Hello, very good morning. This is LaMi gta and I'm working in a Starbucks as a senior data engineer. So I have a total like 14 years of experience in IT industry. So today we are going to talk about DataOps accelerating digital transformation through data centric methodologist. Okay here, the evolving software development landscape organizations are increasingly recognizing data as a strategic asset rather than mely an operational byproduct. This paradigm shift has a new methodologies that bridge the traditional gap between data management and application development process. The emergence of data oriented application development represents a significant achievement significant advancement in this direction. Offering a comprehensive framework that integrates data engineering principles with software dev development practices. As it's occurred, DataOps extends the collaborative principles of Dev DevOps to encompass data centric operations. Creating cohesive ecosystem where data pipelines are treated with the Swm reader on automation as a code deployment pipelines. Okay, this methodology encompasses automated data pipeline construction continues integration and deployment for data workflows, robust data governance frameworks, cross-functional collaboration and comprehensive monitoring systems. The significance of DataOps lies in its ability to address several persistence challenges in modern application development. The need for the high quality data delivered at speed, alignment between technical and business stakeholders, and the increasing complexity of data ecosystems, cloud environment. As organizations prioritize data driven decision making, adopting data ops has transitioned from competitive to advanced to your business necessity. This examinees that theoretical foundations implementation strategies and measurable outcomes of data. Data DataOps methodologies across Val various organization context by analyzing both technical and organizational dimensions of data ops adoption. This study seeks to provide a comprehensive understanding of best practices, common challenges, and future directions in this rapidly evolving field. Okay. The next one, evaluation of tra. So the here the traditional data management, the violation from the traditional data management practices to dev data ops follows a trajectory parallel to software development. From waterfall methodologies to Agile and DevOps approaches, the traditional data management relied heavily on centralized data warehousing with the rigid extract, transformation and load, which is called ETL processes, creating significant bottlenecks between data generation utilization. These legacy approaches predominant through through the early two thousands were characterized by. Siloed teams, manual handoffs, and the lengthy development cycles that could span months or even years for complex data initiatives. Next, the big data era, the big data era. So the exponential exponential growth in data volume, ity and velocity brought about by digital transformation initiatives expose the limitations of these conventional approaches. The emergence of big data technologies in the mid 22 thousands provided new technical capabilities, but simultaneously introduced additional complexity in data pipelines. Next DevOps revolution. And organizations struggled to derive timely insights from their expanding data assets. A new paradigm became necessarily to accommodate both the technical requirements of modern data architectures and the business need for rapid it value delivery. So the next the data ops emergence. The data organically around to 2015. As practitioners begin applying lessons from software engineering representative, technological shift and fundamental rethinking of how data teams operate, collaborate, and deliver value. So according to the recent industry research industry, research organizations is implementing data ops methodologies have. Reported up to 70% reduction in data deliveries and data related defects. This marked improvement and efficiency and quality underscores the transform made two potential of data ops in contemporary software engineering practices. Next. Next. So what are the fundamental principles of data ops? One the conceptual framework of data ops is built upon several foundation principles that collectively enable enable efficient, reliable data operations. So here the first one is automation and orchestration. So data ops data ops emphasis automating repetitive task across the data lifecycle, including acquisition transformation, extends the, extends to orchestration. Coordinating multiple data process and workflows to ensure proper sequencing and dependency management. So next it's A-C-S-E-D for data. So we, it's a continuous integration, continuous delivery for data. So similar to code focus, this currently integrating data changes automatically testing their validity and impact and deploying them to production environments through standardized processes. Next so was control and reproducibility. Reproducibility, DataOps advocates for a version control of logic and analytical models. This worsening ensures reproducibility the ability to recreate and recreate any previous state of the data ecosystems. Next, quality by design rather than treating quality as an afterthought data ops integrates quality validation pipeline. So implementing automation testing and monitoring to the detect anomalies before they impact downstream consumers. So these four interconnector principles