3.6 - Conceptual Data Modeling

Question 1

Why is data modeling important in analytics?

Answer 1

It helps organize data logically to reveal relationships and support accurate predictions.

Question 2

What is a conceptual data model?

Answer 2

A high-level representation of data, showing entities, relationships, and important variables without technical details.

Question 3

Name two benefits of data modeling.

Answer 3

Better understanding of data structure, improved prediction accuracy, easier communication with stakeholders

Question 4

In a model, real-world objects or concepts are represented as ______.

Answer 4

Entities / objects

Question 5

Relationships between entities are represented as ______.

Answer 5

Connections / links / associations

Question 6

What is a feature (or variable) in predictive modeling?

Answer 6

A measurable attribute or property that can influence outcomes.

Question 7

Which of these is part of conceptual data modeling?
A) Defining entities
B) Mapping relationships
C) Selecting predictors
D) All of the above

Answer 7

D) All of the above

Question 8

Give one example of a conceptual model in business data.

Answer 8

Example: Customers → Orders → Products; or Patients → Treatments → Outcomes.

Question 9

Focusing only on relevant entities and variables for prediction is called ______.

Answer 9

Feature selection / variable selection

Question 9

Why should models be conceptual before technical implementation?

Answer 9

It clarifies structure, relationships, and purpose, reducing errors when building technical models.

Question 10

How does conceptual modeling support predictive thinking?

Answer 10

It helps identify which variables affect outcomes and how they relate, guiding model design.

Question 11

Name one tool or method to represent conceptual data models.

Answer 11

Examples: diagrams, entity-relationship (ER) diagrams, flowcharts, UML models.