Hello everyone. This is Ramin and thank you for joining me today. I have 20 years of ID experience and have been working with UKRI as head of Data Platform. In my past experience, I've worked with IBM, Southern and Scottish Energies, Experian and Scottish Government, and the kind of projects I have been involved in. Cloud migrations, legacy transformations, machine learning programs, and business process automations. Today we're gonna talk about the blueprint for the cloud, while building a future ready data strategy to establish a right data foundation for any AI initiatives. As we all know, all the industries today wants to grow faster and in this highly competitive market, staying ahead is more critical than ever. Keeping that in mind, everyone is jumping towards cloud. Business understands clearly that it takes huge investment to expand their own network by keeping scalability and security on the top. At the same time, we know that data has sprouted across legacy system in numerous formats, which is coming through different sources in the different structures. All together, there is a pressure of security. Compliance and AI adoption across all the private and public sectors. The cloud migration without a data strategy is a risk, big or risk, and it's a huge risk for your initial investments and the future vision. The cloud adoption is important and it is accelerating to deliver faster ROI and expanding access to the global partners. Let's look at the risks of the cloud migration when you don't have a clear data strategies, and what are those risks? Data silos and inconsistencies. Security vulnerabilities, regularities and compliance failures when you don't meet the GDPR rules or any kind of other medical related APA insurance rules in compatible head performance due to multi vendors, slower time to values and delay in the ROIs. Increased cost due to unknowns. And no clear strategies from the starting duplicate systems, even the duplicate data with the different formats altogether. Traveling through all of your pipelines, which you are structuring during your modernizations without structured data strategies, risks always multiplies and it doesn't helps you to meet your vision. Without a clear data strategies and vision, it is always impossible to harness the full potential of AI insights. At the same time, we can't ignore the challenges that comes with legacy technologies due to their limited features, rising maintenance cost every year, and lack of flexibilities when it comes to scaling or meeting modern security needs to meet our everyday demands. We should definitely maintain the inventories of our legacy technologies as part of our clear strategies and have a very clear path to get rid of these legacy processes technologies. As per the roadmap during our migration journey, what we just need to do is to record every legacy process, which we believe is need to be changed, but it has an impact to our cloud migration journey. It can be legacy technologies, ETLs, it can be legacy integrations, which doesn't have a robust monitorings, which doesn't have a logging facilities, which are not resilient. There can be a different manual data processes, which needs to be automated. It can be inconsistent when the specific data formats. We may have a huge impacts due to these legacy processes or technologies. So let's record those impacts as part of your inventories and act on these impacts The moment. You have the right time or as per your roadmap, based on your defined journey, based on the critical processes, what you need to deliver, or what you need to deploy in the time zone, or maybe in early stages or in the later stages. So let's explore how to build the secure and scalable environment with the right strategy in our mind for this smooth cloud migration journey. So let's start with the first and the most crucial step. Setting up a clear vision. One that's future proof. Grounded with the strong principles, and guided by the well-defined goals. Secured by design, having zero trust, encryptions and auditable policies to validate and verify all the informations. Scalability with modular pipelines, cloud native platforms, AI readiness with a very high quality data. Data, map data and having a metadata in place. The ultimate goal we wanna achieve is to accelerate the cloud migration journeys with a clear defined data strategies in place, and having all the inventories in the place eliminate inconsistencies with all the clear strategies and unlock innovations through these clear insights and the data-driven analytics. Let's look at the key pillar of right, data strategies, data governance. Let's face it, without strong data governance, things get messy. It's not just about the rules, it's about building trust in our data and making sure everyone is on the same page. Metadata management is a heartbeat for the data, which tells the story behind the data. We don't understand where our data is coming from or how it's gonna use. How can we make the smarter decisions centralized metadata catalog? Imagine having a single where everyone can see what this data means and what exactly it's coming from. This is the power of centralized metadata catalog. Behaves like a Google engine for your storage, data, qualities, and lineage. Good decisions starts with a good data. And to trust the data, we need to know where it's coming from, how it's changing, whether it's reliable or not. We need to take a call. That's why quality and lineage matters. Column level, lineage and impact analysis. Sometimes it's not enough, whether that is coming from, we need to right. This kind of details helps us understand the ripple effect of any change we make during our pipelines. So many consistencies across the domains. It's very simple to use the same terminologies across the platforms. It's easy and flexible for the better collaborations, data quality engines framework. Rather than fixing data after the fact, why don't we building build the checks at the same time from the beginning itself, which means start updating all the pipelines from the beginning itself. Securities and access controls with great data access comes the great responsibilities. We need to make sure the right people have the right access and the sensitive data. Cease protectors protected, always secured by design. Isn't something we bolt on at the end. It has to be part