Data is one of your organization’s most valuable assets and resources. With it, you can expand market share, boost customer satisfaction and revenue, improve productivity, and make smarter decisions. To safeguard enterprise data and use it effectively, you first need to understand how the data lifecycle works in your organization.
What Is the Data Lifecycle?
The data lifecycle refers to the series of processes that data goes through in your organization. The cycle starts at “birth” and ends at “death,” from the data’s creation to its disposal or deletion.
Data lifecycle management is closely related to this information ecosystem. DLM allows you to establish controls and set objectives for your organization’s use of data. With strategic data governance and strong information security, enterprises can stay compliant with regulatory frameworks but also extract more value from their data.
What Are the Stages of the Data Lifecycle?
Technically, the data lifecycle is different for every organization. Cardholder data may require processes for tokenization. Companies subject to GDPR may need to factor in anonymization or pseudonymization.
That said, many enterprises share similar data lifecycle stages. These general categories give you a good starting point for mapping your organization’s unique data lifecycle for management.
1. Data Planning
Even though planning happens before the creation of data, it’s an important part of DLM. This stage helps you approach data management strategically instead of haphazardly. It involves:
- Setting goals for data capture and utilization
- Standardizing the data lifecycle, including definitions
- Selecting formats for data collection and storage
- Identifying relevant regulatory requirements and creating privacy and security policies to match
- Establishing internal rules for data handling
- Determining what controls and audit processes are necessary for DLM
The GDPR framework also requires data minimization. This concept means actively limiting how much information you capture on data subjects and how long you store it for.
2. Data Creation and Capture
Data capture and creation are two sides of the same coin. Both generate new data that your company can use:
- Employees create countless reports, emails, and other documents.
- IoT devices like sensors and manufacturing equipment generate operating metrics.
- Your website captures data on user behavior, including purchases, cart actions, and session length.
- Billing software creates invoices, statements, and account data for B2B customers.
- Cloud-based platforms capture internal and external user data, such as activity logs, records, and user-generated content.
A common issue enterprises face at this stage is allowing data collection to run wild. Without DLM safeguards, overly broad data capture can lead to serious regulatory violations with HIPAA, GDPR, PCI DSS, and CMMC.
The more information you collect — especially sensitive data like cardholder information — the greater your risk in the event of a data breach. For this reason, DLM assessments go hand in hand with the risk management lifecycle. Create a comprehensive inventory of all data types and locations.
3. Data Preparation and Processing
Data capture produces raw information, but not always in a format your company can readily use. During data processing, the information is converted:
- Format conversion: HTML to PDF, or CSV to Excel files
- Structuring: For example, turning raw text into spreadsheets
- Extraction: Capturing key snippets of data, such as CRM insights from invoices
- Classification: Identifying and categorizing data for different purposes, from delivering services to improving marketing
- Scrubbing: Eliminating personally identifiable information and keeping statistics
- Cleaning and validation: Confirming data accuracy and eliminating errors
The specific ways you prepare data depend on your organization, industry, and objectives. Many companies also encrypt sensitive data for greater cybersecurity.
4. Data Storage
The data storage phase involves determining where to house records and how, such as physical servers, cloud storage, and digital file repositories. You also need to develop a naming protocol and document workflow.
Proper data storage plays a significant role in regulatory compliance and cybersecurity. A centralized structure prevents data silos and improves communications for enterprise risk management.
Knowing exactly where each user’s data is stored is vital for GDPR records requests. Data handling best practices include ensuring all sensitive data is automatically stored in a secure environment. Automated workflows make it easier to prevent unauthorized access and avoid vulnerabilities from human error.
5. Data Usage
This stage is where organizational data should spend most of its lifecycle. Improving data utilization can benefit every enterprise department, from HR hiring decisions to quarterly financial goals. Analytics enhance the accuracy of quantitative risk assessments, management processes, and mitigation strategies.
Data usage supports internal and external objectives:
- Tracking regulatory compliance in different departments
- Identifying customer ordering patterns to anticipate inventory requirements
- Locating production bottlenecks
- Sharing real-time risk data between departments
- Developing tailored marketing campaigns for different target audiences
- Strengthening supply-chain security and the contract management lifecycle
When you align data workflows to your organization’s unique operations, the right information reaches the right people automatically. Audit reports, network security logs, and qualified leads are more effective this way.
6. Data Governance and Maintenance
Data governance involves preserving access to critical information. Data loss prevention safeguards are one example, such as making sure workers can’t accidentally modify or delete, or intentionally steal, core operational data.
Data maintenance includes redundancy controls. Properly configured backup copies of sensitive information help to reduce the impact of ransomware attacks.
At the same time, you also need to eliminate unnecessary redundancy. This reduces confusion over document versions and makes policy adoption easier for employees.
7. Data Archiving or Deletion
Some organizational data should never be deleted. Many legal, financial, and proprietary documents must be archived securely according to your data retention policy. Encryption can help make these records secure.
On the other hand, eliminating user data when it doesn’t serve a purpose is a good way to improve cybersecurity and simplify compliance requirements. Many industry frameworks require the creation of privacy and security policies that include specific data destruction processes and timeframes.
Improve Data Lifecycle Management With Automated Compliance
The lifecycle of data handling isn’t fixed. It should be a living system that adapts to your company’s objectives, regulatory environment, and risk posture. Every stage can be improved with metrics and audits.
Compyl is an ideal tool for enterprise data lifecycle management. It can help you develop automated workflows and improve data utilization. Discover the benefits of this state-of-the-art compliance solution today.
