data product discovery. The focus here shifts towards defining how the data product will create a value add and how the great solution concept will be developed to achieve the key results. We start by developing the solution concept by outlining the high level architecture and modeling approach to the problem. This includes the sources, what has to be ingested to the platform, how the data will be curated for usage, and other components that will be used to create the end product which we are trying to get.

Our team will establish the case results or metrics that will be used to measure the success at every step. Metrics can be as simple things as how many sources we are trying to add to the platform, how many critical data elements are added as part of our warehouse, which are blessed by our governance working groups. Overall, the way I look at the how phase of the data product discovery is focused on defining the approach for creating value through the data product and establishing the measures for validation that will be used to determine its success. This phase concludes with how you plan on solving user problems and importantly, the order of solving the user problems, prioritization, and crafting a value proposition and defining what value streams you need to make things happen. The final of the four quadrants, the when phase, this is where the focus is on defining the timeline and the milestones for the delivery of the data product as well as establishing processes for generating business insights and evaluating the success metrics. For delivery of the solution, what our team does is they focus on delivering the data product according to the established timelines and the milestones.

This may involve developing and implementing necessary data pipelines and user interfaces as well as conducting testing and validation that the product meets the requirements. As an agile team, I know industry, everybody these days are agile. We are continuously iterating, and we are continuously improving. Based on the insights which are generated and the evaluation of the success metrics, the team iterates on the data product to make the improvements and enhancements on a continuous basis. This iterative process helps to ensure that the data product continues to meet the evolving needs of the business and for the organization, and it delivers the ongoing value. This could be our bank's most significant challenge yet that we are very highly innovative company, very entrepreneur in how we deliver data to customers by centralizing the critical data assets. Managing data assets is essential, but also it's very quite challenging.

In this space, we are doing a continuous delivery and a continuous discovery, and I say that we're continuously evolving. How do we even manage doing this? And simply, we manage doing this by establishing the multiple stakeholder forums, the towers, and we establish the subject area working groups to discover what the business challenges are that lead to the requirements and that ensures that we have the authority data in place coming in from the single source of drug.

At the end, how we converge from the diverse solutions by understanding what needs to be done, who we are doing this for, how might we do, and when we do it, the time is of the essence. It's very important for us to act swiftly and act decisively. I'll move on to my last slide. I only have a couple more minutes here. I would love to close today's session by sharing my favorite secret sauce strategy for the successful execution, which has proven efficient very effective in every organization I've worked with. One key factor is building relationships and having those offline conversations. While we may not always have the complete authority on everyone, John Maxwell says, and which I strongly believe that, people buy into the leader before even they buy into the vision. He explains how building relationships is a key for the effective leadership, and it goes a long, long, long way.

How much ever I say without having that relationships, without having those offline conversation, without having the people buy in, we cannot move our needle forward as a leaders in any space we are trying to conquer. Second source, which is my favorite, which I want to emphasize is always try to demystify the problems. The problems are big. How are we chunking them? How are we demystifying them? What is it we are trying to solve? How can we make the problem solving and solution finding most most efficient? It's only by demystifying it, putting them into small chunks, understanding what it is, and then converging it together. I would like to end this session with the drum rolls by saying that the problems to be solved should and will come from the insight driven strategy. So it's very important to focus on the bigger outcomes than the smaller outputs which we are trying to achieve to amplify today's AI impact.

So with that, I'll just end today's session, and I'll see if there are any questions in the chat. Judith, not a checklist. Yes. I love that. And then the quadrant frameworks is a practical way. Thank you, Juliet. Thank you for the comment. Any additional questions which I can answer? I only have a minute more. Thank you, the Umerimtech Local Conference for having me and giving me an opportunity to present today. You all have a great rest of your day.