My first Publication Agile-Data-Warehouse-Design-eBook | Page 48

2
M ODELING B USINESS E VENTS
Think like a wise man but communicate in the language of the people.
— William Butler Yeats (1865–1939)
Business events are the individual actions performed by people or organizations
during the execution of business processes. When customers buy products or use
services, brokers trade stocks, and suppliers deliver components, they leave behind
a trail of business events within the operational databases of the organizations
involved. These business events contain the atomic-level measurable details of the
business processes that DW/BI systems are built to evaluate. Business events are
BEAM ✲ uses business events as incremental units of data discovery/data model-
ing. By prompting business stakeholders to tell their event data stories, BEAM ✲
modelers rapidly gather the clear and concise BI data requirements they need to
produce efficient dimensional designs. BEAM✲ modelers
In this chapter we begin to describe the BEAM ✲ collaborative approach to dimen-
sional modeling, and provide a step-by-step guide to discovering a business event
and documenting its data stories in a BEAM ✲ table: a simple tabular format that is
easily translated into a star schema. By following each step you will learn how to
use the 7Ws (who, what, when, where, how many, why, and how) to get stake-
holders thinking dimensionally about their business processes, and describing the
information that will become the dimensions and facts of their data warehouse —
one that they themselves helped to design! This chapter is a
Data stories and story types: discrete, recurring and evolving
Discovering business events: asking “Who does what?”
the measureable
atomic details of
business processes
discover BI data
requirements by
telling data stories
step-by-step guide
to using BEAM✲
tables and the 7Ws
to describe event
details
Chapter 2 Topics
At a Glance
Documenting events: using BEAM✲ Tables
Describing event details: using the 7Ws and stories themes
Modelstorming with whiteboards: practical collaborative data modeling
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