Data Lifecycle Management (DLM) involves the management of flow of data from the initial creation stage to the last stage, when it becomes obsolete and needs to be deleted. It is a policy-based approach for the complete lifecycle of data.
In accordance with specified policies, data is divided into various tiers. This makes the data organization task easier to manage. Data Migration, between these tiers, also becomes automated. Newer and frequently accessed data is stored on faster and expensive storage media, whereas less frequently visited data is kept on slower and comparatively in-expensive media.
Major Goals Of DLM
On average, organizations are dealing with management of huge chunks of data. It is usually stored in on-premise, Edge Environments, Cloud Platforms, or a smart mix of all these. An efficient, comprehensive and smart strategy to manage all this data is vital.
For an effective DLM strategy, enterprises broadly have the following goals in mind.
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Confidentiality and Security of Data
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Data Integrity
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Data Availability
Data is a valuable asset for organizations and keeping it safe and secure is their utmost priority. Securing organizations’ sensitive and confidential data is important and Cloud Platforms, like dinCloud, make sure they deliver in this regard.
Data integrity comprises of accuracy and reliability of data. Here, factors like the number of people accessing data, number of copies being produced and the number of people performing tasks on that data, are not all that important. What really matters is that data integrity is never compromised.
Data availability means easy access to relevant data by approved users, regardless of where, how and when they want to access it. This minimizes work disruption and makes daily operations easier.
There are numerous compliance and regulatory acts that are encouraging organizations to develop a secure environment to safeguard sensitive and confidential data. Some of these regulations are the Health Insurance Portability and Accountability Act (HIPAA), California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR).
Experts in the field of data management emphasize that DLM should be regarded as a detailed approach that includes well defined practices and procedures.
Phases of Data Lifecycle Management
To understand the framework of working with data, DLM can be divided into multiple phases throughout its lifecycle. With a few minor changes, broadly all organizations follow similar structures.
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Collection and Generation of Data
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Data Management and Storage
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Sharing and Usage of Data
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Data Archives
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Destroyed Data
Users, applications, Internet of Things (IoT) devices and other smart gadgets are continuously generating data, both structured and un-structured. Data generation process takes data collection process into account. For example, if an enormous amount of machine data is generated, only in-consistent data is collected.
Integrity and security of data must be ensured by storing it in well maintained and stable environments.
Data goes through some processing, like encryption, compression, transformation and / or cleansing. To avoid redundancy and disasters, enterprises make sure that all systems are in place. This, in turn, would guarantee reliability and availability of stored data.
Collaborations, business intelligence, advanced analytics or visualization are important data-related operations. In this phase, it is ensured that there is smooth running of these operations. Only authorized users should have access to data to carry out these everyday operations. Data usage, sometimes, results in further data creation, and this newly created data must also be stored and processed. This is a very important phase, as it makes sure that authorized users are able to do their job smoothly.
Certain data does not need to be accessed on a daily basis for workflow. It is important, but not required by enterprises on a day-to-day basis. This is where tape storage and Cloud Platforms come into play, storing this data on a long-term basis. The availability and viability of this data is important, as it can be needed anytime for analysis, compliance or reporting purposes. This is why this data is archived like active data to be accessed anytime.
There comes a time when certain organizational data is no longer needed. This is when it has reached its end-of-life cycle. At this point, this data can be permanently deleted. This process is done securely and data protection regulations must not be violated even at this stage.
It is important to remember that this data lifecycle does not necessarily have to be linear. The first three stages can occur all at the same time as well, and as it is evident that the third stage might even be responsible for generation of more data. Often, data generation, collection, storage and availability, for authorized users, occurs all at the same time.
Disaster Recovery (DR) process is also important when we look at DLM. Off-premise data storage locations, like Cloud Platforms, are used for added protection of data. In case of any natural disaster, the lost data, applications and IT resources would be recovered by Disaster Recovery processes. The data stored in the Cloud Platforms will be recovered much more easily in case the on-premise servers are down.
Other Systems and DLM
Often, Hierarchical Storage Management (HSM) is confused with DLM, whereas in reality, HSM is only one part of the broad DLM. Hard disk drives (HDDs), SSDs, optical storage or tape systems are a few examples of HSM. These storages have varying costs and performance attributes. With HSM software, there is an automation in the guidelines defined by administrators. These guidelines include things like the number of times a file is copied to backup storage, and how long is the time interval between those two successive copies.
Differentiation DLM from ILM
Although, Information Lifecycle Management (ILM) and DLM are sometimes used interchangeably, they have an important difference, in that ILM products are much more complex, whereas DLM products include general file attributes (age, type, size etc.). Unlike DLM, ILM product can be used to locate personal data.
Contact dinCloud, an ATSG company, which provides top-notch cloud services for effective Data Lifecycle Management (DLM), while maintaining the full integrity and security of enterprise data.
