Hello everyone. Thank you for joining. My name is Ari Koli and I'm excited to talk take you on a journey through the next evolution of data management. In today's talk, AI-driven data platforms, the transition from manual management to self-optimizing systems will explore how artificial intelligence is transforming data platforms from manual reactive setups into autonomous self-optimizing ecosystems. In other words, we are looking at how to build a self-driving data platform. We will discuss why this change is necessary, walk through the four key pillars that make the, make it possible, see some real world use cases and metrics for each, and then envision the future where data systems manage themselves. My goal is to keep this clear, conversational and practical. By the end, you should see how these AI driven approaches can free us from a lot of data grunt work, and let us focus on getting value from data. Let's dive in the data explosion challenge. First, let's talk about why we need AI driven automation in the first place. We are living in an era of explosive data growth. Consider these numbers by 2025, the world's data is projected to reach 1 75 zetabytes up from only 33 terabytes in 2018. That's an annual growth rate of about 61%, which is astounding. It's hard to even imagine 1 75 terabytes. Some put it in perspective by saying if you stored art on Blu-ray disks, the stack would reach the moon multiple times over. What's more, a huge portion of this data will need to be handled in real time. By 2025, 30% of all data generated will require immediate processing. Think of sensors and iot streams where you can't wait hours or days to react. In fact, IOT devices alone are expected to generate about 90 terabytes of data annually by 2025. So not only is data bigger, it's. Also faster and more complex. The takeaway is that traditional manual data management can't keep up with this volume and velocity. We simply can't hire enough people or write enough static strips to manage 1 75 terabytes. Much of it's streaming in real time. Automation is no longer a luxury. It's a necessity. We need smarter AI driven systems to handle data growth, or we risk in data we can't use. This sets the stage for why we're explore exploring AI driven data platforms to tackle the scale and speed in a sustainable way. Now let's talk about the four pillars of AI driven data platforms. So how do we actually transform our data first? The first pillar is metadata intelligence. This is about making data about our data work for us. The platform uses AI to understand and organize metadata, essentially knowing what data we have, what it means, and how it's connected. Think of it like a brain and nervous system that senses where everything is and helps the whole platform respond intelligently. Second one is self-healing data pipelines. These are your data workflows that can fix themselves. If something breaks or an anomaly occurs, the system detects it and resolves it automatically. It's like an immune system for your data operations, handling problems without waiting for human intervention. The third is predictive optimization. Here, the platform for casts, what resources like compute, storage, et cetera, will be needed and adjusts in advance. Instead of reacting after a slowdown or outage, it proactively tunes performance. This is akin to a smart autopilot that adjusts scores before turbulence hits, keeps things running smoothly. And the final one is the embedded governance. The platforms. The platform has governance, security, and compliance driven in from the start. AI helps enforce policies like privacy rules or data quality standards continuously and transparently. Think of this as having a diligent guardian or compass inside the system, ensuring everything stays on the ethical and compliant part without manual checklists. Each of these pillars address a different aspect of the data platform. Together. They create a system that can adapt, optimize, and govern itself with minimum manual effort. Next, we'll dive into each pillar one by one with examples and results to show how they work in practice metadata intelligence. Let's start with that. Often call the nervous system of modern data platforms. Metadata is essentially data about data. Descriptions of what each data set contains, where it came from, who owns it, quality stats, and so on. In traditional setups, metadata is often passive documentation, but with AI driven metadata intelligence, it becomes an active component that helps the whole platform understand and organize information. In plain language, metadata intelligence means the system can automatically catalog, tag and discover that data sets. It's like having a smart librarian for your data, who not only catalogs everything, but also knows in context how it's used and can instantly direct you to what you need. This dramatically improves how we find and trust data. Here are some outcomes. Organizations have seen 62% faster discovery. Teams can find relevant data more than twice As fast as before, imagine searching for a dataset with AI curated metadata. It's like a supercharged search engine. So what used to take hours of digging is done in minutes. People spend less time hunting for data and more time using it. 72% fewer data quality issues. The platform catches many data errors upfront. AI tags data with quality metrics and flags anomalies. Leading to a dramatic reduction in data incidents in practice, this means far less time spent cleaning data or dealing with broken reports. 