What Is Data Lifecycle Management (DLM)? [Explained Simply]
Data lifecycle management can help create the processes and strategies necessary to keep your data compliant and safe.
What is data lifecycle management (DLM)? Data lifecycle management, or DLM, is a way to manage data from creation to deletion. DLM products help to automate the data lifecycle and keep the process compliant and up to industry standards.
What Are the Stages of Data Lifecycle Management?
As the name suggests, DLM, as a practice, is focused on the movement of data from its creation to its ultimate deletion. This journey, while seemingly simple, can become quite complex depending on the systems the data moves through and the uses that an organization applies to its information.
That being said, the strength of a well-conceived DLM strategy is that it grounds this complexity in a foundational and intuitive set of stages, each applying to a time or place where that data is used or transformed.
The typical stages of a DLM strategy usually includes the following:
- Creation: At some point, data is created within the system. “Creation” can refer to specific and intentional creation (such as data acquisition from customers or partners, data entry during business operations, and automated data capture ) or indirectly (collected from user behaviors).
- Collection: As the data is created, it must be collected through data pipelines that draw it from its point of creation to centralized locations. The actual location can vary, including centralized databases, cloud infrastructure, edge computing infrastructure, or temporary storage nodes for processing.
At this stage, you will also find several different methods or applications for gathering this data, all of which impact how it is created. These collection forms can include office apps, automated auditing and logging, or user interfaces such as web forms.
- Processing: Raw data that has been created and collected can be useful, but more often than not, that information must be processed to render it usable. At this stage, operations are in place to exclude dummy or corrupted data, transform data into different formats, compress that data (if required), and secure that data via encryption.
- Storage: Processed data is then stored. At this stage, information is formatted into specific data sets with unique metadata, classifications, or other required formatting.
- Usage: End-users and applications use cleaned, formatted, and stored data. At this stage, data is deployed in visualization applications, training data for machine-learning programs, or data sets for cloud computing software used by enterprise organizations and their employees.
- Management: From front to end, data must be managed to ensure that it is secure, that data privacy is maintained, and that its handling adheres to relevant compliance standards. A data management solution properly provides the operational infrastructure to govern data along these phases.
- Archive: When data isn’t under immediate demand for processing or use, but it serves a purpose in overall operations (whether related to regulatory compliance, industry-standard data-keeping expectations, or long-term data services), then it is archived. These archives will often call for the same security as regular storage but may reside on slower modes of digital storage.
- Destruction: Once there is no longer a reasonable business case for keeping collected data, the organization should destroy it. This goes far beyond simply “deleting” it because deletion does not remove information from physical storage.
Organizations must digitally destroy data (through sanitization and zeroing techniques) or physically destroy storage media (shredding, melting, burning, magnetizing, etc.).
What Are the Benefits of a Data Lifecycle Management Strategy?
It may go without saying that implementing data lifecycle management strategies has major benefits. This may undersell the value of DLM by and large. In many cases, these benefits are technically optional but significant (like reducing costs). In other cases, working with DLM as part of an overall data strategy must follow regulations and security issues.
Some of the benefits include:
Data is near useless if an organization cannot guarantee the integrity of that data. Without such integrity, then analytics and intelligence derived from that data are suspect at best.
A data lifecycle management strategy provides your organization with a way to place comprehensive controls and checks so that data integrity can be guaranteed throughout large, automated processes.
Contributing to Security and Compliance
Cybersecurity and regulatory compliance are critical and necessary for handling private data, especially for any organization handling personally identifiable information (PII), protected health information (PHI), or any other form of protected or private information.
Implementing proper security controls is impossible without a clear understanding of organizational data flows that, in turn, suggest a need for processes around data lifecycles.
Supporting Governance Policies
Data governance, or data management throughout its entire lifetime in organizational systems, is critical for effective information management and integral to most compliance frameworks. There is no effective governance without having a clear handle on data lifecycle management. A DLM strategy must consider how third-party private data is sent, shared, received, and stored and develop a comprehensive third-party risk management (TPRM) strategy.
