Data is growing by leaps and bounds, both in terms of volume as well as type. IDC’s Global DataSphere Forecast reveals that more than 59 zettabytes (ZB) of data will be created, captured, copied and consumed globally in 2021. It is estimated that this growth will continue through to 2024, with a five-year compound annual growth rate (CAGR) of 26%.
That’s why it is vital for modern businesses to develop a comprehensive data lifecycle management (DLM) strategy to efficiently store and manage increasingly large volumes of data. Read on to find out what data lifecycle management exactly is and why it is so important for today’s data-driven businesses.
What Is Data Lifecycle Management (DLM)?
TechTarget defines Data Lifecycle Management as “a policy-based approach to managing the flow of an information system’s data throughout its lifecycle, right from creation and initial storage to the time when it becomes obsolete and is deleted.” DLM is an organization’s effort to managing its data using different techniques, processes and DLM tools.
While organizations understand the importance of data, protecting, preserving and managing it is a different ball game altogether. Adding to this challenge are complex regulations and the fact that data now resides in multiple places — on-premises, in the cloud, in remote workforce machines and on SaaS platforms.
Having a robust DLM strategy in place will help businesses keep up with constantly evolving regulations and eDiscovery requirements. DLM allows organizations to make the most out of their data. It also gives them greater control over their organization’s data, helps meet archiving needs, minimizes the burden on IT, reduces storage costs, and enables faster decision-making and quick recovery during a crisis.
How Is DLM Different From ILM?
Data lifecycle management and information lifecycle management (ILM) are often confused as synonyms and used interchangeably since both are policy-based approaches to managing data. However, these concepts are designed for different purposes and there are some major differences between them.
DLM is concerned with the flow of data from one stage to another — from data collection or creation to deletion or reuse. DLM aims to answer when certain data should be deleted while ILM deals with the relevancy and accuracy of the information. The key difference between DLM and ILM is that DLM operates on entire files of data or records while ILM operates on what information is in the file. DLM products manage data files based on their type, size and maturity. They allow businesses to search for a certain type of file from a certain period within the stored data. On the other hand, ILM products go beyond that and allow businesses to search various types of files for a particular piece of information in a timely manner, such as a customer’s email address.
While ILM is often considered a subset of DLM, both DLM and ILM are vital to an organization’s data protection strategy.
Stages of Data Lifecycle Management
Since every organization has its own distinct business model, tools and data types, there are many variations of DLM. However, most businesses generally follow these six stages:
Data Collection or Creation:
This is the first stage in the data lifecycle when a new data value enters an organization’s information systems by either using existing data created within a company or acquiring data created externally, or through the reception of signals from various devices such as Internet of Things (IoT). The data collected or created could be either structured or unstructured data.
During this stage, organizations can classify the data based on its file type, such as private, sensitive, internal, public, etc., to define how the data must be processed/managed in the later stages.
Data Storage and Maintenance:
Once data is created or collected, it is essential to securely store it and maintain data hygiene. A comprehensive data backup and recovery process should be in place to ensure data is retained throughout its lifecycle. Appropriate security measures must be implemented to avoid data alteration. Data should be stored in such a way that it maintains compliance with relevant laws and contracts. This stage is not concerned with deriving any useful value from the data.
This is one of the most important stages in a data lifecycle. At this stage, businesses can view, process, modify and save the data. This stage involves making use of data for various organizational purposes, such as decision-making or analysis. There are certain data governance challenges associated with data usage. For instance, understanding if using certain data the way an organization wants can have legal implications.
Data Sharing or Publication:
At this stage, data is shared with employees, customers, stakeholders and various other authorized users. This stage is one of the most vulnerable phases in the data lifecycle since data is being shared internally as well as externally outside an organization, for purposes such as marketing and advertising.
Data archiving is the maintenance of a copy of data that isn’t frequently accessed or used, but must be preserved for litigation and investigation needs. If required, archived data can be restored to an active production environment. An organization’s DLM strategy should clearly define when, where and for how long data should be archived.
Data Deletion or Reuse:
With more than 2.5 quintillion bytes of data generated every day, storing all data is impossible. Adding to the challenge are storage costs and compliance requirements. Therefore, businesses must delete data that they no longer require to create more storage space for active data. During this phase, data is removed from archives when it exceeds the required retention period or no longer serves a meaningful purpose to the organization.
