Thomas Redman aka Data Doc did itwhen he said"Where there is data smoke, there is commercial fire." With data now the most important asset for most organizations, data breaches, mishandling, or loss can and will cause significant disruption to many areas of operations. And to mitigate these risks, you'll need to implement data lifecycle management (DLM).
Protecting and effectively managing your organization's information is more important than ever, and DLM is an approach that aims toensure data privacyand security from data creation to data destruction. DLM offers benefits such as risk management, a better understanding of a company's requirements and objectives, and optimal decision making.
DLM also enables the ability to recover data at unprecedented speeds during high-pressure situations.
In this article, we'll take a look at everything you need to know about data lifecycle management to ensure you fully understand the approach and can optimize control over multifaceted organizational data.
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- Overview: What is data lifecycle management?
- Three main goals and benefits of DLM
- Data Lifecycle Management Framework
- DLM versus other systems
- DLM FAQ
- How Varonis can help
Overview: What is data lifecycle management?
Data Lifecycle Management (DLM) is an approach for businesses that maximizes the benefits of data acquired or generated. DLM also serves to mitigate potential risks related to data collection, storage, or transmission. By implementing DLM, organizations are better protected against ransomware, phishing, and other malicious attacks.
As information technology has progressed, businesses and organizations have shifted from conventional data storage approaches to using digital databases for data management. This evolution has led companies to become aware of the importance of correctly managing the data life cycle, not only to optimize the information but also to mitigate the associated risks.
DLM solutions automate lifecycle management processes associated with data, organize information into tiers and categories according to specific policies, and automate data migration between tiers. Newer and more frequently accessed data is also typically stored on faster and more expensive storage media than less critical data in most DLM implementations.
Three main goals and benefits of DLM
The runaway rise of data means organizations are storing information in more places and platforms than ever before. This includes on-premises servers, cloud environments, and edge computing systems.
The need for DLM is great; Below are three main goals and benefits that any good data lifecycle management program encompasses.
1. Security and confidentiality
One of the main goals of DLM is to ensure that data is stored securely at all times. DLM ensures that private, confidential or sensitive information is continuously protected against possible manipulation, theft or compromise. With DLM, you benefit from a comprehensive approach to protecting sensitive information from internal and external threats.
2. Data integrity
A successful DLM strategy must be able to retain the original form of any data, track all changes, and give visibility to key decision makers. Data must be accurate and reliable, no matter where it's stored, who works with it, and no matter how many copies there are.Maintain data integrityguarantees that the information used is accurate, complete and safe to work with.
3. Availability of data
Data is useless if it's not available for use by teams in your organization, but too much availability can cause problems if left unchecked. Approved users should have access to data where and when they need it, without disruption to workflows or daily operations.
Once you understand the main purposes and benefits of DLM, you'll be ready to take a closer look at the entire DLM framework and how it applies specifically to your organization.
Data Lifecycle Management Framework
Each company has its own way of interpreting and classifying data, depending on its business model, software tools, and individual data management strategies. However, the stages that data goes through during its lifetime tend to be consistent across most scenarios.
- Data Creation:The first phase of DLM is the creation and capture of data. This can take many forms, from PDFs and images to Word documents, SQL database information, and SaaS data that residesplatforms like Salesforce CRM. You can purchase existing data from an external organization or manually enter data internally. The information generated by the devices or systems is also at this stage of the framework in the form of data capture.
- Data storage:Once the data is acquired, entered or captured, you will need to store it. In the DLM methodology, this means protecting data commensurate with the confidentiality or importance of that information. You'll also need to implement a robust backup and recovery process that ensures long-term data retention. Establish policies around data storage and the ways cloud and storage environments containing inactive data will be used.
- Data Usage:Data is classified, used, and shared by members of your organization at this stage. You must ensure compliance with regulatory data policies while using it. Usage is often considered the most sensitive phase in the data lifecycle, so you should establish tracking systems and audit trails to ensure changes or alterations to the data are documented. In certain cases, the data may also be available to people outside of your organization.
- Data file:Archiving your data in a safe and secure environment is the next phase of the DLM framework. An archive is simply a place where data is stored without the need for maintenance or general use. Data that is no longer needed for ongoing business operations should be archived and separated from data that is actively used to avoid mixing or mishandling. Your archive should also be able to restore your data to a working environment immediately, if and when necessary.
