The Data Warehouse Lifecycle Toolkit

Lifecycle Vocabulary Primer 9

let the volume of material overwhelm you. Not every detail of every Lifecycle

task will be performed on every project.

Finally, as we’ll further describe in Chapter 2, the Kimball Lifecycle is most

effective when used to implement projects of manageable, yet meaningful

scope. It is nearly impossible to tackle everything at once, so don’t let your

business users, fellow team members, or management force that approach.

Lifecycle Navigation Aids

Not surprisingly, the book is riddled with references to the Kimball Lifecycle.

For starters, each chapter title page includes a miniature graphic of the

Lifecycle diagram, highlighting where you are within the overall framework.

You should view this as your Lifecycle mile marker. Be forewarned that

there is not always a one-to-one relationship between mile markers and

book chapters. In some cases, a single chapter addresses multiple markers,

as in Chapter 2, which covers both program/project planning and manage-

ment. In other cases, multiple chapters cover a single mile marker, such as

Chapters 6 and 7, which discuss dimensional modeling, or Chapters 9 and 10,

which provide detailed coverage of ETL design and development.

In addition to the ‘‘you are here’’ mile markers, there’s a ‘‘blueprint for

action’’ at the end of each process-oriented chapter that includes the following

guidance and recommendations:

Managing the effort and reducing risk.

Assuring quality.

Key project team roles involved in the process.

Key deliverables.

Estimating guidelines.

Templates and other resources available on the companion book website

at

www.kimballgroup.com.

Detailed listing of project tasks.

Lifecycle Vocabulary Primer

You are inevitably anxious to jump into the details and move ahead with your

DW/BI program/project, but we first want to define several terms that are

used throughout this book. We’ll also note core vocabulary changes since the

first edition of this publication.

Unfortunately, the DW/BI industry is plagued with terminology that’s used

imprecisely or in contradictory ways. Some of the long-standing debates in

10 Chapter 1 ■ Introducing the Kimball Lifecycle

our industry are fueled as much from misunderstandings about what others

mean by a term, as from true differences in philosophy. Though we can’t settle

the debates in this forum, we will try to be clear and consistent throughout

this text.

Data Warehouse versus Business Intelligence

As an industry, we can’t seem to reach consensus about what to call

ourselves. Traditionally, the Kimball Group has referred to the overall pro-

cess of providing information to support business decision making as data

warehousing. Delivering the entire end-to-end solution, from the source extracts

to the queries and applications that the business users interact with, has always

been one of our fundamental principles; we would never consider building

data warehouse databases without delivering the presentation and access

capabilities. This terminology is strongly tied to our legacy of books, articles,

and design tips. In fact, nearly all our Toolkit books include references to the

data warehouse in their titles.

The term business intelligence initially emerged in the 1990s to refer to the

reporting and analysis of data stored in the warehouse. When it first appeared

on the industry’s radar, several of this book’s authors were dumbfounded

about the hoopla it was generating because we’d been advocating the practices

for years. It wasn’t until we dug a little deeper that we discovered many

organizations had built data warehouses as if they were archival librarians,

without any regard to getting the data out and delivered to the business

users in a useful manner. No wonder earlier data warehouses had failed

and people were excited about BI as a vehicle to deliver on the promise of

business value!

Some folks in our industry continue to refer to data warehousing as the

overall umbrella term, with the data warehouse databases and BI layers as

subset deliverables within that context. Alternatively, others refer to business

intelligence as the overarching term, with the data warehouse relegated to

describe the central data store foundation of the overall business intelligence

environment.

Because the industry has not reached agreement, we consistently use the

phrase ‘‘data warehouse/business intelligence’’ (DW/BI) to mean the com-

plete end-to-end system. Though some would argue that you can theoretically

deliver BI without a data warehouse, and vice versa, that is ill-advised from

our perspective. Linking the two together in the DW/BI acronym further

reinforces their dependency.

Independently, we refer to the queryable data in your DW/BI system as the

enterprise data warehouse, and value-add analytics as BI applications.Inother

words, the data warehouse is the foundation for business intelligence.Wedisagree