43% higher trust in data. When data is well-documented and consistently reliable, business users gain confidence. In fact, organizations reported a 43% increase in business stakeholder confidence in their data file. People trust the dashboards and insights because they trust the underlying data. Thanks to that intelligent metadata management. And lastly, 2.7 times faster project delivery with quick discovery and fewer issues. Analytics and data science projects finish. Projects finish almost three times faster. Teams are in bo down by data angling. The data is ready to go so they can deliver insights to stakeholders much more rapidly. In short, metadata intelligence makes the platform data aware and accelerates everything. It's the foundational layer that ensures the right data is easily found, understood, and trusted across the organization. Now to make this concrete, let's look at its case study of metadata intelligence in action. Consider a large e-commerce retailer that implemented AI driven metadata for their product database. All the descriptions, attributes, customer reviews, the rich information of our products. The results were impressive. They saw 37% better search relevancy. Customers found that they were looking for much more what customers found, what they were looking for much more easily. By automatically tagging products with detailed attributes using AI to generate and refine metadata, the site search engine could deliver more relevant results, which resulted in a 37% improvement in search accuracy and relevancy In practice, if you search for waterproof jacket, the system understands context like synonyms related features, and surface as the best matches. And they saw 24% higher conversion rate because shoppers were finding the right products faster. More of them ended up making purchases. This retailer saw a 24% increase in conversion rate, meaning a lot more browsers turned into buyers. And people can quickly find what fits their needs. They're more likely to hit add to cart. Third, 19% fewer product returns. Rich metadata meant product pages had better information, accurate specs, usage details, comparable items, leading customers to buy items that truly met their expectations. The result was 19% drop in return rates. Fewer returns indicate customers are happier with what they bought. They got what they thought they were getting. Lastly, 29% larger auto value. Enhanced discovery didn't just help find one item. It helped. It often helped customers find additional or related items. The retailer saw the average order size grow by 29%. This might be because AI driven recommendations powered by metadata, exposed more relevant add-ons or complementary products, and people felt confident adding them to the cart. This case study shows that. Metadata intelligence isn't just an IT improvement. It directly boosts business outcomes better search and data understanding led to more sales and happier customers. It's a great example of how making data more intelligent pays off intangible ways. Self hailing data pipelines Now onto the second pillar. Data pipelines are the processes that move and transform data. For example, an ETL job that pulls sales records from a database and loads them into a data warehouse nightly or a streaming pipeline that collects sensor data in real time. Traditionally, pipelines are brittle. If there is a slight change in input or an error, same as in file or schema change or a server outage, the pipeline might break. Then data engineers get paged at 2:00 AM to fix it manually. Troubleshooting and patching the issue. With self-healing pipelines, we add AI and automation to make these workflows resilient and autonomous. The platforms will detect issues and fix many of them automatically without needing a human to intervene for common problems. Here is how it works in plain terms, detect anomalies. The system continuously monitors pipeline behavior and data quality. It knows what normal looks like. If something odd happens, example, a data patch is 50% smaller than usual, or an unexpected file doesn't arrive, it flags it immediately. Early detection means we can address issues before they cascade into bigger failures. Second, adapt to changes. Suppose an upstream system adds a new column or changes the data format. Normally that might break the pipeline because the code wasn't expecting it. A self-healing pipeline. Can automatically adjust to certain changes, for instance, by using metadata to map the new schema or by ignoring extra fields until it learns how to handle them. It's as if the pipeline can evolve on the fly to stay compatible, remediate problems. For issues that have known issue success, the system can apply them instantly. For example, if a pipeline fails due to a timeout, the platform might automatically retry it on a backup server, or if data arrives corrupted, it could switch to a secondary source. These automated corrections handle mini routine failure patterns without human involvement. Learn and improve. Every time the system encounters a new issue, it learns from it over time, it builds up knowledge of how to handle various failure modes. This means the more it runs, the smarter and more efficient it becomes self-healing. It's similar to how our immune system learns to fight new pathogens. Each incident makes it stronger for next time. In practice, self-healing pipelines drastically reduce downtime and firefighting. Imagine far fewer nightly alerts and emergency fixes. The data keeps flowing. E, even when hiccups occur, because the platform resolves them, or at least contains them until a permanent fixes applied, it turns a fragile data pipeline into a robust immune system that keeps your data moving. So what is the impact of self hailing pipelines on operations? In short, significantly higher reliability and lower maintenance effort. When your data pipelines can fix themselves or at least handle issues gracefully, you can see benefits like increased uptime and reliability, fewer pipeline failures mean data is available when