create a fund of foundation for efficient, reliable and scalable data system that can quickly adopt to, to changing business requirements. Next it's a core components of data data ops, architecture. So the core components of our core components are so monitoring and observability. So comprehensive monitoring extends beyond infrastructure to encompass data, core data quality, lineage tracking, performance metrics business outcome, alignment, and self-healing. Capabilities implementation typically involves specialized data, observability tools, comp, complementing traditional infrastructure monitoring, often employing sa statistical methods to establish standard patterns and detect anomalies. So the advanced teams implements observability as code, ensuring monitoring evolves alongside data systems to maintain visibility during architectural changes. Next, it's a cross-functional collaboration. So DataOps requires collaboration across traditionally separate domains. The data mesh paradigm has emerged as an influential model for organizing teams around data domains rather than technical functions. Other effective structures include DataOps Centers for centers of excellence. Data product teams and guild models successful collaboration framework, share clear ownership definitions, service level agreements, documented interfaces, and regular synchronization mechanisms. Next data governance frameworks. The modern data ops implements governance as code. So where the policies are codified, version control and automatically enforced within pipelines, the key components includes automated metadata management systems, policy enforcement engines self-service government tools, and the granular access controls. So implementation follows if federated model where. The central T Central teams established frameworks, while domain teams define specific rules relevant to their data domains. Next, CSE integration for data workflows, the continuous integration, continuous development for data workflows adopts. Software development practices to data operations. This integration encompasses virtual control for the pipeline components and automated code correctness and data quality testing and control deployment strategies like blue, green, or can releases. So the data specific CSAD pipelines include additional impact analysis and continuity testing stages. With specialized patterns, like the expand contract approach, enabling non braking schema changes data. Next, the data pipeline automation mechanisms. So the data pipeline automation forms the foundation of data ops, transforming manual processes into streamlined workflows. Modern implementations lowered container radiation technologies like Docker for consistent environments and orchestration tools such as Apache Airflow Perfect and Dragster for defining complex workflows as a code. So specialized tools for data transformations like depth and stream processing frameworks such as Apache Kafka, addresses, spec specific needs. At the same time, cloud providers offer manager services this simplify implementation with the prebuilt connectors and the serverless execution models. So DataOps Architecture integrates these components into a system where data pipelines receive the same engineering engineering rigor as application code. Modern implementations use the business to simplify deployment while enhancing scalability and resilience. So the next one, the implementation strategies and best practices. So here the organizations must cultivate a data driven culture with a psychological safety that encourages experimentation. The cross-functional literacy and the clear data. Product ownership models are essential prerequisites for eliminating our fund data and ensuring proper stewardship across the data lab cycle. So effective implementations follow follow a phased approach. So far there are assessment phase, file phase, expansion phase, and maturity phase. So now we are talking about what is what are the different phases involved here? The assessment phase, the ba it kinds like benchmark capabilities, identity bo identify bottlenecks, established baseline metrics, and define success criteria aligned with the business outcomes, and that it selects a high value use case, build essential capabilities while delivering results and create proof points for stakeholders. And the next expansion phase. So in the, in this expansion phase, we extend implementation across priority data domains, develop repeatable playbooks and the next it's a maturity phase. So establish continuous improvement processes, benchmark against industry strands, and maintain alignment with the strategic objectives. So the successful data ops requires executive sponsorship as a strategic business initiative, not just a technical project. It's not it's not just technical project. Cultivate a data-driven culture where, and with a clear data, product ownership and accountability frameworks. So the organization frequently encounter challenges with legacy system integration, skill gaps, go governance concerns and cultural resistance. The successful implementation addresses these these, the abstraction layers for legacy systems, cross training programs, automated compliance checks and change management initiatives, emphasizing education and early wins. S the next one is some measuring data ops success. So the measuring there, how we can measure the data ops success in the four different elements. It's a pipeline performance, data quality, and time to