Valuable insights tend to be more complex. This typically means a dataset with many columns, also called features or attributes. The more columns in a dataset, the more likely you are to find hidden insights that provide unknown information that can be applied to deliver tangible value to a business. Conversely, the less complex the insight, the more likely the business is already aware.

If the data is structured correctly, the links between various data can be identified and insights can be captured. Think of this data as being in a cube with multiple panes. Each pane is a dimension, and the data is organised in a way that allows patterns etc. to be identified. 

The Entopy platform uses ontology to develop real-time digital twins that reflect the real world, from which insights can be captured through the depiction of entities and the dynamic relationships between them within given contexts: entities (and their many attributes) have dynamic relationships with other entities (and there many attributes) and these relationships change over time. This means Entopy can capture insights that comprise many dimensions (relationships between entities over time, attributes of respective entities with relevance over time, situations of respective entities over time etc.) which uncovers highly valuable insights to be captured.