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How do you evaluate and optimize data minimization and data retention for data reduction?

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Data minimization and data retention are two important principles for data reduction, which can help you improve your data modeling performance, security, and compliance. Data minimization means collecting and processing only the data that is necessary and relevant for your purpose, while data retention means keeping the data only for as long as it is needed and deleting it afterwards. In this article, you will learn how to evaluate and optimize these principles for your data modeling projects.

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1 Data minimization benefits and challenges

Data minimization can reduce the cost, complexity, and risk of your data modeling process, as well as enhance the quality and accuracy of your results. By limiting the amount and type of data you collect and process, you can avoid unnecessary overhead, storage, and processing time, as well as reduce the exposure of sensitive or personal data. However, data minimization also poses some challenges, such as determining the optimal level of granularity, detail, and scope of your data, balancing the trade-off between data quality and quantity, and ensuring that you do not lose or discard valuable or relevant data.

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Yuvaraj V

Assistant Manager - Global Talent Acquisition | MBA in HR
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To evaluate and optimize data minimization and data retention for data reduction, start by assessing the necessity and relevance of the data being collected, ensuring only essential information is retained. Implement data classification to identify critical versus redundant data. Establish clear data retention policies based on regulatory requirements and business needs, specifying how long data should be kept before being securely deleted. Use data compression techniques and efficient storage solutions to reduce the data footprint. Regularly review and update these policies and practices to adapt to changing requirements and technological advancements, ensuring optimal data management and storage efficiency.

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Carlos Fernando Chicata

Ingeniero de datos | AWS User Group PerÃº - Arequipa | AWS x3
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Data minimization is about how all business processes will work with minimum necessary data to get the expected results; so the quality and accuracy results from these processes depend how these processes are designed to support the organization. If the processess add or remove fields on existed entities or generate new entities; your data model must prepare to support these activities, so the capacity to extend the granularity of entities depend of the approach schema you implement ( star schema, data vault schema, 3NF schem, etc ): several benefits and challenges like data distribution, processing time or encryption data will affect on your data model.

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2 Data retention benefits and challenges

Data retention can also help you optimize your data modeling process, as well as comply with legal and ethical requirements. By keeping the data only for the duration that is necessary and appropriate for your purpose, you can free up space, resources, and bandwidth, as well as protect the privacy and security of your data subjects. However, data retention also involves some challenges, such as defining the criteria, frequency, and method of data deletion, maintaining the integrity and consistency of your data, and preserving the historical and contextual value of your data.

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Tashi Tamang

Data Analyst @ WALMART |SQL & PYTHON Specialist | Power BI, Tableau | ML, AWS, Azure||
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-Benefits: Enhances data security, reduces storage costs, and ensures regulatory compliance. -Challenges: Balancing retention policies with operational needs, ensuring data integrity, and managing the complexities of diverse data sources

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3 Data minimization and data retention evaluation

To evaluate your data minimization and data retention practices, you need to assess your data modeling goals, requirements, and constraints, as well as the characteristics and sources of your data. Some of the questions you can ask yourself are: What is the purpose and scope of your data modeling project? What are the legal, ethical, and contractual obligations and expectations regarding your data? How much and what kind of data do you need to achieve your desired outcomes? How reliable, accurate, and relevant is your data? How long do you need to keep your data and why? How do you ensure that your data is secure and compliant throughout its lifecycle?

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Carlos Fernando Chicata

Ingeniero de datos | AWS User Group PerÃº - Arequipa | AWS x3
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The data retention depend the data usage lifecycle associated with table groups or entities/concepts based on the reality, social and business context; while data minimization is about how the business processes will work to give satisfy theirs clients and depend of the technical and business context. Your evaluation in both team are differents and related: the data minimization will give the indication to define its data retention; so you must check the technical, social and legal reality to detect how improve your theirs fields to define and improve these policies and practices.

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4 Data minimization and data retention optimization

To optimize your data minimization and data retention practices, you need to implement strategies and techniques that can help you achieve the optimal balance between data quality and quantity, as well as data availability and security. Some of the strategies and techniques you can use are: Define clear and specific data collection and processing policies and procedures, and document them. Apply data validation, cleansing, and transformation techniques to ensure that your data is accurate, consistent, and relevant. Use data compression, encryption, and anonymization methods to reduce the size and sensitivity of your data. Establish data retention schedules, rules, and mechanisms to determine when and how to delete your data. Monitor and audit your data minimization and data retention activities and outcomes, and adjust them as needed.

