What Is a Data Lake Retention Policy?

A data retention policy is an organized and documented set of protocols and policies meant to retain information. Companies tend to establish these conventions for three reasons: 

• To retain the information necessary to remain compliant with industry regulations  
• To collect information for their own operational needs  
• To store data more efficiently to prevent the formation of data lakes
  

These policies serve a vital role in the data regulation process for any organization that uses vast quantities of data, such as large corporations, government bodies, or healthcare providers. 

What Is Data Retention?

Data retention is a system of information storage for a certain period. It involves a combination of the organizations’ data retention policies (described above). Organizations that practice data retention intend to preserve that data for future use and therefore require an organized and systematic approach to its storage. 

Retention also involves objective measurement of data’s usefulness, so it can be discarded when no longer necessary, and the organization can avoid any resource waste incurred by keeping it. 

How Does Data Retention Fit into the Greater Data Lifecycle? 

Data retention comes into play midway through the data lifecycle. The data lake retention policy plays a key role throughout the remainder of its lifecycle as it determines which data users can access, how they can access it, and when the data should be deleted. 

The data lifecycle begins with data generation and collection. After collection, the organization processes and sorts the data and enters it into storage. 

Your answer to the question “what is a data lake retention policy?” appears here. The data lake retention policy determines how the data is processed, where it is stored, how it is stored, how it is backed up, and when it will ultimately be deleted. 

Data lake retention policies help organizations sort their data into usable warehouses with basic infrastructure. Users can develop that structure into a more rigid storage architecture, which brings the data to the next stage of its lifecycle management. 

This management eases the analysis process with rapid access to vast quantities of data. Computers can sort through organized data much faster than an unorganized data lake. Easier sorting provides faster visualization and analysis of the data so that decision-makers can leverage the data in the most effective way possible. 

What Is a Data Lake Retention Policy?

A data lake retention policy is a system of documentation with requirements and guidelines for data storage. Rather than a universal series of guidelines that all organizations can follow, the organization writes the rules for itself. The policy addresses the organization’s unique needs, goals, and regulatory requirements. 

It outlines and clarifies the answers to important questions for easy reference. The policy leaves no room for interpretation so the enterprise can avoid ambiguity and questions in the future. 

A data lack retention policy answers these questions: 

• Who takes responsibility for the management of which data?  
• What types of data does the enterprise gather?  
• What compliance rules must the enterprise follow for each type of data?  
• What types of data should be stored?  
• What types of data should be archived?  
• What types of data should or should not be retained? 
• Which types of data are stored in which location?  
• How long should each type of data be retained?

Such a policy becomes exponentially more complicated every time the organization adds another form of data. This complexity necessitates an explicit written policy. 

Having the document laid down in text makes it an unchangeable reference with clear documentation of any adjustments—and little room for interpretation or “games of telephone.” 

Objective of a Data Retention Policy

A quality retention policy should seek to cover the entire data storage lifecycle. While this complexity can create a lengthy and technical document, writers must be thorough to avoid oversights. A good policy will meet several objectives, including: 

Availability 

Authorized users will know where to find data relevant to their work and can access it with little delay when needed. 

Compliance 

The organization will store its data according to industry standards, legal requirements, and other policies. 

Secure Storage 

Increased cybercriminal threats require great security. Big data provides a tempting target for hostile actors, who can sell consumer or enterprise data for profit. A data retention policy must document current security methods and provide a clear outline for future iterations and upgrades. 

Appropriate Deletion 

Most data has a temporary purpose. It is not cost-effective to store all collected data for an indefinite period. 

Thus, a data retention policy should outline a system to evaluate data. If the data fails to meet these standards, then the data retention policy should also lay out a clear path for secure and permanent deletion.  

How to Create a Data Retention Policy

Creating a data retention policy can be a difficult proposition for an existing enterprise since it spans the organization’s entire tech stack. Fortunately, the process can be broken down into a few simple steps. These milestones provide clear progression markers and project goals:

Identify Who Will Lead Data Maintenance 

If this role is not already assigned, the team that takes on the responsibility of data maintenance faces a difficult task. They must be prepared to keep abreast of regulatory updates and monitor the entire data infrastructure at all times to enforce the established data retention policy. 

It is entirely reasonable for enterprises to hire additional team members or entire teams to handle this responsibility. Alternatively, they can assign a managed service provider to handle the work on their behalf—but must take care to confirm that third-party assistance does not violate internal or external compliance requirements. 

This step comes first so those responsible for data maintenance can contribute to and familiarize themselves with the data retention policy as it develops. 

Map Out and Categorize Data

This step is the most daunting for enterprises that have operated with significant data use for an extended period but have not yet begun to organize their retention. The enterprise must map out all of its data, including: 

• The data source  
• The method by which data is collected  
• The data’s purpose  
• The lifespan of the data’s usefulness  
• The location of the data—on-premises, private cloud, public cloud, etc.  
• How the data is deleted 

Enterprises should take advantage of this careful analysis process to sort the data into categories. Those categories can derive from a variety of characteristics, including: 

• Source  
• Purpose  
• Regulatory requirements  
• Subject/focus 

These categories can also overlap. For example, credit card data stored by an internet shopping company gathers that information from its customers to ease checkout for repeat customers. 

This data fits into multiple classifications. It could be stored alongside customer email addresses since both come from customers and aim to ease the checkout process—but it will be separated from customer email addresses thanks to regulations that require a higher standard of security for financial information. 

Assess Policy and Business Needs for Each Type of Data

Categorization during the organization step simplifies the next step in the process. Once the enterprise has identified the types of data it possesses and sorts them into clear categories, it can assign guidelines to those categories rather than individual “mini-lakes” of data. 

These guidelines cover how the business can use the data (which also helps it determine the ROI for that type of data) and which policies it needs to write to keep the data secure and properly maintained with minimal cost. 

Write Data Retention Rules for Each Type of Data

As an organization moves into assessment, assessment determines the writing of the rules. Once the enterprise identifies the needs of each type of data, it can put those needs into writing. 

The well-defined rules then provide guidelines for procedures—a series of instructions that tell members of the enterprise how to enact and act upon those rules. 

Establish SOPs

Standard operating procedures (SOPs) define how the enterprise should act on its rules. They translate regulations into actions and rules into reality. 

Finalize Data Retention Policy 

The data retention policy should undergo several rounds of revision, editing, and finalization both throughout the process and when completed. All those involved should review every step of the process relevant to them and confirm: 

• Data storage will meet regulations 
• Policies are reasonable, and procedures are achievable with provided staff  
• The policy leaves room for iteration but no room for ambiguity 

The enterprise can then enact the data retention policy. 

Enlist the Help of Cloud SaaS to Support Data Retention

When necessary, the enterprise can take advantage of a third-party software-as-a-service (SaaS) provider to combine multiple clouds and stacks of data. Multiple data types subject to different storage regulations may see storage in separate media but need to interact to maintain smooth business operations. 

Multicloud SaaS services such as Seagate Lyve Cloud can ease the data-integration process and provide a secure bridge for cloud data transfer. 

Make Retention Policy Visible Company-Wide 

The policy only works if every employee who interacts with data understands the policy and risks. Your policy must be available, both in full and in part, to all employees. Regular training sessions can supplement the policy and inform workers of updates to ensure continued compliance.