Why is it beneficial to split people and positions data?

Let’s start with the relationship between people and positions in the organization. Positions are planned according to business needs, which create the demand for people. People are appointed to positions and provide the supply of positions. Ideally, there’s a one-to-one relationship between the position and the individual employee appointed to that position. But the reality is more complicated.  

Thinking about people, individual employees are constantly joining, moving through and leaving the organization. In addition, there might be situations where: 

• a single employee performing two positions on a temporary or permanent basis (‘double hatting’) 
• two part-time employees sharing the same position 
• employees taking paid or unpaid leave and not holding any position 
• constant adjustment of employees: changing performance scores, changing rewards, changing department managers 

Considering positions, there are cases where: 

• new positions are planned which may or may not be filled within the planned timeframe 
• positions may become temporarily or permanently vacant 
• due to changes in business needs, positions may be closed (or ‘demised’) 
• the location in the organization may change with changed reporting lines and the physical location may change as geographic footprints are adjusted 

In order to manage the complexity of constantly changing organization data, it’s better to split people and positions data.

See also: What is the difference between People and Positions Datasets?