Hi everyone. Thank you for joining me today at Con 42. My name is, and I'm honored to present on the topic of transforming public sector observability using artificial intelligence and business intelligence with over 12 years of experience. As I said, the business objects business warehouse and Power BI specialist and experience on having on working on Microsoft Power BI Stack. I've led digital transformation initiatives for major enterprises, including Nestle, USA, Siemens, and artex. Currently I serve as a principal developer and analyst at I Global Tech. Where I focus on bridging the gap between the technical data solutions and real world public sector needs. Today's session is designed to equip you with the understanding and tools needed to evolve the public sector observability. We'll explore how to use ai, bi, and modern data platforms to convert the fragmented system telemetry into actionable intelligence that improves service delivery and public trust. Observability challenge when we come up to the observability challenge. Let's begin by acknowledging the the core challenges faced by public sector agencies. First complexity overload. Data is being generated across the multiple disconnected systems, including mainframes, modern cloud platforms, and and customer databases. This fragmented environment makes it incredibly difficult to gain a full picture of operations. Second visibility gaps key insights are often buried within individual systems. These silos prevent leaders from understanding the end-to-end flow of services and outcomes. For example, the performance issues in a backend tax processing system might go undetected until constraints begin experiencing delays. And the third one would be the reactive operations. Too often most of the enterprises only. Become aware of issues after a disruption has occurred. And this not only reduces the service quality, but also erodes the customer's trust and and the agency's ability to fulfill its mission effectively modern observative framework to. To address these challenges, we introduce a modern observative framework which is built in four pillars. First one would be the business intelligence, where we use the business BI tools like Power bi to transform the disparate law, raw data into dashboards and score. And this enables decision makers to proactively monitor and manage services. Second one would be the AI powered artificial intelligence which plays a critical role in predicting the system behaviors. Identifying the hidden patterns or finding out the anomalies, and recommending the preventative actions. Machine learning models detect issues before they escalate. Third one would be the data integration where true observability requires merging data from all systems regardless of vendor location or format into a single view. This unified approach of allows for seamless data analysis across agencies. And the fourth one would be the core monitoring where we have the comprehensive collection of metrics. Which are very, which are pretty continuous and and continuous logs, traces and events without a robust telemetry. Foundation it is impossible to gain real visibility into the health and and the performance of the services. These frameworks allows the public sector teams to shift from reactor to the predictive Operations Technical Foundation. So let's, when we explore this framework, it is, which is technically implemented at the data collection, where we establish the pipelines. To acquire the telemetry data from legacy on-prem systems to cloud services, third party APIs and IOT devices. This requires the co connectors, agents, or integration services that that normalize the data. And a centralized analytics engine often built using star spark Databricks similar platforms, processing incoming telemetry. It correlates logs with performance metrics and generates insights in real time. The next one would be the storage. For compliance purposes, we store data in secure cloud-based data lakes. Or warehouses. Solutions like Azure data Lake or Snowflake are ideal because they offer the encryption for data and and which has also have the role based access and max accountability. Next one would be the visualization where dashboards are tailored to specific agency roles. Which includes these agency roles. When we talk about the agency roles, it's like the role based role based, access related dashboard where it involves executive level, operational level, and technical level. Power BI and Tableau and SAP business objects serve these needs by offering the customizable real time visual layers that align with KPIs. This layered architecture ensures that the data is being trusted, timely, and actionable based, actionable insights for the enterprises. Transformation success stories. Here are the three real world examples, which I, based on my experience, which I would highlight the effectiveness of this framework. First one would be the legacy migration where we revolutionize the nest USA's outdated monitoring infrastructure with seamless integration of modern observability tools. And also. The aging. We Nestle used to have the aging monitoring system that couldn't scale or provide the cross-functional visibility. We introduced the modern observability tools indicated with their existing landscape. This resulted in a 40% faster incident response time and and significantly improved the IT and business team collaboration. Next one would be the would be sware. We created the cross-platform observability ecosystem that merged 12 plus data streams into a unified view. And this gave Siemens the operational leaders the ability to see end-to-end service health, reducing the system downtime by 35%. Next one would be the, with Arthrex, where the company needed a way to identify the performance degradation before it affected operations, where we implemented an AI driven no detection system that flagged early signs of failure. Then this way a solution prevented almost 28 major incidents in a year and saved up to $1.5 million in potential downtime cost, downtime costs, and to, and to avoid the disruptions. These stories show how the observatory goes beyond the monitoring and it empowers the predictive operations implementation roadmap. Where we implementing the observability in public sector organizations requires a systematic phased approach. And and the first one would be the assessment where we evaluate the current monitoring capabilities, where we begin conducting the detailed inventory of of your existing monitoring tools, data sources and workflows, evaluate gaps, redundancies, and compliance risks. The. Stage should also I mean involve the cross-functional teams to understand their their burning issues of the bottlenecks that they're gonna face when they're trying to implement the, implement This, data points architecture would be the second step where we design the observability architecture based on the assessment. Define which telemetry signals need to be collected. The a TL workflows is needed and ai ml models to be deployed storage schemas and visual interfaces. Prioritize, prioritizing this modular and scalable design. And the third one would be the implementation where we roll out in phases. Start with the proof of concept in a high priority service and such as backend and emergency response systems and and con configuring the alerting rules AI triggers and dashboards where we use the agile sprints to a iterate quickly. And the adoption would be the fourth one where we, where the technology