Data Lifecycle Management

What is data lifecycle management?

Data lifecycle management (DLM)  is the process of managing business data from creation to deletion. As work becomes increasingly data-driven, businesses must develop policies and procedures for generating, storing, and, eventually, retiring their data. DLM is not a specific product but rather an approach to managing proprietary data. The process includes managing applications, systems, databases, and storage media.

Some software act as repositories for data and help manage the data lifecycle, such as product data management (PDM) software for designing and manufacturing new products and big data integration platforms for retaining massive data sets and managing the data pipeline.

Types of data lifecycle management

There are multiple phases and types of data lifecycle management. These steps are incremental and range from collection to expiration of data.

• Data collection: Data is gathered to eventually be stored and accessed.
• Data storage: Captured data is coded into a business’ database. Some may enter “cold storage”, meaning it may not be useful now but will be in the future. 
• Data preparation: The next step in DLM entails preparing and cleansing data so it is in the right format for usage and interpretation.
• Data usage: Data is advanced from preparation to usage for projects and analysis.
• Data maintenance: The goal of this stage is to ensure relevant data is available for the right team. Data maintenance often occurs when managing CRM databases.
• Data cleaning: Data that is no longer relevant is either purged, destroyed, or archived.

Benefits of data lifecycle management

An effective DLM system has the potential to improve internal processes for collecting, storing, and synthesizing data. Below are the key benefits of data lifecycle management:

• Compliance with regulatory requirements: Every industry and region has unique regulations and requirements surrounding the collection of data, especially with concerns to consumer privacy rights. An automated process for data maintenance helps ensure a business follows laws and regulations around data protection.

• Efficient business processes: Effective data management allows easy access to the right information at the right time. DLM efficiently automates the validation, enrichment, and integration of data.

• Security: DLM codifies secure storage processes, and also provides contingency plans in the event of emergency data crashes or breaches.

Data lifecycle management best practices

There are several best practices to consider when managing the lifecycle of internal data.

• Deployment of automated solutions: DLM strategies must be iterable and clear. This can only happen when an organization deploys automated solutions in the DLM process that organize information into tiers.
• Internal alignment on DLM policies: All employees must be aligned on the policies and processes of DLM. Clear-cut guidelines ensure internal efficiency and adherence to policies and procedures.
• Defined data types: Data cannot be stored haphazardly. Companies must determine clear criteria for categorization to ensure data is properly stored for easy access and increased integrity.
• Contingency planning: Even the most secure DLM systems are not immune to data loss. Thus, contingency plans must be in place to prevent permanent deletion if data integrity is compromised.
• Implementing naming conventions: Unsearchable data is an avoidable form of data loss. To ensure convenient access to data, knowledge management policies, such as consistent naming conventions and file naming processes must be used.

Data lifecycle management vs. information lifecycle management

Data lifecycle management (DLM) often gets confused with information lifecycle management (ILM). However, they are not the same thing, and it is important to highlight how they differ. ILM is primarily concerned with individual data points stored in files, whereas DLM is concerned with the file as a whole. For example, DLM would deal with general attributes of data files, such as type, size, or age. On the other hand, ILM helps handle individual data points like customer numbers. Effectively, they are two sides of the same coin.

Data lifecycle management discussions on G2

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