Smarter Data Science

CHAPTER 4A Look Back on Analytics: More Than One Hammer

“It does little good to forecast the future of … if the forecast springs from the premise that everything else will remain unchanged.”

—Alvin Toffler

The Third Wave

This chapter explores some of the pitfalls associated with traditional analytical environments and how modern data lakes attempt to remediate many of the gaps found in a data warehouse, especially in terms of handling unstructured data and providing support for artificial intelligence (AI). The lessons learned from the failures of previous analytics solutions reveal that further progress is necessary. Organizations need to develop a robust and modern information architecture. Newer solutions must address the needs of multiple personas along with data quality and data governance so that multiple forms of analytics, including AI, can be readily incorporated to yield a sustainable data and analytical environment.

Been Here Before: Reviewing the Enterprise Data Warehouse

Historically, organizations requiring analytics have turned to an enterprise data warehouse (EDW) for answers. The traditional EDW was used to store important business data and was designed to capture the essence of the business from other enterprise-based systems such as customer relationship management, inventory, and sales. These systems allowed analysts and business users to gain insight and to make important business decisions from the business's data.

For many years, techno-religious ...