The Data Warehouse Lifecycle Toolkit

Introduction

Remarkable transformations have occurred in the nine years since the first

edition of The Data Warehouse Lifecycle Toolkit was published. The data ware-

house industry has reached full maturity and acceptance across the business

world. Hardware and software have made mind boggling advances in these

nine years. We have replaced ‘‘gigabytes’’ with ‘‘terabytes’’ in all our conver-

sations. Yet somehow the data warehousing task has remained fundamentally

unchanged.

Many of you have thousands of data warehouse users in your organiza-

tions. You have welcomed operational decision makers to the ranks of data

warehouse users to accompany the original cadres of marketing and finance

users. In fact, operational urgencies are the hottest aspects of data warehous-

ing, with everybody insisting that they need the data in ‘‘real time.’’ As our

data warehouses have become more important and more visible, we have

been hammered by privacy, security, and compliance requirements that are

non-negotiable. Business users are waking up to the value of high quality data

in much the same way that conventional manufacturing has embraced the

management of quality. Finally, and perhaps most important, we have a new

name for what we do that reflects our true purpose. It is business intelligence.

To emphasize that point, in most places in this book we refer to the overall

system you are building as the DW/BI system.

The shift to business intelligence puts initiative in the hands of business

users, not IT. But at the same time this shift puts into perfect focus the mission

of the data warehouse: It is the necessary platform for business intelligence.

The data warehouse does the hard work of wrangling the data out of the

source systems, cleaning it, and organizing it so that normal business users

can understand it. Of course we strive for world class business intelligence, but

world class business intelligence is only possible if you have a world class data

xxxi