Different Data Sets Within a PLM System

Data is the raw material that needs to be processed in order to produce the final output of PLM analytics. Hence, before heading for analytics, taking a look at the different orientations of PLM data may help conceptualize what PLM analytics can provide (see table 1).

Orientation	Description	Example
Product	This is the most prominent group of data within the PLM system. Product data (i.e., product definition information) is the backbone of the entire PLM data set. Other data exists and is organized around product data.	Product requirements Product structure data Product document data Product document metadata
Project	Project-oriented data is used to define and help execute product development projects and processes. This group of data exists for the purposes of facilitating the creation of product definition information, but it is not categorized as product data.	Work breakdown structure (WBS) Resource information Work progression data Project risk data
Process	This group of data refers to PLM users' specific business processes. In general, there are overlaps between this group and the previous group (project data). Process data refers to the daily operational activities that are not managed as projects.	Routing and approval activities Problem-solving activities Collaboration records Transactional data associated with business processes
People	User information (with regard to PLM systems) may be associated with all the previous categories. However, it is necessary to treat the user-oriented data as the fourth data set since the "people" element is an important part of a PLM system.	System user information Roles and groups User login data User participation records

Table 1. Four orientations of PLM data

The above table separates PLM data into four groups: product, project, process, and people. This is not a scientific way of categorizing PLM data since there may be overlaps, dependences, and consequences between one group and another. However, these four sets of data represent four different facets when we look at the entire collection of PLM data, and each of these facets explores an area of PLM analytics.