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How do you manage data lifecycle and retention policies in data science?

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Data is the lifeblood of data science, but it also comes with challenges and responsibilities. How do you ensure that your data is accurate, secure, accessible, and compliant with regulations and ethics? How do you decide when to delete, archive, or retain your data? In this article, we will explore some best practices for managing data lifecycle and retention policies in data science.

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1 Data lifecycle stages

Data lifecycle is the process of collecting, processing, analyzing, storing, and disposing of data. Each stage has its own requirements and risks, and you need to plan and document your data management strategies accordingly. For example, you need to define your data sources, quality criteria, formats, and transformations in the data collection and processing stages. You need to apply appropriate methods, tools, and techniques in the data analysis stage. You need to secure, backup, and encrypt your data in the data storage stage. And you need to follow legal, ethical, and contractual obligations in the data disposal stage.

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Blair Sammons

Data Solutions Architect | Recovering IT Guy - Former IT Director, Engineering Lead, Data Manager, SysAdmin, Consultant, and Entrepreneur
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In AWS and GCP, achieving efficient data lifecycle management involves distinct steps for each stage. - In the data collection and processing phases, AWS Glue and GCP Dataflow provide tools for extraction, transformation, and loading (ETL) tasks. AWS offers services like S3, RDS, and Redshift for storage, while GCP offers options like Cloud Storage and BigQuery. - For data analysis, AWS provides Quicksight, and GCP offers Looker for analytics. Both platforms offer encryption, access controls, and compliance tools for data security. - Data disposal adheres to AWS and GCP's data retention policies, ensuring legal and ethical obligations. Proper documentation and planning within each stage are crucial!

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Leon Gordon

CEO of Onyx Data | Forbes Tech Council | Microsoft MVP | International Keynote Speaker | Gartner Ambassador
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In managing data lifecycle and retention policies in data science, it is important to follow the stages of the data lifecycle. This involves identifying and classifying data, determining its value and purpose, storing and securing it appropriately, analysing and leveraging it effectively, and finally, implementing retention policies based on legal, regulatory, and business requirements. By following these stages, data scientists can ensure data integrity, compliance, and optimal utilisation throughout its lifecycle.

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2 Data retention policies

Data retention policies are the rules and guidelines that determine how long and where you keep your data. They are based on various factors, such as the purpose, value, sensitivity, and relevance of your data, as well as the regulatory and contractual obligations that apply to your data. For example, you may need to retain some data for a certain period of time to comply with laws, audits, or grants. You may also need to delete some data after a certain period of time to protect privacy, security, or intellectual property rights. You may also want to archive some data for future use or reference.

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Blair Sammons

Data Solutions Architect | Recovering IT Guy - Former IT Director, Engineering Lead, Data Manager, SysAdmin, Consultant, and Entrepreneur
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In AWS and GCP, implementing effective data retention policies involves several steps. Both platforms offer tools and services to align with your policies. - In AWS, you can use Amazon S3 for data storage and apply lifecycle policies to automatically transition, archive, or delete data based on defined rules. - GCP provides Cloud Storage, with Object Lifecycle Management to control data actions. BigQuery's data partitioning assists in data organization as well. Both platforms offer encryption and access controls for compliance; while regular audits and adjustments ensure policies stay current and aligned with regulatory requirements and data value.

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3 Data retention challenges

Implementing and enforcing data retention policies in data science can be difficult. You must consider the volume and complexity of data, as well as the ownership and access rights of multiple stakeholders. Additionally, you must be prepared for frequent changes and updates to the data, ensuring that it is consistent, accurate, and traceable. Balancing the costs and benefits of keeping or deleting your data is essential to successful data retention policy enforcement.

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Blair Sammons

Data Solutions Architect | Recovering IT Guy - Former IT Director, Engineering Lead, Data Manager, SysAdmin, Consultant, and Entrepreneur
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In AWS and GCP, addressing data retention challenges requires a strategic approach. AWS offers services like Glue and EMR for data processing, ensuring data consistency. With S3, you can assign access controls and versioning to manage ownership and rights. Retention policies in AWS can be achieved through tools like Data Lifecycle Manager and CloudWatch for monitoring. GCP provides Dataprep and Dataflow for data processing, ensuring accuracy. Cloud Storage allows access controls and versioning to manage stakeholders. GCP's Data Loss Prevention API helps ensure data traceability. Either way, regular monitoring of policy effectiveness and collaboration with legal and security teams help balance costs and benefits.

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4 Data retention solutions

To address these challenges, you need to adopt solutions such as creating and maintaining a data inventory, establishing and following a data governance framework, and implementing and automating data archiving and deletion processes. This will help you track, manage, ensure compliance with laws and regulations, optimize storage, security, and performance.

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Blair Sammons

Data Solutions Architect | Recovering IT Guy - Former IT Director, Engineering Lead, Data Manager, SysAdmin, Consultant, and Entrepreneur
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In AWS and GCP, countering data retention challenges involves practical measures. - Develop a data inventory using tools like AWS Glue and GCP Data Catalog to categorize and track data. - Formulate a data governance framework using IAM to define ownership and access. - AWS S3 and GCP Cloud Storage enable versioning and tagging. - Automate data retention with AWS Data Lifecycle Manager and GCP's Object Lifecycle Management. - Leverage compliance tools like AWS Config and GCP Security Command Center for regulatory adherence. There's so many more tools and systems (wether open sourced or cloud native) that can also help here as well!

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5 Data retention best practices

To improve your data retention practices, you should review and update your policies regularly to reflect changes in your data needs, goals, and environment. Make sure to document and communicate these policies clearly and transparently to stakeholders and collaborators. Additionally, use reliable and secure data storage and backup systems that support your data retention policies. Furthermore, monitor and audit the activities and outcomes of your data retention to ensure compliance and quality. Data retention is an essential part of data management in data science, so by following these best practices, you can manage your data lifecycle and retention policies effectively and responsibly.

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Blair Sammons

Data Solutions Architect | Recovering IT Guy - Former IT Director, Engineering Lead, Data Manager, SysAdmin, Consultant, and Entrepreneur
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In AWS and GCP, elevating data retention involves key strategies. - Regularly update policies using AWS CloudFormation and GCP Deployment Manager to adapt to evolving needs. - Document with AWS Wiki and GCP Docs, at the very minimum. - Employ AWS S3 and GCP Cloud Storage with versioning for reliable storage. - Back up with Amazon Glacier and GCP Coldline Storage for long-term retention. - Monitor via AWS CloudWatch and GCP Monitoring, auditing with AWS CloudTrail and GCP Audit Logging. - AWS Config and GCP Security Command Center ensure compliance. By adhering to best practices, effective and responsible data retention should be documented and maintained regularly.

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6 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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How do you manage data lifecycle and retention policies in data science?

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1 Data lifecycle stages

2 Data retention policies

3 Data retention challenges

4 Data retention solutions

5 Data retention best practices

6 Here’s what else to consider

Data Management

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How do you manage data lifecycle and retention policies in data science?

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6

1 Data lifecycle stages

2 Data retention policies

3 Data retention challenges

4 Data retention solutions

5 Data retention best practices

6 Here’s what else to consider

Data Management

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