- Data destruction:Data is the driving force behind an organization, but it can also be a toxic asset. Keeping data that is useless only exposes you to the risks associated with it. Therefore, you will need to destroy the data at some point for security and compliance reasons such asGDPR, which incorporates the principles of DLM. Data destruction typically takes place at the file location and should be done in accordance with your organization's DLM policies. How you destroy data will also depend on the media or devices it resides on, from idle data storage drives to private cloud servers.
The form these stages will take depends on your organization's business processes anddata security platformas well as applicableprivacy regulations like GDPR and CCPA. It is also important to note that the phases will not necessarily be linear. Data creation, storage, and use often occur simultaneously across all business operations.
DLM versus other systems
While DLM is quickly becoming the standard forpromote data securityand end-to-end information confidentiality, there are a few other frameworks that organizations might consider implementing. Below we've listed two other data management standards and detailed how they compare to DLM.
DLM x HSM
Hierarchical Storage Management (HSM) is sometimes confused with DLM, but in reality, HSM is much less comprehensive. The HSM is an automated software tool used to differentiate between various types of storage media, such as solid-state drives, optical storage, and hard disk drives, each representing a different level of cost and performance.
HSM also focuses on the cost effectiveness of each device while maintaining affordability and performance. With the HSM, administrators can set guidelines for how often various types of files should be accessed, copied, or backed up. Once the administrator establishes these guidelines, the HSM software manages and implements the specifications.
Therefore, while the HSM is useful in various phases of DLM, such as archiving and storage, it is merely a tool as opposed to the holistic nature of the DLM framework.
DLM x ILM
Another strategy comparable to DLM is the information management (ILM) lifecycle, which is fundamentally information-centric. This includes digitally and physically stored information, such as a customer's phone number or social security number. ILM policy requires the handling of such information in all formats, including letters sent through the mail and hard copies on file.
In addition, ILM focuses on how specific information can be searched. While DLM software tools are adept at allowing administrators to sort information by categories, such as file size and attributes, ILM tools are often more effective at locating specific information. ILM tools and strategies can also be useful for regulatory compliance.
For example, the GDPR ensures that every customer has the "right to be forgotten," which means that individuals can legally request that their personal data be erased from the organization's databases. ILM is useful for locating and deleting individual records if this request is made.
Q: Who should use DLM?
A: Any organization that deals with sensitive and private data subject to regulatory compliance should use DLM. If your business collects or stores information such as bank account numbers, contact information, health-related data, etc., implementing DLM is critical.
Q: Why is DLM important?
A: Using DLM creates processes around data collection, access, use, and destruction that protect your information and satisfy regulators. DLM also helps create profitable frameworks around how your technology stack interacts with data.
Q: What are the main objectives of DLM?
A: Security, confidentiality, integrity and availability are key DLM guidelines and are built into every phase, from data collection and creation to archiving and destruction. DLM seeks to properly protect and dispose of data while making it available for your access and use.
How Varonis can help
Varonis can help you with your DLM efforts by identifying sensitive data, mapping who has access and helping to ensure that only the right people have access, and monitoring the use of that data for suspicious activity that puts your organization at risk.
1. Security and confidentiality
Varonis automatically discovers and classifies where sensitive data resides in your on-premises and cloud environments. We can apply sensitivity labels that fully integrate with Microsoft Information Protection (MIP), allowing you to easily mark your data for encryption, obfuscation, or deletion, helping to enforce your data protection policies.
2. Data integrity
Varonis monitors your data activity and provides a complete audit trail of events so you can easily see and report which users have access to your data and what they are doing with it.
Use Varonis to create security and privacy policies that automatically move, archive, quarantine, or delete data based on content type, age, sensitivity, and access activity.
3. Availability of data
Varonis provides granular data access and permissions mapping. By monitoring and analyzing how users access and share data, we can identify excessive access to sensitive data and provide recommendations for where permissions should be revoked—and we can even do this automatically at scale—all without disrupting daily operations.
This helps ensure that only the right people have access to the data they need to perform their roles, and allows you to scale permissions appropriately to enforce a least-privilege access model.
DLM adoption helps organizations protect information, be profitable andidentify vulnerabilitiesin your data technology ecosystem. Almost every organization that handles sensitive or private data that needs protection should seriously consider implementing DLM, along withalerting softwareand detect compromises in real time. Working with an experienced partner like Varonis to formulate the right DLM strategy will help you manage information properly and ensure end-to-end compliance.