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Introduction

As cloud storage becomes the de facto data lake, data warehouses continue to be the linchpins for making enterprises data-driven. As enterprises investigate AI, data integration and data quality remain perennial challenges.

Highlights

  • This research note outlines the issues that veteran data warehouse customers face as they look at extending coverage to data lakes and incorporating AI.

Features and Benefits

  • Discusses how managed cloud data warehousing services could help enterprises address data sprawl.
  • Discusses how enterprises can plan for growth in their analytics infrastructure to avoid becoming victims of their own success.
  • Evaluates how changes in the business will impact the choice of the underlying technology stack and, with it, the need for database and analytics skills, plus the associated data warehousing and BI software stack.

Key questions answered

  • What are the key steps in the learning curve for adopting or extending enterprise analytics?
  • How will changes in the business impact choices for the underlying data warehousing and BI software stack and the analytic skills associated with it?

Table of contents

Ovum view

  • Summary
  • Data continues to sprawl
  • Yes, AI fever is spreading, but not everybody is aware of it yet
  • Avoiding becoming a victim of your own success

Appendix

  • Further reading
  • Author