insight and research utilization. So we have a there is 65% improvement in build success rates and deployment frequency through automated workflows and continuous integration processes. So the data quality, so we are like on our like 48% enhancements in data completeness, accuracy, and the consistency through systematic validation protocols and automated testing. So the time to instance insight. There is a 70% reduction in analytical cycle times, dramatically accelerating decision velocity and the builder responsiveness to emerging opportunities and the resource utilization. So there is like 42% improvement in infrastructure efficiency and cost optimization through intelligent workload management and cloud resource optimization. Yeah. Measuring the DataOps success requires a multi-dimensional approach is balancing technical metrics and business outcomes. The DataOps maturity model provides the structured framework for evaluating capabilities across six dimensions from ad hoc level one, two, optimize level five. Next economic implementation imp implications of data ops. DataOps requires that capture direct financial impacts and a bro broader value creation. So these frameworks analyze implementation, operational, and transition cost against the structured benefit taxonomy beyond simple ROA calculation. Leading organizations employ sophisticated economic models including net present value analysis and real options valuation. To accurately capture the full economic impact of data ops initiatives. So the data ops in data ops enhance operational efficiency through process automation, which is which which can reducing manual effort by 40 to 60% for routine task and error detection. Which is following the shift left principle to minimize the costly remediation cost. Sorry. Next it's resource utilization optimization. So particularly in cloud environments and reduced overhead coordination, so it is decreasing of meetings and status communications by up to 30% through transparent automated tracking. The global data ops market says was valued at approximately $2.1 billion in 2023, and is projected to reach 10.5 billion billion dollars by 20 20, 20 30. So the long-term sustainability derives from self self-reinforcing improvement cycles rather than one time efficiency gain. Critical aspects includes technical depth management, which is systematically addressed, suboptimal suboptimal solutions, knowledge preservation, which is reducing organizational dependence on specific individuals and economic adaptability, rapidly adjusting to changing business requirements and market conditions. Next DataOps Emerging Technologies. Here the integration with artificial inte intelligence, integration with artificial intelligence and machine learning workflows. So the convergence of DataOps and a ML workflows has led to integrated practices often terms. AA Ops or M ML enabled data ops. The key integration points include future store, that pro detection capabilities that identify when explainability frameworks that trace predictions through models to underlying data transformations. Next edge computing. So the edge computing introduces unique data ops challenges requiring specialized approaches. These include efficient data filtering and aggregation, inter intermittent connectivity management through store and forward architectures and model deployment mechanisms that can package and verify AI ML models across heterogeneous edge developments. Time next, the real time data processing challenges and solutions. The realtime pro processing requirements have driven significant innovation in the DataOps methodologies, stream processing architectures using technologies like Apache Kafka, and Flink Flinger from the foundation, typically employing Lambda or Kapa patterns. Advanced implementations addresses state management challenges through distributed state stores. Implement sophisticated back pressure handling and employ specialized testing methodologies like chios engineering and next. So the impact of cloud native architectures, the cloud native architectures has transformed data data ops implementation patterns, shifting focus from infrastructure management to service composition, serverless computing, and, containerization provide consistent execution environments while infrastructure as code has as code has become standard practice. So the multi-cloud strategies introduce additional complexity address through meta data driven architectures that separate logical data flows from physical implementational details. Okay. And then next, next slide. So the ethical and regulatory consideration. So here, the first one is the data privacy implications. So the data ops practices directly impact privacy production capabilities. Privacy by design, by, by design principles increasingly influence implementations with requirements like data minimization data minimization. And integrated directly into pipeline specifications. Advanced techno techniques including dynamic data masking and home homomorphic encryption have become standard components in privacy. Conscious tool chains. The modern consent management tracks permissions as metadata following long set personal data while jurisdictional routing capabilities dynamically apply. Different processing rules based on data, subject, location, and applicable regulation. Next regulatory complaints. The effective strategies employ unified frameworks, addressing common principles while handling jurisdiction specific requirements