4 Steps to Get Started with Data Lifecycle Management

Data Lifecycle Management - March 01, 2023
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How to start creating a strategic DLM approach

For organizations of all sizes and within all industries, data is the center of the universe. All day, every day, data is created, sent, received, edited, moved, and deleted. And while this data provides varying degrees of value, it also has inherent risk. Without proper management, data can become disorganized at best, and a liability at worst. IT teams need a strategic approach to manage their data at each phase throughout its lifecycle, setting policies and guardrails for everything from how long to keep data and where to store it, to identifying usage patterns and looking for vulnerabilities.

At its core, data lifecycle management (DLM) is an 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. This is vitally important because data needs to be preserved and managed properly to ensure its usability and protect the company from security and legal risks.

Since it can be overwhelming to get started creating a DLM approach, we’re here to guide you through it. Done right, a holistic DLM strategy will serve as the building blocks for how your IT team thinks about and manages data for years to come.

Step 1: Identify your biggest data concerns

DLM involves creating a framework of best practices and standards for managing the flow of data throughout its lifecycle. Generally speaking, DLM consists of 5 main phases:

  • Phase 1: Data Design & Creation
  • Phase 2: Data Storage & Management
  • Phase 3: Data Usability
  • Phase 4: Data Retention & Preservation
  • Phase 5: Data Destruction & Deletion

Rather than trying to boil the ocean and attack it all at once, the best way to get started on creating a DLM approach is to identify an area where you have a concern and work outwards from there. Most commonly, we’re seeing the biggest area of need being data retention and preservation. As you attack solving this area of concern, you’ll find it easier to expand to process along each of the four phases.

Step 2: Understand your data regulation requirements & risks

Depending on where you operate and what industry you work in, there are likely numerous data policies and regulations that you need to adhere to. GDPR, CCPA, HIPPA, and SOC, just to name a few. If you process data related to children or students, there are additional protection requirements you need to understand.

You need to identify where data related to these regulations is being created and hosted so that you can build systems to easily comply with any requests related to the regulations. Take GDPR, for example. One of the primary components of that regulation is that any EU resident can request their PII data be sent to them and/or deleted at any time. If you don’t know where that data is stored and have an easy way to access it, those requests become a burden on your team and put the organization at risk of non-compliance.

One thing we know for certain is that data regulations are not going away any time soon. Completing a risk assessment so that you know exactly which regulations impact your business is essential to any effective DLM strategy.

Step 3: Map data retention & preservation policies

Once you know what regulations you need to adhere to, you can begin to map retention and preservation policies to ensure compliance. In addition to third-party regulations, you’ll likely want to develop your own data retention policies to reduce legal and security risks. Keeping any data around longer than its usable shelf-life makes it vulnerable to attacks and can potentially be summoned in legal cases.

When it comes to preservation, several factors influence the choice of a data storage process, including:

  • Data Type: The type of data being stored will have a significant impact on the choice of storage process. For example, structured data such as customer names, addresses, and purchase histories can be stored in a relational database, while unstructured data such as images, videos, and audio files may require a different storage approach, such as object-based storage.
  • Data Volume: The volume of data that needs to be stored also plays a role in determining the storage process. Larger volumes of data may require distributed storage approaches that can scale horizontally to accommodate growth.
  • Performance Requirements: The performance requirements of the system can dictate the storage process used. For example, if the system requires fast access to data, a high-performance storage solution such as solid-state drives (SSDs) may be necessary.
  • Access Patterns: How data is accessed by the application or users can influence the storage process used. For example, if data is frequently updated, a transactional storage solution such as a relational database may be needed.
  • Cost: The cost of storage can be a factor in determining the storage process. Some storage solutions may be more expensive than others, so it is important to weigh the cost against the performance and scalability requirements of the system.
  • Security: The security of the data is another important consideration. Some storage processes may provide better security features, such as encryption and access controls, than others.
  • Compliance: Depending on the nature of the data being stored, compliance requirements may dictate the storage process used. For example, data that is subject to specific regulatory requirements, such as health or financial data, may need to be stored in a specific manner to comply with industry standards and regulations.

Step 4: Find the right tools and get help from experts

As mentioned, this process can be overwhelming. But the good news is, you don’t have to go it alone. There are a lot of tools and experts that specialize in helping organizations effectively manage their data. There are tools to help with each component of data management – everything from governance to archival and recovery. The tools and expertise you need are largely going to depend on a few factors:

  • Size of your company: The bigger the company, the more complex it will be to put governance policies into place. You need to consider how you’re going to educate employees across your organization and what steps you need to have in place to enforce any necessary policies. In addition, if there has ever been an acquisition, this can add significant complexity to unraveling your current data reality.
  • What types of data you process: Even if you’re a small dental office, for example, if you process and store data related to children, then you need to be aware of the added complexity in your data management processes. Understanding what data you have is essential to understanding what tools and processes you need to manage it.
  • Your risk level of being a cyberattack target: All companies are at risk of being a victim of a cyberattack. However, companies that work with healthcare, government, or financial institutions are at an increased risk. Know your threat level so you can take the necessary precautions to safeguard any sensitive information.

Data management needs to be an integral part of an organization’s IT strategy. When done with intention, data management helps a business streamline its operations and protects it from a myriad of risks. Learn more about why a comprehensive data lifecycle management strategy is the key to efficient, effective, and secure IT management and get tips on how to get started creating a DLM approach, by reading our Data Lifecycle Management FAQ.

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