people need it. If your dashboards are updated by 6:00 AM daily, they continue to be updated on time. Because the system has handled any overnight glitches automatically, this consistency builds trust with users who rely on timely data, lower maintenance burden. Your engineers and DA data ops team spend a lot time, a lot less time reacting to problems. One study by McKensey found that advanced self failing systems can cut operational costs by up to 30%. Through reduced downtime and more efficient fall management. Think about that. Nearly a third of the effort and cost that used to go into manual troubleshooting can be saved. Your team can redirect that time to more strategic work, like improving the platform or building new data features faster recovery when issues occur. Even when the problem happens, the systems can't fix fully by itself. The automated detection and preliminary action means the eventual human intervention is quicker. The system might already have isolated the issue or tried common fixes, so the meantime to recovery drops dramatically. You're looking at minutes instead of hours for many incidents. Greater scalability of operations. As your data volume and complexity grow, a self-healing system scales much better than a manual one. You don't need to grow your ops team linearly with data. The AI copes with the growth by learning and automating. This makes the platform more scalable from an operation standpoint in an, in a sense, we move from a situation where data engineering teams are constantly putting out fires to one where the platform autonomously handles routine issues. That shift not only saves time and money, but it also gives developers and analysts confidence that the data pipeline won't be the bottleneck or a single point of failure in their projects. Our third pillar is predictive resource optimization. This is about managing the computational resources of the platform, things like CPU Memory Storage Network in an intelligent, proactive way using ai. Traditional systems often either overprovision resources just in case, or you reactively scale up after you notice performance lag or high load, which can be too late. Neither is ideal. Predictive optimization uses machine learning to anticipate demand and edges resources ahead of time. Here is the breakdown of how it works. Learn patterns. The platform looks at historical usage data. For example, it learns that every weekday at 9:00 AM there's a spike in dashboard quiz, or that end of month processing uses a lot of CPU. By analyzing these patterns over weeks and months, ML models learn the typical workload cycles. It's similar to how a smart assistant might learn your routine forecast demand. With these learn patterns, the AI can predict future. For instance, it might forecast that tomorrow's web traffic will be 20% higher due to a marketing event, or that in December you'll need extra storage for air end data. It anticipates both regular cycles and unusual events. If it is fed the right signals, optimize resources based on the forecast, the platform proactively allocates or adjusts resources. This could mean spinning up additional server instances at 8:50 AM just before the users login. So performance stays smooth or automatically scaling down some services during weekends to save cost when demand is low. Essentially, it tunes capacity in advance as instead of waiting to react, it's like packing an umbrella because the weather forecast said there is a high chance of rain. You were prepared ahead of time. Measure results. After acting the system watches what actually happens and compares it to the prediction. Maybe it's predicted a 20% spike, but it was 25%. It then learns from the discrepancy and fine tunes its model. This is a feedback loop. Over time, the predictions get more accurate and the optimization strategies improve. The system continuously learns and adapts to changing usage patterns. In non-technical terms, predictive resource optimization makes your platform run like a well oil machine, always sufficiently power for what's coming, but not excessive. It's the difference between always driving your car with the pedal to the metal just in case, versus using cruise control and predictive GPS that knows a hill is coming and accelerates a bit beforehand than eases off and coasting. The result is smoother performance and far better efficiency. What benefits can we actually get from this kind of predictive optimization? Let's look at some real results achieved with AI driven resource management. Utilization improvement systems have seen around 43.8% improvement in resource utilization. In one comparison, using ML based workload forecasting, outperform traditional threshold based. Auto scaling by that margin Concretely, this means the hardware and cloud instances you're paying for are being used much more efficiently. Before maybe servers sat idle 60% of the time as a safety buffer. After implementing AI predictions over provisioning dropped from about 61% to just 12.3% while still maintaining performance. The platform isn't holding a bunch of extra capacity that never gets used. It's closer to a just in time model For compute, this efficiency is a direct cost saver, cost reduction, because of better utilization, companies saved a lot of money on our, on average midsize deployments, saved about 476 K per year by using AI to optimize resources. In fact, the investment in the, in these optimization algorithms paid back three 80% in the first year. A three 80% return on investment is huge. Essentially, for every dollar put into implementing the solution, they got back three 80 back in savings within that year. This kind of ROI indicates that the optimization not only covers its own costs, but generates significant net savings very