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Last updated on 2024年9月16日

What are the tools and techniques for data modeling in the context of data minimization and data retention?

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Data modeling is the process of designing and documenting the structure, relationships, and constraints of data in a system. It helps you to understand, communicate, and optimize your data requirements and quality. However, data modeling also involves some trade-offs and challenges, especially when it comes to data minimization and data retention. In this article, you will learn what these concepts mean and how they affect your data modeling decisions. You will also discover some tools and techniques that can help you to balance data minimization and data retention in your data models.

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M.R.K. Krishna Rao

Professor in Artificial Intelligence and Machine Learning
Vivek Harikrishnan

Empowering organizations in eCommerce, Marketplace, and Omni-channel environments with AI-driven solutions to solve…

1 Data minimization and data retention

Data minimization is the principle of collecting and storing only the data that is necessary and relevant for a specific purpose, and deleting or anonymizing the rest. Data retention is the practice of keeping and maintaining data for a certain period of time, depending on legal, regulatory, or business requirements. Both data minimization and data retention aim to reduce the risks and costs of data breaches, compliance violations, storage inefficiencies, and data quality issues. However, they also pose some challenges for data modeling, such as how to define the scope, granularity, and lifespan of data, how to handle data updates and deletions, and how to ensure data consistency and integrity across different sources and systems.

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Vivek Harikrishnan

Empowering organizations in eCommerce, Marketplace, and Omni-channel environments with AI-driven solutions to solve complex challenges, enhance data-driven strategies, and lead impactful product management
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For data modeling with a focus on data minimization and retention, tools like ER/Studio, ERwin, and dbt (Data Build Tool) are effective. Techniques include designing your schema to store only essential data, using data partitioning, and implementing time-to-live (TTL) policies to automatically delete outdated data. Use anonymization or encryption for sensitive information and ensure compliance with regulations like GDPR by regularly auditing your data models. These approaches help balance data utility with privacy and compliance.

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Carlos Fernando Chicata

Algunas insignias de community Top Voice | Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
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In my opinion, the data minimization could affect how data model is designing based on the storage space optimization and performance aspect, while data retention could improve the storage space optimization and performance aspect on the data storage implement the model.

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Dylan Pulver

Senior Software Developer | Specialist in Full-Cycle Development & Advanced Data Systems | Proven Success in System Architecture & Operational Efficiency | Exploring Opportunities in Tech Leadership & Startups
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1. Use Aggregated Data: In data modeling, focus on aggregating data to the necessary level of detail, minimizing the scope and granularity to what’s essential for your business needs. 2. Implement Data Lifecycle Management: Design models that incorporate data lifecycle rules for retention and deletion, ensuring compliance with data minimization and retention policies. 3. Ensure Consistency with Audit Trails: Maintain audit trails and versioning to track data changes and deletions, preserving consistency and integrity across systems.

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2 Conceptual, logical, and physical data models

One way to address the challenges of data minimization and data retention is to use different levels of abstraction in your data models. Conceptual, logical, and physical data models are three common types of data models that represent data at different degrees of detail and implementation. Conceptual data models show the high-level entities, attributes, and relationships of data in a domain, without specifying technical details or constraints. Logical data models add more details and rules to the conceptual data models, such as data types, keys, cardinalities, and normalizations. Physical data models translate the logical data models into specific schemas, tables, columns, indexes, and constraints for a particular database or platform. By using these different levels of data models, you can separate the business logic from the technical implementation, and apply data minimization and data retention principles at each stage.

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M.R.K. Krishna Rao

Professor in Artificial Intelligence and Machine Learning
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Conceptual Modelling: Establish a high-level representation of the business domain, identifying key entities and their relationships. Logical Modeling: Refine the conceptual model, introducing more technical details such as data types, attributes, and constraints. Physical Modeling: Define the implementation details, including specific database structures, indexes, and storage mechanisms. Alignment with Data Governance: Ensure that the data models adhere to organizational data minimization and retention policies. Leverage Modelling Tools: Utilize specialized software to streamline the modelling process and enhance collaboration. Regularly review and update the data models to reflect evolving business requirements and regulatory changes.

