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Mastering Seamless Data Integration for AI with AWS
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pp__international_ai_with_precisely_and_aws_final_240919.pptx
- 1. Mastering Seamless Data Integration forAI with AWS Arianna Valentini | Director, Product Marketing for Data Integration
- 2. How are companiesusing AI? of leading businesses have ongoing investments in artificial intelligence 91% Source: NewVantage Increase productivity and efficiency Attract and retain customers with personalized service Enhance and accelerate data-driven decisions Create new ideas that provide a competitive edge 2
- 3. Only 4% ofrespondents say their data is AI-ready Pitfalls of AI adoption Risks of AI without the foundation of all your business’s relevant, critical data: • Inaccurate and unreliable outcomes • Bias, unfair, and discriminatory results • Inefficient resource utilization • Damage to brand and customers • Compliance and regulatory failures • Eroded user and stakeholder trust Source: 2023 Gartner IT Symposium Research Super Focus Group 3
- 4. For trusted AI,you need integrated data Real-time Complete Relevant Strategize and drive your AI/ML initiatives with a business outcome driven approach
- 5. Building a soliddata integration strategy Define goals and scope of data integration projects Assess current IT landscape and identify data sources Choose the right approach, tools, and partners Design and implement data integration architecture Ensure quality and governance throughout the process Monitor and optimize performance and outcomes 5
- 6. Impacts of fullyintegrated data on AI efforts When AI leverages consolidated, accessible data it fosters: • Creation of innovative products and services • Agility to adapt quickly to emerging trends When AI uses comprehensive, integrated data it ensures: • Reliable decision-making • Increased value of company data • More precise forecasting When AI models are fed with all enterprise data it can: • Identify undiscovered inefficiencies and redundancies • Streamline workflows, reduced manual tasks, and minimalize errors Increased Efficiency and Reduced Costs Reliable AI Results Faster Decisions in Evolving Markets 6
- 7. Precisely and AWSare partnering to deliver data from your mission-critical systems to enhance AI readiness
- 8. AWS Mainframe Modernization DataReplication with Precisely Quickly replicate mainframe and IBM i data to AWS Cloud Allow business applications to leverage data mainframe and IBM i in near real-time Agility Replicate mainframe and IBM i data for use with AWS Cloud services Use for reporting, advanced analytics, ML/AI, and migration and modernization Innovation Leverage built-in centralized security, monitoring, fail-over and data sovereignty enabled by target AWS Cloud databases Enable data consistency across mainframe and IBM i to the AWS Cloud with near real-time replication Resilience
- 9. Benefits Quickly Replicate mainframe and IBMi data Supports mainframe and IBM i data sources Pay per use by GB of data replicated Resiliency and high availability Built in integration with Cloud DB and AWS Services Observability and auditability
- 10. How it works Quicklyreplicate the data into purpose built datastores on AWS to generate business value AWS Mainframe Modernization Data Replication with Precisely Data Replication with Precisely uses CDC for replication to AWS People Apps Devices Integrate Databases Data warehouse Amazon SageMaker Store, Query, Analyze Act Amazon Bedrock Amazon Aurora Business Intelligence Amazon QuickSight ML Generative AI Amazon Redshift 1 2 3 AWS Cloud Sources Mainframe IMS IBM i Db2 for IBM i Db2 for z/OS VSAM Amazon DynamoDB Amazon MSK
- 11. Use Cases 11 Business InsightsDigital Operations • Web and mobile apps • Near real-time fraud detection • Chat bots • Customer engagement • Business insights • Reporting • AI model training • Generative AI use cases Incremental Modernization • Start with inquiry services Distributed architecture with CQRS pattern • Incrementally offload business functions
- 12. Key takeaways 2 AI projectsneed to be built on a solid data integration strategy 1 AI will make a lasting impact on organizational use cases 3 Precisely and AWS enhance AI readiness 12
- 13. Precisely is yourtrusted partner Powerful business impact We meet you where you are to solve today's challenges and build tomorrow's vision Comprehensive offering Unique combination of software, data and data strategy services Unrivaled experience Decades of domain expertise from a single company Better Data. Better Decisions.
- 14.
Editor's Notes
- #2 map to biz outcomes (read right side), new mechanism for rev (grow rev, manage risk, reduce costs)
- #3 Clear is why – path has many risks, recognize risks up front, all top of mind, hallucination, bias – garbage in and garbage out – more relevant today than in the pass, autonomous system generating output without a human – damage brand, put stuff out there, misuse of efficiency, Adding a layer to this in countries within GDPR are that minimal data is used to build AI models, anonymization must be used in LLMs, delicate balance of having someone’s permission to build on their data. AI systems must integrate security practices to prevent data infringements and unauthorized access.
- #4
- #5 **Step 1: Define Data Integration Goals and Scope** Start by clarifying your specific AI objectives. Different AI use cases require different integration approaches. Defining goals, expected outcomes, and deliverables ensures clarity with stakeholders. Key questions to ask include: What business problems are we addressing? What are the data sources, challenges, and success criteria? This foundation sets the project’s scope and expectations. **Step 2: Assess Your Current Data Landscape** Next, assess your data environment. Conduct an audit of your data sources to identify gaps and issues related to data quality, accessibility, and compatibility. Prioritize critical data sources based on their value and relevance to the project, ensuring you’re addressing security and governance concerns early on. **Step 3: Choose Your Data Integration Approach and Tools** Select the best integration methods and tools based on your project’s complexity. Consider architecture (centralized or hybrid), techniques (ETL, ELT), and platforms (cloud, on-premise). Ensure the solution meets functionality, scalability, and performance needs while staying within budget and resources. **Step 4: Design and Implement Your Data Integration Architecture** Now, design an architecture that efficiently connects, transforms, and delivers data to your AI applications. Define data flows, transformations, and control standards for quality, governance, and security. Test and validate the architecture to ensure it meets project requirements. **Step 5: Maintain Data Quality and Governance** Data quality and governance are critical throughout the process. Assign clear roles for data management, implement tools for data validation and auditing, and continuously monitor metrics such as accuracy and consistency. This ensures reliable and compliant data for AI use. **Step 6: Monitor and Optimize Performance** Finally, treat data integration as an ongoing process. Monitor for bottlenecks or errors, implement optimizations, and continuously refine goals and deliverables as data needs evolve. Regularly review performance to ensure your integration pipeline is delivering value.
- #8 Play up the innovation, AWS recognized Precisely as a leader in the space, joint innovation with AWS, integrations with CloudWatch monitoring and observability, moving to container based deployment easier upgrades, uptime
- #10 Low impact – using logs and journals so no impact to your systems, once it’s delivered open to all AWS flavors (Bedrock, SageMaker) All flavors of Kafka are supported (Apache Kafka, Confluent Kafka, Amazon MSK)
- #13 Precisely is the trusted partner for data integrity, with decades of deep domain expertise that span software, data and data strategy services that empowers you to solve today's challenges while building tomorrow's vision.
- #14 Do you have any customer examples of organizations incorporating data integration to fuel AI projects? Are there elements other than data integration that customers can leverage to make data AI ready?