Data Warehousing in the Age of Artificial Intelligence
by Gary Orenstein, Conor Doherty, Mike Boyarski, Eric Boutin
Chapter 1. The Role of a Modern Data Warehouse in the Age of AI
Actors: Run Business, Collect Data
Applications might rule the world, but data gives them life. Nearly 7,000 new mobile applications are created every day, helping drive the world’s data growth and thirst for more efficient analysis techniques like machine learning (ML) and artificial intelligence (AI). According to IDC,1 AI spending will grow 55% over the next three years, reaching $47 billion by 2020.
Applications Producing Data
Application data is shaped by the interactions of users or actors, leaving fingerprints of insights that can be used to measure processes, identify new opportunities, or guide future decisions. Over time, each event, transaction, and log is collected into a corpus of data that represents the identity of the organization. The corpus is an organizational guide for operating procedures, and serves as the source for identifying optimizations or opportunities, resulting in saving money, making money, or managing risk.
Enterprise Applications
Most enterprise applications collect data in a structured format, embodied by the design of the application database schema. The schema is designed to efficiently deliver scalable, predictable transaction-processing performance. The transactional schema in a legacy database often limits the sophistication and performance of analytic queries. Actors have access to embedded views or reports of data within the application to support recurring or operational ...
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