quickly. Performance enhancement, it's not about cost, performance, reliability improved too. SLE violations drop by 76.2%. That means far fewer incidents where the system's performance fell below. Agreed targets. Like slow response time or downtime. By predicting and preventing resource bottlenecks, the system can meet its performance targets much more consistently. Over three quarters of the issues that used to break as is were eliminated handling 3.2 times workload variability. The platform became much more robust against traffic spikes and variability in tests. It could handle 3.2 times more variations in workload. Without performance degradation compared to before. So if your user load suddenly triples, think flash sale or a wire event, the system can absorb it gracefully. This is a big deal for scalability. It means fewer emergencies when load searches unexpectedly because the system likely already scaled itself in anticipation and can react fast enough thanks to the predictive head start. Overall, these results show a win-win. Greater efficiency and better performance. Often. In the past we had a trade off cost versus speed. You'd overprovision to ensure speed or save cost, but risk slowdowns AI lets you get both lower cloud builds and a faster, more reliable system. It's a great example of optimization done right now let's talk about the fourth pillar embedded governance and ethical ai. This is all about trust, security, and compliance built into the data platform. Traditionally, data governance, ensuring the right people have access to the right data. Data is issues used ethically, privacy is protected, et cetera, has been a very manual set of processes. It often feels like a gatekeeper that slows things down. Example, waiting weeks for approval to access a data set or big audits to ensure compliance with regulations like GDPR. In a AI driven platform, we turn governance from a hurdle into an integrated automated safeguard. The platform itself helps enforce policies and ethical guidelines continuously so that it's not all manual checkpoints. Let's break it down. What the entails. Automatic detection. The platform automatically identifies and classifies sensitive data. For example, it can scan incoming data and recognize this column looks like social security numbers are. This dataset contains personal health information using ai. It can even detect things that aren't just based on simple rules, say it learns to flag a combination of data fields that together are sensitive. By knowing where all the sensitive or regulated data is, we have the foundation to handle it properly. Policy enforcement. Once data is classified, the system can apply the appropriate protections automatically. If something is tagged as confidential, the platform might automatically encrypt it, mask it, and analytics or restrict who can query it. The idea is that rules like financial data X can only be seen by those roles or mask out customer names when data is used for analysis, are enforced by the system at runtime, not just written in a policy document. This reduces human error and makes compliance consistent. Continuous monitoring governance isn't a one-time thing. The platform continuously monitors data usage for compliance risks. It watches for unusual access patterns. For instance, if someone is s querying an abnormal amount of sensitive data at 2:00 AM it might flag or block that as potential breach. It also ensures that as new data comes in. As regulations change, it keeps everything in check in real time. This is like having a security guard on duty 24 by seven inside your data platform. Always alert, transparent decisions. A critical part of ethical AI is transparency. The system provides explanations for automated actions. If it blocked the user's access or encrypted a data set, it'll log or even inform you why. This day-to-day set contains HIPAA protected data, or so direct access was restricted. The transparency is key to trust both internally, so data teams understand system behavior and externally. Imagine how regulators or customers clear audit trail of how data is handled. We don't want a black box governance, ai. We want a glass box that shows its reasoning. Ultimately embedded governance means compliance and ethics are not an afterthought. They're bake into the daily operations of the data platform. It ensures that as we automate more, we are doing so responsibly for the organization. This means safer data, fewer compliance nightmares, and a strong ethical stance by default, rather than depending solely on periodic manual checks. It turns out doing governance right with AI and automation doesn't just avoid problems. It can actually speed up your organization and build trust becoming a competitive advantage. Let's look at some metrics that companies have reported after embedding governance into their data platforms, operational efficiency. Automated governance dramatically cuts down the bus, busy work and compliance. For example, companies saw a 67% reduction in audit prep time, preparing for audits, used to take months of gathering records, permissions, data, lineage information. Now, a lot of that is available on demand via the platform's, logs and catalogs. They also saw 43% improvement in audit success rates. Meaning far fewer findings or issues since the system was already keeping things in line. Another impressive figure. 