Three Goals of Data Lifecycle Management
Data is the lifeblood of modern business. Therefore, a robust data lifecycle management approach is essential to ensure its security, availability and reliability. With data growing at an exponential rate, the need for proper data management is greater than ever before. To ensure seamless flow of information throughout its lifecycle, DLM has three main goals: confidentiality, integrity and availability, also known as the CIA triad.
Organizations today use and share massive volumes of data every day. This increases the risk of data loss and misuse of information. Therefore, data security and confidentiality are crucial to protect sensitive information, such as financial records, business plans, personally identifiable information (PII), etc., from unauthorized access and cyberattacks.
Once data enters an organization’s storage systems, it is accessed, used and shared among various users. Whenever certain data is in use, it is bound to undergo multiple changes and modifications. An organization’s DLM strategy must ensure the information available to users is accurate, up to date and reliable. Therefore, one of the goals of a DLM strategy is to maintain data integrity by protecting the data while it is in use, in transit and when it is stored.
While it is important to protect data and maintain its integrity, it wouldn’t be of much use if it is unavailable to users when required. Data availability is especially crucial in today’s 24×7 global business environment. DLM aims to ensure data is available and accessible to users when they need it, so critical business operations are unhindered.
Benefits of Data Lifecycle Management
Apart from streamlining the flow of information and optimizing data throughout its lifecycle, a DLM offers several other benefits including:
Some industry compliance standards require organizations to retain data for a certain period. For instance, the Criminal Justice Information Services (CJIS) Security Policy states that the “agency shall retain audit records for at least one year. Once the minimum retention period has passed, the agency shall continue to retain audit records until it is determined they are no longer needed for administrative, legal, audit or other operational purposes.” DLM helps businesses comply with regulations (both local and regional) while also meeting other needs such as audit, legal and investigations.
Organizations rely on data to improve their business operations and make informed decisions. An effective data lifecycle management strategy helps ensure that data is constantly available, consistent, reliable and secure, and is aligned with data privacy regulations.
Given today’s threat landscape, data security is the top concern for business leaders and IT professionals alike. DLM helps organizations protect their data from loss, deletion, cyberattacks and more. It enables businesses to define how their data is treated, used, saved and shared. This helps minimize the risk of data breaches and prevent critical information from being misused.
Value and Efficiency:
Businesses today are data-driven. Data plays a crucial role in driving the strategic initiatives of an organization. Therefore, it’s important for businesses to ensure their company’s data is clean, up to date and authentic. A good DLM strategy ensures that data available to users is accurate and reliable, thereby enabling businesses to derive the most value out of their data. DLM helps maintain data quality throughout its lifecycle, which in turn enables process improvement and increases efficiency.
Reinforce Your Data Lifecycle Management Strategy With Unitrends
Data lifecycle management without the right tools can be cumbersome and time-consuming. Without intelligent automation, data management can become an overwhelming task. And relying on old, ad hoc, manual processes can prove to be fatal to your business. Having the right backup solution in place is the first step towards building a robust DLM strategy. This can help you overcome the challenges of administering increasingly larger volumes of data.
Unitrends can help do away with large swathes of the manual processes involved and ensure business-critical data is preserved and recoverable when needed. Here are a few ways Unitrends can help drive your organization’s DLM strategy:
- Unitrends supports backup and recovery functions for various platforms, from physical workloads to virtual machines and specific application data (i.e., SQL, Oracle and Exchange).
- Our solution provides support for incremental backups, differential backups, log backups, etc.
- It also provides detailed reports on retention, backup history, recovery and compliance testing.
- With Grandfather/Father/Son (GFS) retention, you can manage long-term retention of your backup assets. You can apply customizable retention settings to your local appliance and replication targets, including the Unitrends Cloud. Our backup solution also supports low-cost public clouds, such as AWS S3, AWS S3-IA, Google Cloud Standard, Google Cloud Nearline, Rackspace and Wasabi, for cold copy replication.
- Unitrends Cloud supports specific retention periods — 90 days, one to seven years, or even infinite retention (until deletion is requested or the contract is terminated).
- To enable faster recovery, all data replicated into the Unitrends Cloud is available for self-service recovery with no egress or ingress fees from your local appliance UI. Additionally, data stored on external media can be imported to the appliance and recovered to the production environment.
- For complete peace of mind, Recovery Assurance/recovery testing ensures the integrity and functionality of your data contained within backups.
To find out how Unitrends can help simplify your organization’s data management challenges, request a demo today.