alone doesn't guarantee success, where we trade staff on the observability tools establish documentation and onboard executive champions create feedback loops and continuous me measure usage, system performance, and user satisfaction. So by this roadmap, the agencies can avoid and our enterprises can avoid. Most of the companies can avoid the analysis para paralysis and and achieve the, measurable progress in three to six months, or the desired time period, which we, which during the product planning phase. The next one would be the technology stack where this gives you the effective observability on well integrated technology stack which has a bunch of tools where which is, describes the, describes its own importance on performing the front end and and backend calculations, and also back, and also performing the machine learning stuff. Power bi, which is a leading visualization platform where it provides the customizable dashboards that pull in realtime data through APIs and direct query and import models. Power BI also supports the role level security, which is also added for the sensitive public sector data or and on and also in private organizations, SAP business objects, which is, which remains critical for agencies that require a governed enterprise reporting. Compliance based scheduling and complex report logic where where most of the semantic model has been performed in business objects and s business objects has disparate. Values, and connecting to disparate data sources which integrates with the S-A-P-E-C-C-I, which is a backend business warehouse and other ERP systems, Azure, and Snowflake, which serves as a infrastructure and the data warehouse layers. Azure supports identity management. Data pipelines and AI services. Snowflake provides a fast, scalable storage and built-in support for semi-structured and unstructured data. So Databricks, which powers advanced machine learning, it supports time series forecasting, anomaly detection, and NLP on on logs or customer feedback. So this Databricks. Notebooks also allow for the flexible experimentation by data scientists, and this stack is chosen, not for the technical strength, but also for the compliance, scalability, and long-term cost effectiveness. ROA metrics. When agencies invest in observability and measurable outcomes what can they expect is what this showcases. The, our observability solutions majorly deliver the measurable returns across key performance indicators. With significant improvements in operational efficiency and service quality, the fast flow incident re resolution would be the highest KPI, which would instead of spending the spending hours diagnosing root causes, teams can ask, isolate issues in minutes using correlation dashboards and AI alerts. We have seen resolution times drop by up to 50%. So improved uptime would be the second KPI. With with the productive alerts and proactive maintenance uptime SLAs improved dramatically. This means the fewer customer complaints and higher service continuity and better audit readiness. Increased staff productivity would be the third one where analysts and engineers spend less time annually stitching together data with the centralized dashboards and they've, they majorly focus on the improvements rather than firefighting. Reduce the operational costs. Where, which, in which reduces the manual interventions contact escalations and downtime related penalties in several projects. A total all operational costs dropped by 20% within a year. So higher customer satisfaction where the customers, most of the customers don't notice when things go right, but they do when the systems fail. Improving the uptime and, responsiveness, enhances public trust and stakeholder confidence. Typically the investment in observability pays for itself within nine to 12 months. Next one would be the common implementation challenges where the. Security complaints where the public companies public assure companies must come, must comply with stringent data regulations. It's essential to choose tools that are FedRAMP and FSMA compliant and and implement strict access controls across your observability stack. And second would be the data integration complexity where legacy systems often lack APIs or standardized formats. Teams need to build custom ETL pipelines, use middleware, and sometimes reverse engineer logs. Data integration is the hardest part, but also the most rewarding. The next one would be the cultural resistance or where observability shift the most. Cultural resistance or organizational adoption where most of the. Observability shifts the way people work it. It requires transparency, accountability, and data literacy. Some teams may resist change, fearing loss of control and increased scrutiny. That's why the executive sponsorship and and cross training is more significant and vital. Next one would be the budget constraints where where a, where most of the companies often operate within tight budgets and long procurement cycles. Start with the high ROI pilot and measure value early and secure. The incremental funding to scale further. Anticipating these challenges allows for the smoother implementation and higher adoption rates. Next steps would be. Would be to find out the observability of your organization. To bring the observability to the, your organization. Here are the practical steps where observability assessment, which evaluates your current monitoring capabilities and identify the key gaps where we, where it, where it is needed to run a two to four week internal review to evaluate what's being monitored and what's missing, and who owns which data streams. Map out the telemetry architecture. And next one would be this would be the pilot implementation where we select the pilot use case, where we choose a high visibility service such as the customer portal, performance. Distributor, if there is any distribution, monitoring, or emergency services response team or something like that. And demonstrate a tangible which demonstrate the tangible results within 30 to 60 days. And also building the strategic roadmap where we develop the phased approach to full observability implementation, where we define short, midterm, long-term goals, which includes tool standardization, AI model expansion, and training models. And, ensure the alignment with the agency's digital strategy. Investment in training and literacy would be the, another observability where we, which also comes under the cap capability building. And this observability is, it's for the analysts leadership and operations team, and we offering the workshops. Create document, creating the documentation and fostering for the cultural culture of the data driven decision making would be the best one under this category. And also it also if the internal bandwidth is limited. Then it's considered to be engaging the experienced partner to accelerate the design, deployment and change management. So transformation doesn't have to be big bang but start with one one use case, and then I. Deliver the success and scale it interactively. Thank you for spending this time with me today. I hope you know, have a clear vision of how AI and business intelligence can elevate observability across public sector organizations. Whether you're just a beginning or a scaling existing efforts, the opportunity is vast and the impact is real. I welcome your your questions and and thanks for connecting and let's work together to transform the vis into the visibility into mission success. Thank you.