Best practices for salesforce data lifecycle management

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salesforce data lifecycle management: a comprehensive guide with best practices and code examples

data lifecycle management (dlm) in salesforce is the process of managing your data from its creation to its eventual archiving or deletion. effective dlm is crucial for maintaining data quality, optimizing performance, reducing storage costs, ensuring compliance, and improving overall system health. without a robust dlm strategy, your salesforce org can become cluttered with redundant, outdated, or trivial (rot) data, leading to performance degradation, compliance risks, and reduced user productivity.

this tutorial will delve deep into the concepts, best practices, and practical implementation of salesforce dlm. we'll cover various stages, techniques, and considerations, along with code examples to illustrate key concepts.

**i. understanding the stages of the data lifecycle**

the data lifecycle typically consists of the following stages:

1. **creation/acquisition:** data is generated or acquired through various channels like user input, integrations, data imports, or automated processes.
2. **storage:** data resides in salesforce, occupying storage space and impacting performance.
3. **usage/maintenance:** data is actively used by users, reports, dashboards, and integrations. it may be updated, enriched, or transformed.
4. **archiving:** data that is no longer actively used but needs to be retained for compliance or historical purposes is moved to a less expensive storage solution.
5. **deletion:** data that is no longer needed for any purpose is permanently deleted. this needs careful consideration to avoid data loss and compliance violations.

**ii. key goals of data lifecycle management in salesforce**

* **data quality:** ensure data accuracy, completeness, consistency, and validity.
* **performance optimization:** reduce data volume to improve query speeds, report generation, and overall system responsiveness.
* **cost reduction:** minimi ...

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data lifecycle management
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data governance
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data migration
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data archiving
data integration
data security
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metadata management
data analytics
data cleansing
data strategy

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