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Data Foundation in the Age of Generative AI
Key Takeaways:
- The world of data has gone through many evolutions over the past three decades, marked by key defining moments like data warehousing, Big Data, NoSQL, and machine learning.
- Data has been the driving force behind these technologies, and now generative AI (Gen) is the latest development impacting data engineering.
- AWS is scaling and evolving its data foundation capabilities to meet the demands of building Gen applications.
What is a Data Foundation?
- A data foundation is a behind-the-scenes organizational strategy that centers around the ingestion, integration, processing, transformation, and governance of an organization's data.
- It is intended to serve the needs of employees, partners, and customers who work with the organization's data.
- The key goals of a data foundation are to enable data-driven decision-making and provide a rich customer experience.
- The benefits of a data foundation include improved data quality, trust, and monetization, as well as better interoperability, reusability, and data governance.
How Data Foundations Change in the Age of Gen
- Gen introduces the need for additional data sources, primarily in the form of unstructured data, which requires metadata discovery and management.
- Data processing phases are influenced by the Gen application building approach, such as feature engineering, inference, and vector data management.
- Vector data management involves tokenizing domain data, generating numerical vectors, and storing them in a vector database for fast semantic search and retrieval.
- User personalization and context are important for Gen applications, requiring access to customer 360 data and real-time user information.
- Comprehensive data governance becomes crucial for Gen applications, including data sharing, privacy, quality, and cataloging.
Real-World Example: Amazon Finance
- Amazon Finance Operations is responsible for vendor payments, customer payments, and financial transactions at a massive scale.
- To address data silos and enable a single source of truth, Amazon Finance implemented a data mesh strategy on AWS.
- The data mesh approach decentralizes data management, with data producers responsible for data quality and data consumers able to easily access and use the data.
- Amazon Finance leveraged AWS data integration capabilities like Redshift Data Share and AWS Lake Formation to enable secure data sharing without data duplication.
- With a strong data foundation in place, Amazon Finance was able to quickly enhance their data mesh with generative AI features, such as:
- Using vector embeddings and large language models to understand business context from policy documents.
- Combining the business context with financial data to provide analysts with targeted problem-solving recommendations.
- Deploying a Gen chatbot to improve the productivity of analysts by over 80% in responding to customer queries.
The Future of AWS Data Foundations
- AWS is evolving its data foundation capabilities to provide a more unified experience, including:
- Sagemaker Unified Studio: A single data and AI development environment for building applications, including Gen.
- Sagemaker Data and AI Governance: Capabilities for managing data assets, models, and Gen applications with fine-grained access controls.
- Sagemaker Lakehouse: A unified data management layer that brings together the strengths of data warehouses and data lakes, accessible through open APIs.
- These new capabilities aim to help customers collaborate and build faster, with a comprehensive data and AI development platform on AWS.