94.8% sensitive data classification accuracy. The AI is identifying sensitive data, almost 95% correctly, which is better than most humans could do at scale. High accuracy here means you really mis tagging something that needs protection. Giving you confidence that nothing is slipping through the cracks. Agility and compliance embed, embedding governance actually makes the organization more agile in how it can use data because the guardrails are automated. One metric data access requests that used to take nearly nine and a half days on average. All the approval sticker checks now can be fulfilled about 12 minutes, in about 12 minutes, basically in near real time, once the proper criteria are met. That's a game changer for analytics or scientists who need data now rather than two weeks later. Another metric, adapting to new regulations went from 97 days to 26 days when a new law or policy comes out. Because much of the governance logic is in software, you can update rules in the platform and achieve compliance in a few weeks instead of a quarter. And importantly, unauthorized access incidents went down 82%. The continuous monitoring and automated controls shut down a lot of opportunities for mistakes or malicious access. So there are far fewer security incidents. That's a huge risk reduction market trust. Strong governance boosts your external reputation. Companies with this autonomous governance measures got regulatory approvals 28% faster. For things like new product launches or certifications, regulators trust them because they can see robust controls in place. So the approval process, speed up customer confidence in how the data is handled, jumped from about 51 to 74% in surveys. That's a big leap. When customers know you take their data seriously and have the systems to protect it, they're more comfortable doing business with you. And notably, legal challenges and disputes related to data dropped by 76% fewer lawsuits, fines, or public relation issues occur when you are on top of privacy and ethics. In summary, good data governance isn't just about avoiding penalties, it's about it builds trust with customers and regulators. Which is invaluable for business continuity and brand reputation. So this pillar turns what used to be seen as a check boss or drag on productivity into a source of speed and trust. It empowers the organization to use data more freely and to confidently show the world that it can be trusted with data. It's absolutely a competitive advantage in today's data-driven market. We have talked through the four pillars, metadata intelligence, self-healing, pipelines, predictive optimization, and embedded governance, and seen how each improves a part of the data platform. Now let's zoom out and conclude by painting the picture of the future, the truly autonomous data platforms the. The theme across all these pillars is moving from manual reactive ways of working to automated, proactive, and intelligent systems. On this slide, we sum it up as a series of transformations from documentation to active intelligence. Metadata is no longer just passive documentation sitting in a catalog. It becomes an active living part of the system. In the future when new data comes in, the platform doesn't just note it down. It actively understands it, organizes it, and even uses that knowledge to automate processes. It's as if your documentation comes alive and start doing some of the work for you. From brittle to self hailing pipelines, data pipelines today can be fragile, breaking with small changes. We are moving to a world where pipelines are self-healing and resilient by design. This means a minor schema change or a spike in data volume won't bring things crashing down The pipeline adapts or recovers on its own. The future pipeline is like a robust organism that can heal wounds quickly rather than a house of guard. This drastically reduces failures and maintenance effort from reactive to predictive resource management instead of the ops team reacting to outages or slowdowns. Chasing after issues, the platform will predict and prevent many of those issues. Resource management and tuning will largely be on autopilot. The systems will know, for example, that end of quarter is coming, let's allocate more capacity now, or, this query pattern looks like it could cause a bottleneck tomorrow. Let's opt optimize it now. We move from always being on the back foot to being a step ahead. The result is smoother performance and happy users who don't even realize how much foresight is happening behind the scenes from control to empowerment. Governance shifts from a strict control mindset to an empowerment mindset. Yes, you can use this data because the controls to do it safely are already in place and automated. In the future, governance is an enabler, not a roadblock. It gives people confidence to explore and use data freely within safe boundaries. This means data innovation can flourish because the platform is both open and safe. People get to work with data faster, and the system widely ensures it all. It's all compliant and ethical when you put it all together. Future autonomous data platform is one where a lot of the heavy lifting, like finding data, fixing errors, tuning performance, checking compliance is handled by the platform itself. It's analogous to how modern cars app lane assist, automatic braking and self parking. We are adding those kind of intelligent features to data systems. We, as data professionals get to focus more on strategy, analysis and innovation. Rather than babysitting pipelines, or chasing now issues, this transition from manual to self-optimizing systems is already underway. As these pillars mature, we will see data platforms that can run with minimal human intervention With us, providing high level guidance and handling the exceptions, it's an exciting future, one that turns the growing data uch from a challenge into an opportunity. Because our AI driven platforms will be ready to handle it. Thank you for listening. I hope this gave you a clear picture of how AI can fundamentally improve data platform management. With that, I wrap up and I'm happy to take any questions or discuss further. Thank you.