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Data Contextualization

Data only becomes valuable when it carries business semantics and context—this is what enables business users or data analysts to derive meaningful insights. During the modeling process, IDMP provides contextualization in the following ways:

  1. Every element or attribute can be configured with a description.
  2. Elements can have various static attributes, such as model number, serial number, etc.
  3. Both elements and attributes can be assigned categories, making them easier to search and tag with business labels.
  4. Elements and attributes can be configured with custom properties to add personalized context.
  5. Each element can include location information.
  6. Attributes can be configured with storage units, display units, limit values, target values, and more.

IDMP provides element and attribute templates to help standardize these configurations. However, IDMP cannot enforce semantic and contextual definitions—it relies on the organization’s own management processes. The entire modeling process is essentially a process of contextualizing data. Only when metadata is rich and accurate can valuable analysis be performed—and only then can AI truly perceive the scenario and automatically generate analytics and dashboards.