Hi, everyone. I'm Shashank Reddy Pirevelli, currently working in Walmart as a software engineer. I'm excited to be here to explore a topic that has a tremendous value for business across industries. How AI improves data quality for better business insights. In today's data driven world, accurate and reliable data is the backbone of any successful business decision. However, as we know, data is often unstructured, incomplete, or inaccurate. These data quality issues can result in poor decision making, missed opportunities, and inefficiencies. In this session, we will explore how artificial intelligence is transforming data quality management by offering advanced solutions. We will also dive into real world challenges as well as how AI approaches in sectors like financial services and manufacturing. Today, we are facing a number of challenges across data quality. Let me highlight a few key issues. Data team often spend about 40 percent of their time dealing with quality issues, which can consume up to 25 percent of operation budget. This result in substantial loss of productivity. Next, we have the issue of critical data gaps. Many enterprise systems functions at only 75 80 percent completeness, leaving important insights hidden due to missing or incomplete data. Another challenge is the issue with duplicate records. Around 25 30 percent of customer and vendor record contain duplicates, resulting in waste resources and leading to inconsistent customer interaction. Lastly, corporate data become outdated at a rate of approximately 23 percent per year and B2B data is even more volatile, degrading by 36 percent annually. This rapid rate of data decay makes it increasing difficult to rely on information for accurate decision making. These challenges not only affect productivity, but also hinder overall business performance and growth. Now let's talk about how AI power solutions are transforming data quality management. With advanced AI algorithms, we can automatically detect and fix data inconsistencies. This reduces error rate by an impressive 67 percent and process data 50 times faster than the traditional methods. Moreover, natural language processing models are now being used to standardize data across multiple languages, achieving 94 percent accuracy and ensuring consistent data quality and on a global scale. Additionally, using advanced machine learning models, AI can intelligently fill data gap with 91 percent accuracy, eliminating the need for manual data entry. In real time, AI validation system can process up to 850k records per minute, maintaining an impressive 99. 5 percent accuracy. This ensures errors are detected and corrected before, after, and after. The impact critical business decisions. These AI solutions are drastically improving both the speed and accurate of data management, making it easier to ensure high quality data across the board. Let's now look at the impact of AI powered solutions in financial services. Thanks to machine learning algorithms. We can accelerate transaction validation and fraud detection by 250 times. This significantly speeds up the processing and validation of transactions, allowing business to respond to potential issues much faster and more effectively. Next, we observe 35 percent reduction in cost. By leveraging smart automation and predictive maintenance, operational expenses are cut down by 35%. At the same time, service quality is enhanced. resulting in more efficient and cost effective operation. Then there is a 92 percent reduction in errors. Advanced pattern recognition significantly reduces complaints related data error by 92 percent, minimizing the risk of cost, regulatory mistakes, and penalties. Lastly, we save 6. 6 percent of time. Automated data collection and validation process reduce regulatory report time by 6. 6 percent, allowing staff to focus more on strategic tasks and significantly boosting overall productivity. These improvements not only boost operational efficiencies, but also mitigate risk, lower cost, and enable more efficient decision making in financial sectors. Next, let's explore how AI is transforming supply chain data optimization in manufacturing. AI powered validation systems continuously monitor production data in real time, catching defects before they can impact product quality. This ensures we maintain a 99. 7 percent accuracy rate. Automated data harmonization ensures 98. 5 percent consistency across global suppliers. Eliminating discrepancies and achieving seamless data alignment worldwide. Smart algorithms automate supplier data matching and verification, reducing weeks of manual work to minutes and significantly boosting efficiency. Machine learning models analyze historical pattern to predict delivery time with 76 percent greater accuracy, enabling better planning and minimizing supply chain delays. AI 34. 