1. Introduction
This policy document outlines the standards and practices for implementing and maintaining data dictionaries and data models within Igile Technologies India Pvt Ltd. The objective is to ensure that all data management activities are conducted with consistency, accuracy, and efficiency, supporting the effective use and governance of data across all projects.
2. Purpose
The purpose of this policy is to define the framework for:
- Developing and maintaining data dictionaries and data models.
- Ensuring consistency and accuracy in data definitions and usage.
- Supporting the effective implementation of data management practices.
3. Scope
This policy applies to all employees and contractors involved in data management, including those working on software development projects, database management, and data analysis.
4. Data Architecture
4.1. Overview
Data Architecture encompasses the design, organization, and management of data structures and systems. It includes data models, data dictionaries, and data management practices.
4.2. Components
- Data Models: Visual representations of data structures, relationships, and data flow within systems.
- Data Dictionary: A repository of metadata that defines data elements, attributes, relationships, and rules.
4.3. Implementation Process
- Requirement Gathering: Identify data requirements from stakeholders and document business processes.
- Data Modeling: Design conceptual, logical, and physical data models based on the gathered requirements.
- Data Dictionary Creation: Develop a data dictionary to document data definitions, data types, and data relationships.
- Validation and Review: Validate data models and dictionaries with stakeholders and review for consistency and accuracy.
- Deployment: Implement data models and dictionaries in the project environment.
- Maintenance: Regularly update data models and dictionaries to reflect changes in data requirements and business processes.
5. Data Dictionary
5.1. Definition
A data dictionary is a centralized repository that describes the structure, constraints, and relationships of data elements within the organization.
5.2. Components
- Data Element Definitions: Descriptions of individual data elements, including name, type, and purpose.
- Data Relationships: Descriptions of how data elements relate to each other.
- Data Constraints: Rules and constraints governing the data, such as data types, formats, and allowable values.
- Metadata: Information about data sources, usage, and ownership.
5.3. Creation and Management
- Standardization: Use consistent naming conventions and definitions across all projects.
- Documentation: Maintain comprehensive documentation of all data elements and relationships.
- Access Control: Implement access controls to ensure data dictionary integrity and confidentiality.
- Updates: Regularly review and update the data dictionary to reflect changes in data requirements and definitions.
6. Data Model
6.1. Definition
A data model is a visual representation of data structures, including entities, attributes, and relationships within a system.
6.2. Types
- Conceptual Data Model: High-level representation of data entities and their relationships, independent of technology.
- Logical Data Model: Detailed representation of data entities, attributes, and relationships, focusing on the organization of data.
- Physical Data Model: Implementation-specific representation of data structures, including tables, columns, and indexes.
6.3. Creation and Management
- Design: Develop data models using industry-standard methodologies and tools.
- Validation: Ensure data models are validated against business requirements and technical constraints.
- Documentation: Document data models, including diagrams and descriptions of data entities and relationships.
- Version Control: Maintain version control for data models to track changes and updates.
6.4. Data Model Diagram
Below is a simplified example of a data model diagram used in our SaaS platforms:
7. Compliance and Audits
- Compliance: Ensure adherence to industry standards and regulatory requirements related to data management and security.
- Audits: Conduct regular audits of data dictionaries and data models to ensure accuracy, consistency, and compliance with the policy.
8. Roles and Responsibilities
- Data Architects: Design and maintain data models and data architecture.
- Data Stewards: Manage and oversee the data dictionary, ensuring accuracy and completeness.
- Project Managers: Ensure data management practices are followed in project implementations.
- Developers: Implement data models and adhere to data dictionary definitions in application development.
9. Enforcement
Failure to comply with this policy may result in disciplinary action, including but not limited to, retraining, reassignment, or termination of employment.
10. Policy Review
This policy will be reviewed annually or as needed to ensure it remains current and relevant to the organization’s needs.