As information technology makes new inroads in our professional and personal lives, the volumes of data is also increasing manifolds. According to one estimate, a whopping 60 Billion terabytes of data will be generated only during the year 2020.
With every passing day, we are inching closer to a fully digital and automated world. However, the lifeblood of such intelligent and indigenous systems is data. It is data that reveals valuable insights about previously undiscovered phenomena.
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The Challenges of a Data Driven World
As we inch closer towards an ultra-connected 5G world, the volumes of data being generated every single day will increase manifolds. Due to the sheer volume of data being created every second, effective management of this data is becoming a challenge.
What is Data Lifecycle Management?
In the present era, data lifecycle management has become a domain in itself. Data lifecycle management can be defined as the complete set of protocols and procedures that are adopted right from the generation to the ultimate destruction of obsolete data.
In-between these two extremes, creation and ultimately destruction, data undergoes many changes. The whole science of understanding and managing every phase of data is classified as data lifecycle management.
Cloud Computing and Data Lifecycle Management
The domain of cloud computing revolves around an intricate and highly coordinated management of virtualized computing resources. Add data to the mix and you have an even bigger task at hand.
To handle your valuable data, you need a secure and reliable Cloud Service Provider (CSP) like dinCloud. We have a global footprint of data centers that are fully equipped in terms of security and technology to manage your data effectively.
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Data Lifecycle and Hybrid Cloud Environments
Every organization that deploys cloud computing has certain end goals behind such a strategic move. From a data lifecycle management perspective, a hybrid cloud environment becomes even more challenging upfront.
In any hybrid cloud environment, organizational workloads have been segregated between on premise and cloud provider’s infrastructure such as dinCloud. This also involves constant exchange of data between these two infrastructures.
Technology and Data Lifecycle Management
Given the complex stages through which data passes from its creation till the end, technology and automation are perhaps the best answers to the whole management process. In this post, we will cover some key aspects of data’s lifecycle.
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Data Lifecycle Strategy
At the organizational level, you will need an effective data lifecycle management strategy that will jot down how all the data that’s being gathered or generated shall be handled. This roadmap will make the implementation phase much easier and auditable.
Creation of Data
In this phase, you will map out all the sources of your data. You will also need to differentiate whether the data is originating from outside the organization or otherwise. A further distribution would be whether it’s created on the cloud service or on premise.
Data Storage Considerations
Once you have identified data sources, you will attach relative importance to each data stream. At this phase, your entity will also need to evaluate any regulatory or compliance centric issues related to the data.
As a rule of thumb, cloud is the most efficient and cost effective platform for storing data. However, you will have to evaluate the data center certifications of your cloud provider to allay any compliance related concerns.
dinCloud’s global data centers are independently certified for some of the best international standards laid down for physical and cyber security of data. With dinCloud as your cloud provider (CSP), meeting regulations won’t be a concern in most cases.
Platform for Data Analysis
The end goal of generating, gathering and sorting terabytes of data is objective analysis. This enables organizations to extract valuable and actionable business insights. The more your data in terms of volume, the more robust architecture would it require.
Cloud providers like dinCloud, with nearly limitless supply of compute and storage, are the best platform when it comes to data analysis. In addition to the resourcefulness of the cloud, it is also a very cost efficient means of playing around with data.
Backing Up Data
This is one of the most important and controversial aspects of data lifecycle management. Every department of an organization would never feel comfortable with the thought of declaring certain data obsolete or redundant.
Instead, the general mindset is to keep piling on terabytes of data, with the approach that what if we might need it going forward. While this may be true in rare cases, there is a certain financial and administrative cost attached to maintaining vast amounts of data.
Therefore, organizations will have to evolve a relative metric for each class of data, which enables them to identify data that’s worth keeping and one that’s no more required. The next phase is defining the relevancy period for each class of data.
Once this has been done, the next phase would be selecting a platform for backing up your data. In most cases, the data centers of a quality cloud provider like dinCloud will be the best option from all the available alternatives.
The data centers of dinCloud have some of the best cyber security measures in place for both internal vulnerabilities and external threats. The greatest advantage of backing up data over the cloud is that you are also tending to disaster recovery (DR) concerns.
Leading CSPs like dinCloud maintain multiple backups of your valuable data on regular intervals. To further protect high value data, dinCloud maintains backups of your data at physically dispersed data centers as a further line of defense against loss.
Deletion of Obsolete Data
This is also a very important aspect of the data lifecycle management process. When you have identified the data that’s no more required or its relevancy cycle is complete, you will also need to phase out or delete your data.
The valuable storage resources you free up from this process will make way for the more valuable data instead. A good strategy at this phase is to maintain a temporary backup of even your deleted data for a reasonable period, say 15-45 days.
Automation of Data Lifecycle
To achieve the best results from your data lifecycle management, you need to automate most of these processes. Another very important aspect of your data lifecycle is constant review of strategy and how effectively is it being implemented across the entity.
Conclusion
Data lifecycle management is indeed a domain whose importance is increasing with every passing day. As the size of an organization and its corresponding data increases, so does the need for effectively managing its data lifecycle.
