Hi, everyone. I'm Shashank Reddy Biravelli, currently working in Walmart as a software engineer. I'm excited to be here to dive into a topic that holds immense value for business across all industries. How AI driven solutions are transforming data quality management. In today's data driven world, maintaining high quality data is no longer just important, it's mission critical. Organizations often face challenges in ensuring their data remain accurate, consistent, and complete across all systems. Without strong data quality management, there may be business unreliable decisions, missing valuable opportunities, and incurring significant financial losses. AI driven solutions are completely transforming the way we validate, clean, and maintain data at scale, making data management not only more efficient, but also more reliable than ever before. The AI revolution in data quality is transforming how businesses manage their data. Predictive validations use advanced AI algorithms to analyze patterns and forecast up to 95 percent of potential data issues. before they affect operations. Trastically reducing cost errors. Automated cleaning systems continuously monitor and cleanse data in real time. Eliminating duplicates, standardizing formats, and correcting inconsistencies. with 99 percent accuracy. Meanwhile, real time enrichment engines automatically enhance data by adding valuable content from trusted sources turning simple records into powerful business intelligence. This level of automation and intelligence ensures data stays accurate, reliable, and ready for impactful decision making. The tangible business outcome of AI driven data quality management are significant. Thank you AI solutions can improve data accuracy by 40%, leading fewer errors, more reliable reporting, and more confident decision making. They also reduce operational cost by 35%, by automating manual data quality process, which eases the burden on IT teams and data analysts. Additionally, AI systems process data to 50 times faster than traditional methods. enabling real time quality monitoring and instant correction of data issues. These improvements result in more efficient, cost effective, and reliable data management across organizations. Let's take a look at some real world success stories where AI driven data quality solutions deliver impressive results. A Fortune 500 bank reduced its manual data cleansing by 77 percent from 120 per week using AI powered validations. This change eliminated 2. 3 million in annual operational cost and improved compliance accuracy to 99%. And Automotive Manufacturing implemented AI quality control systems. across 12 global plants, achieving a 85 percent improvement in data accuracy. This transformation eliminates 150k production errors annually and reduced material waste by 4. 2 million. A national healthcare network uses AI to enhance patient data management. boosting forecasting accuracy by 64%. This improvement led to optimized staff scheduling across 23 facilities, reducing wait time by 40 percent and saving 5. 8 million in operational cost. These examples highlights how AI driven data quality solutions are creating tangible value, driving efficiencies and delivering significant cost savings across various industries. AI leverages cutting edge machine learning models to process and validate data at a speed of up to 850 kbps. This enables instant anomaly detection and pattern recognition across massive datasets. ensuring high quality data is maintained in real time. AI algorithms uses sophisticated neural networks and assembled learning techniques to achieve 99 percent accuracy in data validation, correction, and standardization. This ensures that data, regardless of type or format, is clean, reliable, and ready to use. By combining deep learning with predictive analytics, AI transforms raw data into real time data. actionable business intelligence. This results in 40 percent more accurate sales forecast, 65 percent better customer segmentation and doubling of market campaign ROI, empowering business to make more informed and effective decisions. These advanced AI techniques make data quality management faster, more accurate, and more valuable, turning data into strategic assets for organizations. Now let's take a look at some key implementation best practices for ensuring the success of data quality initiatives. Start with a pilot program in one critical business unit, validate the results, then systematically expand AI implementation to other departments. Based on the lessons learned and measurable ROI. This gradual approach helps ensure smooth adoption and minimizes risk. Conduct thorough data audits to identify your most valuable and vulnerable data asset. Focus AI implementation on data sets that directly impacts revenue, compliance and customer experience. Ensuring that the AI system is aligned with your business top priorities. Establish clear KPIs and automated monitoring systems to track AI performance, data quality metrics, and overall business impact. Regular reviews and proactive adjustments are essential to maintain, optimize results, and adapt to changing business needs. By following these best practices, business can effectively migrate AI driven data quality solutions. and achieve long term success. Organizations that implement AI driven data quality solutions see a 67 percent reduction in system integration issues, leading to an average annual savings of 2. 3 million in troubleshooting and maintenance costs. Companies typically achieve full return of investment within just 14 months, with an average ROI 3 years. This is driven by increased efficiency, reduced errors, and improved decision making capabilities. AI driven data quality solutions result in 45 percent faster time to market. 32 percent increased in customer satisfaction and 28 percent revenue growth. These outcomes are directly tied to enhanced data driven decision making and operational excellence. These statistics highlight how AI driven data quality not only saves costs, but also accelerates business growth and performance. Now let's take a look at AI impact on business intelligence. AI Power Data Quality is transforming marketing effectiveness, boosting campaign performance by 56%. This leads to 3 times higher conversion rates and an average 40 percent increase in return on ad spend. Making marketing efforts more cost effective and impactful. Advanced AI algorithm enhance customer segmentation accuracy by 70%. Enabling hyper personalized experiences. This results in 45% increase in customer lifetime value, and a reduced in churn by one third, driving stronger customer loyalty and retention. A automates data validations and standardizations reducing compliance, supporting time by six 6% while achieving 99%. This saves thousands of staff hours virtually automating costly complaints or ions. and ensures organizations stay ahead of regulatory requirements with ease. These AI driven improvements in marketing, customer engagement, and compliance, not only streamline operations, but also provide significant boost to business performance across Let's now focus on the future of AI driven data quality. Predictive AI algorithms will transform data analysis by uncovering hidden patterns and correlations. This will allow organizations to forecast market trends with up to 95 percent accuracy and gain deep insights into customer lifetime value, enabling more informed business strategies. AI powered automated complaints monitoring will transform data governance. by detecting regulatory violations in real time. This will reduce complaints risk by 80 percent while ensuring ethical data handling across global operations, safeguarding organizations from legal and reputational issues. Real time AI data validation will speed up decision making cycles by 60%, empowering leaders to make strategic decisions based on continuously verified and reliable data. High quality data streams from across the enterprise. This shift will result in faster, more accurate decision making at all levels of the organization. The future of AI in data quality management promises more accurate predictions, stronger governance, and faster data driven decision making. Positioning business for greater success in an increasing complex and competitive landscape. To wrap up, here are the key takeaways from today's discussion. AI is transforming data quality management by significantly improving accuracy, speed, and efficiency. Enabling business to unlock the true potential of their data. AI driven data quality delivers real world benefits such as improved customer satisfaction, reduced operational cost, and enhanced decision making capabilities. which drive overall business success. To stay competitive, organizations must embrace AI technology. By harnessing its transformative power, business can gain a significant edge in today's data driven landscape. AI is no longer just a tool. It's a game changer for business that wants to make smarter, faster, more efficient. and more informed decisions. Next, start exploring AI powered data quality solutions today. Begin by assessing your current data quality challenges and identifying areas where AI can bring the most value. Focus on high impact data sets that will yield the most immediate improvements. Implement a phased approach, gradually expanding AI driven solutions. As you validate results and measure ROI, by embracing AI driven data quality, your organization will not only improve operational efficiency, but also unlock the true potential of your data, positioning yourself for a significant competitive advantage in the marketplace. 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 driven solutions are transforming data quality.