Through configurable rules, organizations implement geo-fencing capabilities to enforce data residency requirements. Automated lifecycle controls for consistent retention management and the comprehensive lineage tracking that documents each transformation and access to reg regulated data and next ethical processing. So the ethical consideration have gained prominence as organization recognized the potential for unintended consequences from automated systems. Bias. Direct bias detection and mitigation capabilities are increasingly incorporated into pipelines with fairness, metrics, integrated data integrated into data quality frameworks so that transparency mechanisms have evolved beyond the technical documentation to include accessible explanations for different stakeholders While. Continuous ethic. Ethics monitoring systems compare current processing behavior against baseline parameters to detect ethical drift. Next, accountability. So it extends beyond specific regulatory concerns to broader governance objectives impact AXA assessments. Frameworks were increasingly integrated into workflows, particularly for high risk applications. Organizations implement human in the loop checkpoints through orchestrated approval workflows, documentation automation to maintain accurate operational records and vendor assessment controls that provide consistent governance across the entire data supply system supply chain. Okay. The next one, real world applications of data DataOps. So there are plenty of services using the DataOps DataOps applications. The first one we can talk about financial services. The organizations on undergoing digital transformations have lowered DataOps to modernize legacy systems while maintaining business continuity. Financial solutions like JP Morgan Chase have implemented data ops to consolidate disparate data sources, enabling realtime fraud detection and the personalized customer experiences while meeting while meeting string and regulatory requirements. And the next field, it's healthcare. The healthcare provides have adopted data ops to improve pa PA patient outcomes and operational efficiency. Mayo Clinic's implementation integrated clinical, operational and financial data streams to enable predictive analytics for patient admissions, resource allocation and treatment effectiveness while maintaining H-I-P-A-A compliance through automated governance tools governance controls. Next its manufacturing, so manufacturing companies have deployed data ops production processes. Process through throughout iot integration. Simons uses DataOps methodologies to process sensor data from production environment, enabling predictive maintenance that has reduced downtime by 30% while improving product quality through real time process adjustments. Okay, and the next, next we can talk about what is the future directions of data ops. So the evolving methodology. Next, the first one, like the evolving methodologies, the emerging trends, reshaping DataOps include declarative pipeline specification approaches that define desired outcomes rather than specific steps and the contracts that co codify expectations between producers and con consumers. Self-healing pipelines with automated remediation capabilities. And democratization. Democratization of DataOps through low code and no code platforms that make development accessibilities to non specialized specialists. The technical disruption, several technologies show potential for significant disruption to current practices. The knowledge graphs and the semantic technologies are gaining adoption for complex integration scenario. Quantum computing presence possibilities for the specific processing challenges. Synthetic data technologies are advancing for privacy, pre preserving development, and anonymous systems provided by powered by Reinforcement Learning, learning show promise for self-optimizing data, infrastructures. Next, integration with the other disciplines. The DevOps will likely see deeper integration with MOPS and DevOps DevOps, creating unified frameworks and spanning spanning the entire digital value chain and expect extending beyond enterprise boundaries. So finally, I conclude that the data ops represents a transfer May two paraic shift in how organization. Conceptualize, implement and manage their data ecosystems by integrating software engineering principles with the data management practices, data practices, data ops addresses, addresses the critical challenges of data velocity, quality and governance that have historically limited the business value of data initiatives. Mature DataOps implementations deliver benefits across multiple dimensions. Which are like technical gains via reduce it cycle times and improve quality economic advantages through optimize resource allocation and cost reduction and the strategic value creation through enhanced organization agility and innovation capacity. The future evaluation of DataOps will like likely see deeper integration with adjacent disciplines like ML ops and DevOps. Increase autonomy autonomy through AI powered self optimization and expanded scope to en income encompas cross organization data ecosystems for organizations committed to becoming. Truly data driven and data ops provides not just a framework for transforming data from passive asset into a active driver of business value. So that's how I brought the data ops. Thanks for giving opportunity, and if you have any questions, please reach out to me. Thank you. Have a good day.