Salesforce Data Lifecycle Management Strategy

Have you ever thought that perhaps everything is data? Yes, we’re actually surrounded by data in our lives. In reality enterprises, like yours, that leverage the Salesforce cloud platform to manage the plethora of your business processes are overburdened by massive heaps of data. Do you know that enterprise data is expected to grow at the rate of nearly 463 exabytes each day by 2025?

With data being literally everywhere & being interconnected, it has essentially become a cohesive living entity that flows through the entire Salesforce system, passing through several departments. In simpler terms, the entire period of time the data exists within the system is coined as a data lifecycle. This encompasses all the stages the data passes through, from its generation to the time it’s deleted:

  • Generation – Data in Salesforce can be generated either by the organization itself, or by its customers, & sometimes by the third-party apps the enterprise may or may not be aware of. 
  • Collection – Next step is identifying which data should be captured as well as determining the best means of doing so (forms, direct observation, surveys, etc).
  • Processing – Once data has been collected, it must be processed where it is transformed from its raw form into something more meaningful & useful.
  • Storage – Storing data for future use is the next step which is commonly done by creating datasets in the primary data storage of Salesforce.
  • Management – Storage is followed by the ongoing process of data management which involves organizing, storing, & retrieving data as and when necessary.
  • Analysis – Next this data needs to be analyzed in an attempt to gain meaningful insights from it. Commonly used methods include algorithms, data mining, artificial intelligence, & machine learning.
  • Interpretation – Finally, the interpretation phase of the data lifecycle commences where users make sense of the analyzed data in order to make informed business decisions. 

Also Read: 5 Mistakes to Avoid Related to Data Storage in Salesforce

What Exactly is Data Lifecycle Management?

If you also feel the entire data lifecycle is overwhelming, believe it or not, you’re not the only one. In fact, the entire burden of maintaining optimal data health in Salesforce falls upon the company’s data lifecycle management strategy. Simply put, Data Lifecycle Management (DLM) is a process that helps organizations in managing the flow of business data throughout its lifecycle, from initial creation to destruction. By defining, organizing, & creating policies about data management at every stage of its life, DLM helps companies get the most out of their data until it’s deleted.

Data Lifecycle Management

Phases of Data Lifecycle Management in Salesforce

Data Generation & Collection – The first phase of any data lifecycle management strategy starts with data gathering. Now, this data (cases, leads, evets, etc) can be enerated from multiple objects, both standards & custom-desined. For this phase, companies usually establish a set of rules to gather data in standardized formats so it can be accessed & managed later on. Data is typically created in an organization in one of three ways: data acquisition, manual data entry, or data capture.

Data Storage – Once data has been created within the organization, it is stored & protected in the data storage of Salesforce, with the appropriate level of security applied. This data can either become an active asset to be used & reused, or it can be classified as inactive and be directly archived. It’s also recommended to properly monitor the storage at all times to gain important insights from it, like understanding what objects generate the most amount of data. Having well-established policies is always recommended at this step.

Phases of Data Lifecycle Management in Salesforce

Data Maintenance – Next comes the data maintenance stage which includes several processes, including inspecting & enriching data before making it accessible to the right users. This data could be further processed to suit different business needs, such as finance, marketing, customer relationship management, etc., As part of the data maintenance process, data is also classified as internal, sensitive, restricted, & public.

Data Usage – This is finally the stage where data plays an active role in making business decisions, based on the information revealed by the data. While during the previous stages, data was collected, organized, inspected, & sorted, this is the phase where data properly supports several different business functions. At this stage, data must be easily available to users both inside & outside the organization.

Data Purging – In this final stage, old/inactive data records are purged, deleted, destroyed, or archived from the active level of production, depending upon the need. This is usually done since the data storage in Salesforce is quite expensive & because of the fact that once data has exceeded its usage limit, it’s good to purge it from the storage. An important thing to ensure before deleting data is that it has exceeded its required regulatory compliance period.

Where Does Data Archiving Come In?

When there is a chance that the inactive data might be useful in the future or simply the data still hasn’t fulfilled its retention period, then data archiving emerges as the best option. As part of the data lifecycle management strategy, enterprises usually create policies on how to archive the selected records & separate them from the active data. Whenever necessary, archived data can be restored into the active production environment for further usage.

If a comprehensive data archiving strategy for Salesforce is what you’re looking for, we can help you with that. Our data archival platform is always at your disposal. With our archival platform, enterprises get the option to either archive their legacy data from Salesforce into its native Big Objects or archive it into any external database (Postgres, Redshift, SQL Server, Oracle) by leveraging the cloud (AWS, Azure, GCP, Heroku) or on-premise platform. This helps in 80-90% data storage costs reduction, 10 times improvement in system performance, giving 100% data accessibility & visibility, & simplified compliance.

To understand the data archiving process with our data archival platform for Salesforce & learn what their benefits are, you can check out this datasheet. Or simply reach out to us & we’ll resolve any query you may have. 

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DataArchiva is the ONLY Native Data Archiving Solution for Salesforce using Big Objects that help Salesforce application users archive their historical data without losing data integrity.

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