When it comes to storing, processing, archiving, and collecting data, there are no complete standards from front to back. Different technologies serve different purposes, support different operations, and bring different costs.
With a solid data lifecycle management strategy in place, your organization can better understand what technologies and processes are needed throughout that lifecycle, and if possible, how to manage costs related to those technologies.
What Are Some of the Challenges of Data Lifecycle Management?
Implementing DLM processes and strategies, while beneficial, and in many cases, necessary, are also challenging. It requires a significant effort and understanding around specific and complicated parts of the technological and administrative infrastructure to properly implement DLM and get value from it.
Some of these challenges include:
- Complex Data Streams: When a cloud platform collects data from several, sometimes dozens or hundreds of sources, it takes quite a bit of orchestration to organize them correctly. This orchestration has to consider the creation and collection methods, processing requirements, and so on, which can bury an underprepared IT staff.
- Data Volume: On top of multiple data streams, the sheer volume of data that comes in can swamp the effectiveness of a DLM strategy. Initial plans to implement DLM must consider what’s coming in and provide the correct support (technology, staff, etc.) to process and store that data properly.
- Access Demands: Different departments and stakeholders have vastly different requirements to handle data in their applications. As such, proper DLM implementation must address these various needs without placing the burden of system complexity onto the end-user or their work.
- Security and Compliance: Significantly, the management of massive data workflows must always consider security and compliance. Such consideration is impossible without a clear understanding of the data lifecycle and a working management plan. Accordingly, organizations need to ensure DLM is part of their cybersecurity risk management.
Support Your Data Management Lifecycle With the Secure, Powerful Kiteworks Content Management Platform
To properly implement and maintain a data lifecycle management program, you require the right tools for high-level observability and granular control.
With the Kiteworks Private Content Network, you can connect foundational office applications like file sharing, workflow management, email, messaging, and storage with high-level logging and auditing capabilities. This combines security and compliance controls, seamless communication and security integration, and powerful auditing tools with capabilities that include:
- Security and Compliance: Kiteworks utilizes AES-256 encryption for data at rest and TLS 1.2+ for data in transit. The platform’s hardened virtual appliance, granular controls, authentication, other security stack integrations, and comprehensive logging and audit reporting enable organizations to easily and quickly demonstrate compliance with security standards.
The Kiteworks platform has out-of-the-box compliance reporting for industry and government regulations and standards, such as the Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry Data Security Standard (PCI DSS), SOC 2, and General Data Protection Regulation (GDPR).
In addition, Kiteworks touts certification and compliance with various standards that include, but are not limited to, FedRAMP, FIPS (Federal Information Processing Standards), FISMA (Federal Information Security Management Act), CMMC (Cybersecurity Maturity Model Certification), ISO (International Organization for Standardization), and IRAP (Information Security Registered Assessors Program).
- Audit Logging: With the Kiteworks platform’s immutable audit logs, organizations can trust that attacks are detected sooner and maintain the correct chain of evidence to perform forensics.
Since the system merges and standardizes entries from all the components, Kiteworks’ unified syslog and alerts save security operations center teams crucial time while helping compliance teams to prepare for audits.
- SIEM Integration: Kiteworks supports integration with major security information and event management (SIEM) solutions, including IBM QRadar, ArcSight, FireEye Helix, LogRhythm, and others. It also has the Splunk Forwarder and includes a Splunk App.
- Visibility and Management: The CISO Dashboard in Kiteworks gives organizations an overview of their information: where it is, who is accessing it, how it is being used, and if data being sent, shared, or transferred complies with regulations and standards. The CISO Dashboard enables business leaders to make informed decisions while providing a detailed view of compliance.
- Single-tenant Cloud Environment: File transfers, file storage, and user access occur on a dedicated Kiteworks instance, deployed on-premises, on an organization’s Infrastructure-as-a-Service (IaaS) resources, or hosted as a private single-tenant instance by Kiteworks in the cloud by the Kiteworks Cloud server. This means no shared runtime, shared databases or repositories, shared resources, or potential for cross-cloud breaches or attacks.
Discover the power of Kiteworks for data lifecycle management with a custom demo.