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3 Data modeling tools

Another way to facilitate data minimization and data retention is to use data modeling tools that can help you to design, document, validate, and generate your data models. Data modeling tools are software applications that provide graphical interfaces, templates, libraries, and wizards to create and manage data models. Some examples of data modeling tools are ER/Studio Data Architect, Oracle SQL Developer Data Modeler, IBM InfoSphere Data Architect, and Microsoft Visio. Data modeling tools can help you to apply data minimization and data retention principles by allowing you to define the scope and purpose of your data models, specify the data elements and relationships, enforce data quality and integrity rules, and generate code and scripts for database creation and maintenance.

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M.R.K. Krishna Rao

Professor in Artificial Intelligence and Machine Learning
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Data modeling tools are indispensable for the effective design and management of databases. These software applications provide intuitive graphical interfaces, pre-built templates, and extensive libraries to facilitate the creation and visualization of data structures. They enable organizations to comprehensively understand the relationships between entities, attributes, and data flows, ensuring data consistency, integrity, and adherence to data minimization and retention policies. Popular tools include ER/Studio Data Architect, Oracle SQL Developer Data Modeler, IBM InfoSphere Data Architect, and Microsoft Visio.

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4 Data modeling techniques

A third way to support data minimization and data retention is to use data modeling techniques that can help you to optimize the structure and performance of your data models. Data modeling techniques are methods or best practices that can help you to solve common or complex data modeling problems. Some examples of data modeling techniques are entity-relationship modeling, dimensional modeling, data vault modeling, and anchor modeling. Data modeling techniques can help you to apply data minimization and data retention principles by allowing you to choose the most appropriate and efficient way to represent and organize your data, depending on your business needs, data sources, and data analysis goals.

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Carlos Fernando Chicata

Algunas insignias de community Top Voice | Ingeniero de datos | AWS User Group Perú - Arequipa | AWS x3
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Like an alternative point; I think the narrow tables approach is a create option to manage the data based on the high flexibility to adapt any field based on storage space optimization; but the performance on data storage you must check. The Anchor and Hyper-agility modeling approach can give a good performance, flexibility and storage space optimization trade-off.

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5 Data lineage and metadata

A fourth way to enhance data minimization and data retention is to use data lineage and metadata to track and document the origin, transformation, and usage of your data. Data lineage is the information that shows how data flows from source to destination, through various processes, systems, and transformations. Metadata is the information that describes the characteristics, properties, and context of data. Both data lineage and metadata can help you to apply data minimization and data retention principles by allowing you to understand the provenance, quality, and value of your data, identify the dependencies and impacts of data changes, and comply with the regulations and policies that govern your data.

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M.R.K. Krishna Rao

Professor in Artificial Intelligence and Machine Learning
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Data lineage and metadata help track your data's journey, making it easier to manage data retention and privacy. Document data sources: Know where your data comes from. Track data transformations: Record changes made to your data. Log data usage: Keep a record of who uses your data and for what. Categorize data with metadata: Classify data based on its sensitivity and retention needs. Use data lineage tools: Automate tracking and mapping. Keep metadata updated: Ensure it's accurate. Use metadata for compliance: Show compliance with data privacy rules. Use metadata for data governance: Make informed decisions about data. By using data lineage and metadata, you can better understand your data, manage retention, and ensure privacy.

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6 Data lifecycle management

A fifth way to implement data minimization and data retention is to use data lifecycle management to plan and execute the processes and activities that involve your data throughout its existence. Data lifecycle management is the framework that defines how data is created, collected, stored, accessed, used, shared, archived, and deleted. It covers the technical, operational, and governance aspects of managing data. Data lifecycle management can help you to apply data minimization and data retention principles by allowing you to establish the roles and responsibilities of data stakeholders, set the standards and procedures for data quality and security, monitor and audit the performance and compliance of data processes, and optimize the resources and costs of data storage and maintenance.

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M.R.K. Krishna Rao

Professor in Artificial Intelligence and Machine Learning
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To ensure effective data minimization and retention, organizations should implement the following data lifecycle management best practices: Develop comprehensive retention policies. Classify data according to sensitivity. Maintain a detailed data inventory. Automate data deletion processes. Regularly review and update policies. Provide comprehensive employee training. Conduct regular audits and assessments. Consider anonymization or pseudonymization. By following these best practices, organizations can effectively manage their data lifecycle, minimize risks, and comply with data protection regulations.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the tools and techniques for data modeling in the context of data minimization and data retention?

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1 Data minimization and data retention

2 Conceptual, logical, and physical data models

3 Data modeling tools

4 Data modeling techniques

5 Data lineage and metadata

6 Data lifecycle management

7 Here’s what else to consider

Data Modeling

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