Understanding data lifecycle management

thumbnail for blog post on the three main goals of data lifecycle management

Statistics about the global volume of data are staggering. A whopping 90% of an organization’s data is unstructured, according to exclusive research from IDC, and the amount is growing (organizations will generate over 73,000 exabytes globally in 2023).

As the volume of data continues to grow, you need efficient management of your organization’s entire data lifecycle. That’s how you ensure your content — the lifeblood of your business — remains secure, reliable, and available.

In this blog, we’ll explore the three main goals of data lifecycle management — confidentiality, integrity, and availability — to understand better how to unleash your content’s full potential.

3 main goals of data lifecycle management

What is data lifecycle management (DLM)?

Data lifecycle management (DLM) is a strategic approach to managing an organization’s data assets by optimizing their entire journey from initial acquisition or generation to storage, archival, retention, and secure disposal. 

Establishing data lifecycle program management requires defining and enforcing policies, roles, and responsibilities for data throughout its lifecycle — giving you greater control and visibility over your content, ensuring it’s accurate, secure, and used in compliance with regulations. 

What are the three main goals of data lifecycle management?

Confidentiality, integrity, and availability — often referred to as the CIA triad — are the three main goals of data lifecycle management. Here’s a quick breakdown.

GoalDescriptionMeasures
ConfidentialityProtecting sensitive information from unauthorized access or disclosure, maintaining its privacy, and preventing breachesEncryption, access control, and data classification
IntegrityMaintaining the accuracy and reliability of data throughout its lifecycle, preventing unauthorized alterations, corruption, or tamperingData validation and version control
AvailabilityEnsuring the data is consistently accessible to authorized users when needed, minimizing downtime, and enabling business continuityCentralized cloud storage, backup, redundancy, failover, and disaster recovery

Now, let’s look deeper at the importance of confidentiality, integrity, and availability for data governance, security, and compliance.

Data confidentiality

To achieve this data lifecycle management goal, you need to safeguard your content from unauthorized access, ensuring sensitive information remains confidential. 

Here’s how DLM helps maintain data security and privacy:

  • Classifying data based on its sensitivity and importance, which helps identify which content requires the highest level of protection
  • Implementing control mechanisms that limit access to data only to authorized individuals or systems 
  • Deploying encryption measures to protect data at rest and in transit — so in case of a breach, the data remains unreadable without the appropriate decryption keys

Data integrity 

The goal here is to preserve the quality and reliability of data throughout its lifecycle. In a survey by Precisely and LeBow College of Business, three out of four respondents said data quality is their organization’s top concern. Ensuring the data remains accurate, consistent, and unaltered is critical for decision making. In the banking industry, for example, a small error in data could result in significant financial losses, while in healthcare, inaccurate patient records could lead to misdiagnoses or improper treatments.

Typical DLM measures to support data integrity include

  • Implementing checks during data entry to verify accuracy
  • Identifying issues such as omissions, incompleteness, or duplication
  • Tracking changes made to data to maintain a historical record

Data availability

Availability is about ensuring information is accessible when needed. To achieve this goal, you need to eliminate data silos and prevent downtime. That’s why your data lifecycle management strategy should include:

  • Scalable cloud storageCentralizing data storage in the cloud enhances accessibility and allows for scalability while minimizing the risks associated with data fragmentation and local storage limitations.
  • Redundancy and backups: Duplicating data across multiple servers or cloud platforms ensures that if one system fails, another quickly takes over.
  • Load balancing: Distributing traffic across multiple servers prevents overload on a single server and enables continued access.
  • Disaster recovery: Setting procedures to guarantee data availability during unforeseen disasters, such as natural catastrophes, cyberattacks, or system failures. This plan typically includes clear protocols for data restoration, minimizing downtime and data loss.

Data availability is critical to operational efficiency. According to IDC, 96% of organizations with mostly (or completely) siloed unstructured data don’t know what information lies inside their content. 

Why is data lifecycle management important?

An executive survey from NewVantage Partners indicates that only 53% of companies have a formalized data strategy to realize business value. This statistic is concerning, given the importance of data management in helping enterprises optimize their operations and drive results. 

Consider these benefits of the data lifecycle management framework.

Making the most out of data assets to improve decision making

Only 29% of those surveyed by Accenture have “high trust” in their organization’s data, which is important to deriving value from it. By eliminating redundant, inaccurate, or obsolete information, DLM ensures your data is reliable, so you’re able to unlock actionable insights and gain a competitive advantage. 

Reducing inefficiencies to cut down costs

Implementing a data lifecycle management policy leads to operational efficiency and cost savings, too. Automated classification and archival processes, for example, reduce the time and manual effort required to manage data. DLM optimizes resource utilization by preventing unnecessary data duplication, overloading of storage systems, and the costs associated with managing vast quantities of redundant or obsolete information. 

Safeguarding information to mitigate data risks 

Data is a valuable asset, but it can also be a liability if not managed properly. Data lifecycle management is important to mitigate risks such as:

Data lifecycle management solutions establish robust enterprise storage, backup, and security measures to protect your assets from external threats and internal mishaps. This way, you’re able to minimize the potential for breaches and reduce the financial and reputational damage that results from these incidents.

Meeting compliance requirements to avoid financial burden

Data security is also intertwined with legal and regulatory compliance. Different regions and sectors have various stringent regulations governing data collection, storage, and handling, particularly concerning sensitive or personal information. That’s the case with GDPR in Europe and HIPAA in healthcare

By setting up retention and disposal policies, documenting sensitive content management practices, and implementing security measures, you demonstrate your organization’s commitment to data protection and adherence to relevant laws and regulations. 

The data lifecycle management process

stages of data lifecycle management process

The data lifecycle refers to the entire time the data exists in your system. Here are the key stages for an effective data lifecycle management process.

  • Data creation and acquisition: The initial step involves collecting and generating data, obtaining necessary permissions or consents when dealing with personal or sensitive information, establishing standardized formats and metadata tagging, and cleaning and classifying content. 
  • Data storage and backup: This step includes selecting the right storage solutions and ensuring information is stored and backed up in secure vaults to prevent unauthorized access.
  • Data archival and retention: This step involves storing data that is infrequently accessed in a cost-effective way. Data archival is critical to maintain compliance and make historical data accessible when needed. By establishing data retention policies, you define how long data should be retained, taking into account legal and regulatory requirements, business needs, and data relevance.
  • Data destruction: At some point, data becomes obsolete or poses a security risk. To mitigate potential breaches, make sure to prioritize secure and irreversible data disposal methods. 

Learn how to make the most of these stages with our content lifecycle management best practices guide.

Improve your lifecycle data management with Box

Box makes it easy for you to manage the entire data lifecycle securely. With the Content Cloud, you centralize your content on a single platform. You’re able to create and share files, edit collaboratively in real time, track changes, collect signatures, customize retention policies, and so much more.  

By providing a single content layer, the Content Cloud empowers your teams to work the way they want. Our enterprise-grade security and compliance give you the peace of mind that all your data is protected, from creation to deletion.

Leading organizations trust Box to mitigate risks and ensure data confidentiality, integrity, and availability. We simplify data governance with flexible retention schedules, preservation for defensible discovery, disposition management, and other features. 

Reach out to our team and discover how to achieve all the main goals of data lifecycle management with the Content Cloud.

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**While we maintain our steadfast commitment to offering products and services with best-in-class privacy, security, and compliance, the information provided in this blogpost is not intended to constitute legal advice. We strongly encourage prospective and current customers to perform their own due diligence when assessing compliance with applicable laws.

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