Data Flow Design

Data Flow Design is a crucial aspect of data management that documents and visualizes how data moves through various business processes and systems. It provides insights into data relationships and interactions across an organization, enabling stakeholders to understand and improve data workflows.

Key Components of Data Flow Design

Data Lineage Documentation

• Definition: A type of documentation that tracks and visualizes the flow of data from its origin through various transformations to its final destination.
• Purpose: Helps in understanding data provenance, ensuring data integrity, and complying with regulations.

Data Flow Mapping

• Relationships:
  • Applications: Identifies how data flows between different applications within a business process.
  • Data Stores: Maps interactions between various databases and data storage solutions in the environment.
  • Network Segments: Useful for security mapping, indicating how data traverses different network segments and identifying potential vulnerabilities.
  • Business Roles: Illustrates the responsibilities of different roles in creating, updating, using, and deleting data (CRUD operations).
  • Local Differences: Identifies locations where variations in data handling or processing occur, allowing for tailored approaches to data management.

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Above and Beyond Data Modelling

Data modeling is foundational, but several additional concepts enhance data governance, quality, and management:

1. Entity Lifecycle State-Transition Diagrams

• Definition: Diagrams that represent the states an entity can occupy throughout its lifecycle and the trigger events that cause transitions between these states.
• Purpose: Helps to understand how entities evolve, ensuring that processes are in place to manage transitions effectively.

2. Valid Reference Values

• Description: Documentation of codes, names, and meanings used across the organization to ensure consistency and clarity in data usage.
• Purpose: Provides a standardized approach to data entry and reporting, reducing errors and discrepancies.

3. Business Glossary

• Definition: A comprehensive collection of terms used within the organization, including definitions beyond just entities.
• Purpose: Ensures a shared understanding of terminology, facilitating communication across departments and teams.

4. Enterprise Content Management Taxonomies

• Description: Standard topic hierarchies that categorize and organize content within the organization.
• Purpose: Enhances information retrieval and management, making it easier to locate and use data.

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Data Attribute Quality Rules and Requirements

Ensuring data quality is critical for effective data management. The following dimensions of data quality should be monitored:

• Integrity Rules:

  • Ensure data is accurate and consistent across systems.

• Format Requirements:

  • Define expected data formats (e.g., date formats, numerical precision).

• Data Cleansing Rules and Procedures:

  • Processes to identify and rectify errors or inconsistencies in data.

• Match / Merge Rules:

  • Criteria for identifying duplicate records and merging them appropriately.

• Accuracy / Precision Requirements:

  • Standards to ensure data reflects reality accurately and is precise to the required degree.

• Timeliness / Freshness Requirements:

  • Guidelines on how current data must be for it to be considered usable.

• Consistency Requirements:

  • Ensure data remains consistent across various systems and applications.

• Security / Privacy Protection Requirements:

  • Policies and procedures to protect sensitive data from unauthorized access.

• Security Classification:

  • Classification of data based on sensitivity (e.g., restricted, confidential, internal only, public).

• Retention and Archival Requirements:

  • Guidelines for how long data should be retained and the processes for archiving it.

• Regulatory Compliance Requirements:

  • Ensure data practices comply with relevant regulations and standards.

• Audit Requirements:

  • Establish processes for auditing data practices to ensure compliance and quality.

• Critical Dimensions of Data Quality:

  • Identify and focus on the most significant dimensions of data quality that impact business operations.

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The Zachman Framework

• Overview: The Zachman Framework identifies primitive model artifacts, primarily focusing on data-to-data relationships.
• Composite Models:
  • Data-to-Process: Understanding how data interacts with business processes.
  • Data-to-Organization: Recognizing how data structures align with organizational structures.
  • Data-to-Role: Defining relationships between data and business roles.
  • Data-to-Application: Mapping how data flows through various applications within the organization.

Significance

• Recognizing that relationships beyond just data-to-data are critical for holistic enterprise architecture and data management. This broader perspective ensures comprehensive understanding and governance of data across the organization.