DataModel Definition

In Genera Science, the study of Genericism, is an information science. From the past-forward, historical reference, the initial use of Genera Science is the recognition of Generic System. This use takes into account the initial presence of Specific Systems, and applies standardized strategies which yield a Generic System.

For this practice, there is need to borrow terms from historical data sciences, yet there is also need to recapture definitions for these terms to suit Genera Science. There is need to define, for utilization, several concepts which form the foundation for the information science. Here is introduced the Genera Definition of DataModel.

DataModel

A datamodel is a list of [key and datatype and understanding] where key is a targetable field which yields a value, datatype is one of a set of system defined formats with known parsing/rendering mechanisms, and understanding is a human langauage description for understanding purposes.

Title: “Understanding Data Models: Keys, Data Types, and Context”

Content:

1. Introduction to Data Models:
   A data model serves as a blueprint for how data is structured and utilized within a system. It comprises three essential components: key, datatype, and understanding.
2. Key:
   • Definition: The key represents a targetable field within the data model. It serves as an identifier that can be used to retrieve data.
   • Examples: User ID, Product SKU, Transaction Date.
   • Purpose: Keys enable efficient data access and organization, allowing for easy querying and reporting.
3. Datatype:
   • Definition: The datatype specifies the format of the data associated with the key. It defines the system’s rules for parsing and rendering the data.
   • Common Data Types:
     • String: Text data (e.g., names, descriptions)
     • Integer: Whole numbers (e.g., counts, IDs)
     • Date/Time: Temporal data (e.g., timestamps, deadlines)
     • Boolean: True/False values (e.g., active/inactive status)
   • Importance: Correctly defining data types ensures data integrity, facilitates validation, and optimizes storage.
4. Understanding:
   • Definition: This component provides a human-readable description of the key and its associated datatype, enhancing comprehension and usability for developers and stakeholders.
   • Examples:
     • Key: “User ID”
       Datatype: Integer
       Understanding: “A unique identifier assigned to each user in the system.”
     • Key: “Transaction Date”
       Datatype: Time
       Understanding: “The time when the transaction occurred.”
   • Role: Understanding fosters better communication among team members, ensuring that everyone has a clear grasp of what each key represents.
5. Conclusion:
   By clearly defining keys, data types, and their corresponding understandings, data models become powerful tools for organizing, managing, and interpreting data effectively. This structured approach not only aids in system design but also enhances collaboration and data-driven decision-making.