Powering Up Customer Experience with End-to-End Data Strategy

Why Create an End-to-End Data Strategy?

• Data strategy is a golden key for creating a centralized data strategy that integrates data sources for advanced analytics, machine learning, AI, and generative AI.
• Improper data can lead to wrong outputs in applications like chatbots.
• Data strategy provides quick insights from large data volumes to enable better, faster decision-making.
• Data strategy helps:
  • Provide a special, differentiated customer experience and improve member loyalty.
  • Stay ahead of industry trends and competition by enabling generative AI applications.
  • Reduce data operation costs.

Challenges in Implementing Data Strategy

• Handling large and growing data volumes (e.g., 100 million rows, 10TB).
• Sharing data across teams and organizations and ensuring data availability.
• Lacking technical skills to build machine learning and generative AI applications.
• Ensuring data security and compliance with requirements like HIPAA and PCI.
• Integrating diverse data (structured, semi-structured, unstructured).

Building a Modern Data Strategy

• Modern data strategy involves a plan of aligned actions with an agile approach, spanning mindset, people, processes, and technology.
• The three key factors are:
  1. Technology: Includes tools and services like generative AI and data warehouses.
  2. People and Processes: Aligning teams, skill sets, and tools to drive data-driven outcomes.
  3. Mindset: Creating a data-driven culture with beliefs, values, and behaviors.

Building a Modern Data Architecture

1. Data Discovery:

   • Define business value and goals.
   • Identify user personas and data usage patterns.
   • Identify data sources (databases, CSV files, applications, etc.).
   • Determine data storage requirements.
   • Plan data processing and application use cases.

2. High-Level Data Architecture:

   • Data Ingestion: Seamlessly migrate data to the cloud (e.g., AWS Data Migration Service).
   • Data Storage: Use services like Amazon S3 for raw, landing, and curated data zones.
   • Data Cataloging: Understand data representations.
   • Data Processing: Clean, enrich, and move data to the consumption zone.
   • Data Consumption: Explore, analyze, and visualize data (e.g., Amazon Athena, Amazon QuickSight).
   • Segmentation: Unify and enhance customer data for personalization (e.g., Amazon Personalize).
   • Security and Governance: Ensure data protection, encryption, and compliance.

Next Steps

• Scan the QR code to learn more about building a modern data strategy with AWS.
• Contact AWS to get information about the Data Strategy Diagnostic workshop.