5 million annual savings and significantly enhancing the bottom line. These AI solutions streamline operations, improve accuracy, and save significant time and money, making supply chain management more efficient and cost effective. Now, let's take a look at some key implementation best practices for ensuring the success of data quality initiative. Start by conducting a comprehensive baseline assessment to understand where your data currently stands. From there, establish clear quantifiable objective focusing on customer financial and operational data. This will give you a solid foundation and clear goal to aim for. When choosing the right technology, plan for 42 percent annual data volume growth. Look for a platform that can handle processing data volume up to 2. 5 petabytes annually, with the capacity to scale up to 5 petabytes within the next 3 years. This ensures your system is future proof and can handle increasing data demand. Adopt a phased approach with 10 to 14 week pilot project to test and refine before full scale implementation. Conduct multi layer validation including automated testing, business process validation, and user acceptance testing to ensure the system meets all business requirements. By following these best practices you can ensure smooth, scalable, and successful performance. Implementation of data quality solutions. Let's now explore how AI driven data quality management is enhancing business intelligence. By leveraging AI, organizations have reduced forecast error rate by 64 percent leading to far more reliable business planning and decision making. This helps companies make smarter and more efficient decisions. Confident Decisions. AI driven solutions deliver 92 to 96% accuracy in demand prediction surpassing traditional methods at 75 to 80%. This precision transforming inventory planning and enhancing customer satisfaction by enabling business to respond effectively to market needs. AI has improved forecasting accuracy by 37%, reducing working capital needs by 28%. This frees up resources for strategy investments. While maintaining optimal stock levels and minimizing excess inventory, these improvements have a significant financial impact with organizations saving an average up to 32 million annually throughout precise resource allocation. In short, AI powered data quality management is transforming forecasting inventory management and financial performance, making organizations more agile and effective. Now let's take a look at how AI is enhancing customer intelligence to drive better marketing outcomes and customer engagement. AI has led to 73 percent improvement in customer segmentation precision, allowing business to create hyper target market strategies across more than 12 distinct customer personas. AI driven personalization and real time response optimization has led to 42 percent increase in campaign ROI. Leveraging predictive analytics and proactive engagement strategies, organizations have achieved 35 percent higher customer retention rate. AI has increased customer lifetime value by 2. 1 times through enhanced engagement and data driven relationship management, ensuring customers to stay longer and spend more over that course. In summary, AI is significantly improving how customers segment, engage, and retain customers, leading to high marketing effectiveness and greater customer lifetime value. Let's now focus on how AI is driving operational performance optimization across key areas. Advanced AI algorithm enables organizations to process data 76 percent faster with 99. 9 percent accuracy in validation and cleansing. This ensures faster processing and higher data quality driving better business insights and decisions. AI improves resource utilization accuracy by 41%, optimizing workforce and asset deployment. This ensures resources are allocated effectively, reducing waste and maximizing value. AI driven improvements have cut data management overhead by 34%, resulting in 2. 8 million in annual savings. This reduction frees resources for strategic investments and enhances operational efficiencies. In short, AI enhances speed, decision making, resource allocation, and cost efficiency, driving operational optimization, and boosting bottom line performance. To wrap up, here are the key takeaways from today's discussion. AI powered data quality solutions have a transformative impact on organizations. This significantly improves operational performance, customer satisfaction, and strategic decision making capabilities, empowering businesses to perform better and stay competitive. AI has delivered exceptional results in financial services and manufacturing, driving significant efficiency, accuracy, and cost saving. These sectors leverage AI to streamline operations, improve forecasting, and enhance customer experience. A successful implementation strategy follows a phased approach with clear objectives and comprehensive validations. This ensures smooth adoption and maximizing the impact of AI driven solutions. Looking ahead, AI driven data quality management is poised to become even more crucial in shaping business intelligence and driving operational excellence. As data continue to grow in volume and complexity, These solutions will play an increasingly vital role in helping business stay agile and make data driven decision. Thank you all so much for being part of this session. I truly appreciate your time and attention today. It's been a pleasure sharing insights into how AI enhance data quality for better business