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Tashi Tamang

Data Analyst @ WALMART |SQL & PYTHON Specialist | Power BI, Tableau | ML, AWS, Azure||
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Evaluate data minimization by removing redundant data, and optimize retention with clear policies and timelines, ensuring compliance. Use automated tools and regular audits to monitor practices, maximizing data value and minimizing risks.

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5 Data minimization and data retention tools

To support your data minimization and data retention efforts, you can use various tools and frameworks that can help you automate, simplify, and standardize your data modeling process. Some of the tools and frameworks you can use are: Data modeling tools that can help you design, create, and manage your data models, such as ERwin, PowerDesigner, or SQL Developer Data Modeler. Data quality tools that can help you assess, improve, and monitor your data quality, such as Talend Data Quality, Informatica Data Quality, or IBM InfoSphere QualityStage. Data protection tools that can help you secure, encrypt, and anonymize your data, such as AWS KMS, Azure Key Vault, or Google Cloud KMS. Data lifecycle management tools that can help you manage, archive, and delete your data, such as AWS S3 Lifecycle, Azure Blob Storage Lifecycle, or Google Cloud Storage Lifecycle.

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Tashi Tamang

Data Analyst @ WALMART |SQL & PYTHON Specialist | Power BI, Tableau | ML, AWS, Azure||
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Evaluating and optimizing data minimization and retention involves assessing the necessity of each data type, setting clear retention policies, and implementing automated tools to enforce these policies. Key strategies include periodic audits, leveraging data anonymization techniques, and using advanced data management platforms. This ensures compliance, enhances data security, and reduces storage costs.

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6 Data minimization and data retention best practices

To ensure that your data minimization and data retention practices are effective and efficient, you need to follow some best practices that can guide you throughout your data modeling process. Some of the best practices you can follow are: Align your data minimization and data retention practices with your business objectives, stakeholder expectations, and regulatory requirements. Communicate and collaborate with your data sources, users, and partners to ensure that you have a shared understanding and agreement on your data minimization and data retention policies and procedures. Review and update your data minimization and data retention practices regularly to reflect any changes in your data modeling goals, requirements, or constraints. Evaluate and measure the impact and value of your data minimization and data retention practices on your data modeling performance, security, and compliance.

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Sowmya Reddy Yella

Data Engineer & Analytics Specialist | MS in IT & Management | Python, SQL, Tableau, ETL, AWS, ML | Seeking Full-Time Opportunities
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In my opinion, the key to successful data minimization and retention is balancing regulatory compliance, business needs, and operational efficiency. Regularly assessing data procedures, integrating stakeholders, and using modern tools can significantly enhance data management. Furthermore, developing a culture of data knowledge and accountability within the firm is essential. Continuous learning and adaptation to new technologies and regulations will ensure that data strategies are strong and effective.

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Nicholas Whiting

Senior Fellow | CXO Advisor | Principal Consultant | Architect / Engineer | Flight Instructor - IT Governance, Digital Transformation, Data and ML Programs, Cybersecurity, Application Modernization, and Cloud Adoption
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Regularly conduct data audits to ensure compliance with your minimization and retention policies. These audits can reveal areas where data policies might be too strict or too lenient, allowing for continuous refinement. Involve legal and compliance teams in the audit process to ensure that your practices meet evolving regulatory requirements. Moreover, implement a feedback loop with end-users to understand how data retention policies affect their work and adjust as necessary to balance efficiency with usability.

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7 Hereâ€™s what else to consider

This is a space to share examples, stories, or insights that donâ€™t fit into any of the previous sections. What else would you like to add?

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Data Modeling

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How do you evaluate and optimize data minimization and data retention for data reduction?

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1 Data minimization benefits and challenges

2 Data retention benefits and challenges

3 Data minimization and data retention evaluation

4 Data minimization and data retention optimization

5 Data minimization and data retention tools

6 Data minimization and data retention best practices

7 Hereâ€™s what else to consider

Data Modeling

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How do you evaluate and optimize data minimization and data retention for data reduction?

1

2

3

4

5

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7

1 Data minimization benefits and challenges

2 Data retention benefits and challenges

3 Data minimization and data retention evaluation

4 Data minimization and data retention optimization

5 Data minimization and data retention tools

6 Data minimization and data retention best practices

7 Hereâ€™s what else to consider

Data Modeling

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