This chapter, which focuses on human resources (HR) data, is the last in the series that deals with cross-industry business applications. Similar to the accounting and finance data described in Chapter 7, HR information is disseminated broadly throughout the organization. Unlike finance, however, we typically don't find a cadre of tech-savvy HR analysts in many organizations.
Most of us operate in a rapidly changing, competitive business environment. We need to better understand our employees' demographics, skills, earnings, and performance in order to maximize their impact. In this chapter we'll explore several dimensional modeling techniques in the context of HR data.
Chapter 8 discusses the following concepts:
Dimension tables to track employee transaction facts
Skill-set keyword dimension outrigger
Handling of survey questionnaire data
Thus far the dimensional models we have designed closely resemble each other in that the fact tables have contained key performance metrics that typically can be added across all the dimensions. It is easy for dimensional modelers to get lulled into a kind of additive complacency. In most cases, this is exactly how it is supposed to work. However, with HR employee data, many of the facts aren't additive. Most of the facts aren't even numbers, yet they are changing all the time.
To frame the problem with a business vignette, let's assume that we work ...