Everything you need to know to get started with data lifecycle management
Companies of all sizes – from startups to well-established Fortune 500 brands – need to proactively and continually consider the best approach to store, manage, protect, and preserve their data. A well-established approach to data management from creation to destruction helps a company maximize the benefit of its data and protect its integrity, while managing risk and maintaining compliance. Adopting a data lifecycle management (DLM) approach is the ideal method for organizing, maintaining, and preserving data within your organization. But what exactly does that mean and how do you implement a DLM approach within your company? Read on to hear our answers to the most pressing questions about DLM.
What is data lifecycle management?
DLM is an approach to managing your data throughout its lifecycle – from creation through destruction. At its core, it is a governance approach to help a company know how to store, organize, use, evaluate, preserve, and destroy all the data that is created or received within the organization. While there are certainly useful products available to help with DLM, it is important to note that it isn’t a single solution. Rather it’s a product-agnostic approach to how a company manages its data.
What are the primary goals of data lifecycle management
Proper data management is essential for any sophisticated organization. Companies are handling more data than ever, making proper management incredibly important. Creating a strategic DLM approach helps companies achieve important organizational benefits by meeting three primary goals.
- Organize data for efficiency – It is widely accepted that a company is only as good as the quality of its data. All organizations have data of varying levels of importance that are being added to by the day. Critical customer information, proprietary product code, or important operational intelligence, for example, are mission-critical and need to be both protected and easily accessible. With a proper DLM approach, data is organized by level of importance and frequency of use or retrieval. It is easily accessible to users who are approved to access it, and it’s regularly maintained for usability. As an added benefit, proper data organization also reduces the overall cost of data ownership.
- Maintain data integrity – Unfortunately, it is all too common for data to be disorganized and chaotic. Think about it. Data often lives in different systems that are set up with unique data entry requirements. Duplicate data runs rampant and unless there is a set data retention policy, it is common for data to be forgotten and kept far past its necessary shelf life. Adding further complication, if there has ever been an acquisition, that adds new data from disparate systems into the mix. It’s no wonder it can feel nearly insurmountable to get a handle on creating order and consistency. A DLM approach serves as the building blocks for how you manage data to maintain its integrity and maximize its usefulness.
- Protect against losses and liabilities – The world is becoming more sophisticated in understanding risks associated with keeping old data without proper management. Regulations such as GDPR, SOX, and CCPA all have been created to ensure organizations take the proper measures to handle data with care. These regulations mean that companies can no longer ignore the proper maintenance and disposal of data. Doing so puts them at risk for legal ramifications and massive fines.
What are the phases of a strategic data lifecycle management approach?
DLM consists of creating a governance framework of best practices and standards for managing data through each phase of its lifecycle.
Phase 1: Data Design & Creation – New data is created all the time, every single day. That data needs to be captured and organized in a way that is both secure and accessible for its future use. Some data might not need to be stored beyond its initial use case, while in other instances, it needs to be categorized and stored appropriately. In a DLM approach, data is evaluated based on its quality and relevance to the business.
Phase 2: Data Storage & Management – Data preservation is the process of maintaining and preserving digital information over time so that it remains accessible and usable. It allows organizations to ensure the long-term availability, integrity, and authenticity of their digital assets. Proper data storage is about much more than simply deciding where you host your data. Of course, that is an important consideration. Is it stored on-premise or in the cloud? What is the backup process to ensure proper redundancy? When storing data, it is typically encrypted, cleansed, compressed, or transformed to ensure integrity and security. Data needs to be stored in a way that is both accessible and secure. And there needs to be systems in place to ensure business continuity should there be a need for disaster recovery.
Phase 3: Data Usability – What are you doing to make sure you are increasing the value of your data? Are you adding encryption to ensure it’s secure? What metadata can be added to increase its usability? Is it organized and categorized in a way that it can be easily accessed and manipulated for its various applications? Data is only as powerful as its usability, so putting parameters around how to optimize it for use is an essential component of proper DLM.
Phase 4: Data Retention & Preservation – When data is not needed on an ongoing basis, but shouldn’t be deleted, it needs to be preserved. Data preservation is the process of maintaining and preserving digital information over time so that it remains accessible and usable. Often this means archiving it in a non-alterable format so that it is available if needed, but not at risk for manipulation.
Another important part of an organization’s DLM strategy is a data retention policy. Considering any industry or regional regulations, a data retention policy outlines a protocol for how long a company will retain various types of information. A comprehensive policy helps a company automate compliance while reducing legal risks, reduces storage costs, and increase the relevance of existing data.
Phase 5: Data Destruction & Deletion – Eventually, data is no longer needed to support an organization’s day-to-day operations and needs to be deleted. Keeping data past its necessary shelf-life is not only costly, but it can also put the organization at risk. But deleting it haphazardly can cause legal ramifications. It is important to be strategic when determining what data gets deleted and what gets archived. Companies will often set up a data deletion committee that is responsible for creating a data deletion policy and action plan. Additionally, it’s common practice to log deletion locations and dates so that you have an up-to-date record.
How do I get started creating a data lifecycle management approach?
- Define the process – The first step is to set guidelines and guardrails for how to manage your data at each phase of its journey. This includes everything from trivial activities like making sure names, dates, and other inputs are added using a standardized format to making rules around how to write data so that it can be used in sophisticated AI use cases.
- Organize existing data – Perhaps the most time-intensive part of the process, you need to organize existing data so that it follows the parameters that you’ve set.
- Execute and maintain your strategy – Once you have your guidelines and parameters set, you need to be relentless about maintaining the process. It cannot be one single person’s job to maintain a DLM strategy. It must be woven into the fabric of the organization. Think about how you’re going to roll out this new approach to the organization, who will be responsible for training new employees, and what support is needed to maintain your DLM approach for the long term.
- Find the right tools – While DLM is an approach, not a product, there are products on the market that can make managing data easier.
If your data strategy is still operating like the wild west, it’s time to put an approach in place that will guide your team on how data is created, used, stored, and deleted. Learn more about Rimage’s approach to helping companies get started on a DLM journey by implementing a Defense in Layers approach to data management.