of our design from the day one. That mindset gives time, money, and repetitions in the long run integration framework. Our system needs to talk to each other smoothly every day, 24 into seven. That's why flexible integration frames, frameworks are the key to flow data across the platform, tools and teams. Without any frictions, AI and analytics enablement from the day one AI isn't related things anymore. We need to think about enabling AI and analytics right from the start. That means setting the right data foundation today to unlock insights for tomorrow. Let's look at the key principles while building a secure data Foundations zero trust architecture, we don't automatically trust anything. Everything gets checked. Instead of just assuming that things inside our networks are safe, we need to check everything from this user perspectives. Devices and the connections point of view, it is always safer and smarter way to handle the security encryptions at the rest and in transitions. We keep data safe, whether it is stored or being shared. At any point of time it needs to be encrypted, no matter where it is. Identity and access management and fine grain access controls, people only get access to what they need. Nothing more. We manage access carefully. Not everyone should see everything. We have to give the right access to the right persons for the right reasons. All the times. Automated compliance audits. All the times, not once a year. Instead of waiting for audit, we must in checks, runs automatically. So we compliant for all the. Identify legacy inventories, depth risks, and impacts all the time. Monitoring and alert dashboards, look at the problems at the early stages and fix them fastest. With the help of these dashboards and alert systems, we can use dashboards to keep an eye on the systems in the real time and enable these alerts to call out the variances in our shop Locals. AI and analytics in the monitoring these days. All the Smart Clouds tools helps us identifying unusual behavior and fix it on the go. With ai, we can detect anything like strange data patterns or unusual behavior. It helps us act for smart small issues, funds into the big problems. It is always important to have the right data inventories for your cloud migration journeys. Inventories may have a different components like the ETLs and a p, a platform with different integrations or different solutions. There can be numerous manual processes in the file formats. Because of different sources altogether, different data owners may have a different excess dependencies based on your different solutions, maybe power apps or the front end journeys. We always need to ensure we map our inventories with the right data lineage and the critical parts with the critical systems. Non in advance. It's important to identify your critical systems critical process for the right operational support readiness at the start of the journey. Your data catalog and the metadata is always needs to aligned to your inventories. For the right production readiness systems, AI modeling opportunities has to be identified in the early stages. The clear data models and the concise model is a key success for the cloud migrations. For clear strategies, we need to define the governance and ownership right from the beginning. The data keywords and the data owners rules helps us manage and take care of the data all the time. It keeps the data clean, accurate, and reliable. The data usage policies keeps preventing, misusing the data and keeps everyone aligned all the times. Data sharing agreements in those data shared safely and only for the right reasons. Within the data sharing agreements within an internal system or the third party systems, the business Aries and the metadata tagging helps everyone understand their data. Integrities the regulatory alignments like cprs helps us, follows the data privacy and the protection laws, and keeps us legally compliant. The cloud offers, tools and services through marketplace, which scales automatically and makes it easier for the businesses. The serverless data pipelines like Lambda Athena enables faster processing with low cost, without worrying about the infrastructure cost, and can be scaled. On demand at any point of time. The data lake architectures slows all the data in the raw form. Makes it easy to access, easy to store and analyze without the hassle of any predefined data structures in advance. The even driven injections allows real-time data to flow through different integration as soon as any even triggers the containerized workloads provide porta consistent environments to run and deploy your apps faster in no time. These steps becomes critical for our transformation. Discovery and assessment is key critical process to identify our inventory at early stages with the right information. Defining governance and security models helps us defining the rules and standards. Continue modernizing your ETLs and APIs with the deployment pipelines. Start migrating in the phases by identifying your key critical processes and operational readiness. Start enabling AI from the day one by introducing the data quality frameworks in your pipelines. In my recent experience, I've been part of migration project where we had to shift the legacy systems to the a w solution. We followed the similar migration path as we discussed in the previous slide, and we could see the outcome without any slippage. We managed to shift all the legacy APIs to the cloud, hosted A-W-S-A-P-I gateways, and we could see. APIs are working fine without any issues. Within the right time. We continu developing our ETLs with the lamb and the functions and continued feeding back to the business for the reporting purpose. At the same time, we continue replicating our legacy reporting to the new Power BI tools and continue deploying this functionality to the business users. All I can say, it is important to have a very clear strategy for your migrations to reach your goal in time. I. To summarize, it's important to have a clear strategy and migration path for successful migration journey. Investing more time at discovery stages, accelerate the journey, think beyond the tools and focus on the outcomes. Clear strategies helps us link solid foundations for AI initiatives. Thank you for having me here. If you have any follow up questions, please reach out to me. Thank you.