Data Lifecycle Management

Data is ubiquitous. More than ever before, everything is interconnected. It is now crystal clear that data has exceeded the level of just being a discrete form of information. Corporate and organizational data has transformed into an entity that flows throughout the organization’s information systems. Within a corporate environment, data moves through many desks and creates multiple decisions across several departments.

The fact that data has expanded in volume in recent years is not a new thing. It has grown, it is still growing, and it will keep growing. For this reason, Talend stated that the expansion of our data analogy depends on us. We’re the users of this data. We’re the managers, we’re the workers, we roll the ball, and we keep the ball rolling. We are in charge of the health of our data throughout the entire data life cycle.

Businesses are now in better alliance with SaaS( Software as a Service). They employ the use of web applications and web forms and harness more data from these platforms. Simultaneously, the population of people adopting and using the internet is on the rise globally. This implies that more people are clicking links, snapping real-time images, and filling out web forms, which all point to the same thing; more data. Moreover, with the advent of smart devices and the Internet of Things, one has every iota of information at their fingertips.

Nevertheless, with more significant improvement comes more outstanding issues. The idea of having access to numerous data within seconds sounds intriguing; however, data management challenges keep piling up as the volume of data increases. That is, more data means; a higher cost of storage, additional resources are needed for data preparation and data analysis, and the possibility of experiencing a data landfill if the appropriate digital transformation strategies aren’t employed at the right time. This is where we get to appreciate the importance of an effective data management cycle.

Data Life Cycle

Data life cycle, information life cycle, or whatever name we call it, means the same thing. It is the whole period in which a particular data exists in one’s information system. The Data life cycle entails the entire stages that a specific data passes through, starting from the moment it was created. It can be likened to the life cycle of a living thing, from the point of birth to infancy, adolescence, adulthood, and old age. Similarly, various data objects will experience several stages of their life during their time in the information system.

What is Data Management Life Cycle?

Data lifecycle management is a diverse area and entails several forms of data storage. Integrify defines data lifecycle management as effective practices that guide data management in an enterprise—starting from the point of creation of the data till it is archived, with the sole aim of achieving data integrity. Although the form and type of data vary with the kind of enterprise, the critical idea and algorithm that guides the mode of management of this data remain the same.

Data lifecycle management is a policy-dependent approach that manages the flow of data that is present on a particular information system throughout the data’s lifecycle, from its creation to its storage and deletion. Data integrity becomes uncertain if these policies and legal controls are left out of data usage and management.

According to TechTarget, the data life management cycle can be viewed as an automated life cycle management procedure that sorts data into different levels based on specific policies. This procedure also automates data movement from one tier to another contingent upon those policies. Part of what this entails is that the more recent data and data that are more frequently used are stored on faster and more sophisticated media, while data that is of less priority is stored on cheaper media.

Stages of Data Life Cycle Management

An effective data lifecycle management strategy entails these steps:

• Data Creation

Regardless of the means, one obtains information in one way or another. We receive data through data entry, acquisition from existing sources, or signals from our own and other devices. Users continuously create structured and unstructured data via devices, applications, the Internet of Things, machinery, and other means. The method in which data is captured is contingent upon its method of generation and the type of data.

The data creation stage signifies the stage at which the data is introduced into that particular information system.

• Data Processing

After the data has found its way into the system, it doesn’t end there. The data has to be processed to determine the following line of action. There are several processes involved, and the preparations may be dynamic. Activities such as data integration, data validation, and data application, among others, will be carried out. Such that at the end of this stage, the data would have been reformatted, standardized, summarized, and even augmented.

• Data Analysis

This aspect is even more interesting; you analyze and interpret your data. You get to traverse and interpret your data, even though this requires several forms of analysis. These analyses could be in the form of statistical analysis, visualization of artificial intelligence, data modeling, or any other means.

• Data Storage

Now that the earlier stages are checked and successful, you have to store the data for future purposes. Data has to be stored in a stable environment where it is adequately maintained to ascertain its integrity and safety. In this phase, the data is taken through some processes, such as encryption, compression, and transformation. Data storage also ensures the systems are in place to maintain the reliability and redundancy of the system. And to also implement disaster recovery.

• Publicizing the Data

This is the point where you foresee particular possibilities, and you come up with decisions and methods that might help you sort these things out. When you share the information you’ve acquired from data analysis, your data provides you with a practical business value. Data is valuable only if the approved users can use it to their satisfaction.

In this stage, users can access and alter data as needed to suit their daily operations. Furthermore, users can perform other data-related functions such as further analysis, joint work, business ideas, or visualization. Data sharing and usage can lead to the emergence of additional data that can be stored and processed. In essence, this stage allows the users to function effectively.

• Archiving the Data

After a long run of effectiveness and usefulness, it gets to a point whereby data becomes less relevant to the organization’s regular operations and workflows. It’s not totally useless. It has just gone down in terms of priority. At this point, the data can then be archived in a secured and reliable storage system. Either physically or on the web ( cloud storage).

Notwithstanding, the data might still be subjected to specific processes ( such as compliance, reporting, analysis, etc.)  at some points to ensure that its value hasn’t been jeopardized. The archived data has to be fully secured, just like active data. For an archive to be relevant in the future, you must keep its metadata in your records.

• Deletion

Even the best things come to an end, and that includes data. When data reaches its end-of-life, which is utterly useless to the organization’s operations, it can be permanently erased. However, this deletion has to be carried out securely and in line with the guiding data protection regulations.

The data life cycle then goes back to the first stage from the last stage.

It is worth adding that when it comes to data storage and archiving, this process can be easily automated and placed in the area of data protection. Storware Backup and Recovery allows you to implement such a solution for multi-format data.

Goals Of Data Lifecycle Management

A significant portion of modern enterprises relies on data. This has called for the adoption of an effective data lifecycle management technique that ensures the security, accessibility, and integrity of organizational data. Unitrend affirms that the three primary goals of data lifecycle management are  Confidentiality, Integrity, and Availability. Otherwise known as the CIA triad.

• Confidentiality

Modern businesses utilize and share a voluminous amount of data regularly. This heightens their susceptibility to data loss and misuse. Therefore, the security of data and its confidentiality are essential for protecting prioritized information such as business plans, client details, financial records, etc., from cyber-attacks and unwanted access.

• Integrity

When data finds its way into an organization’s storage systems, it will be accessed, used, and shared among several users. When these users start to use this data, they are likely to bring in alterations and modifications. An organization’s data life cycle management method must guarantee the availability of the data to the users in its accurate and reliable form.

• Availability

The integrity and confidentiality of data would be useless if the users could not access it when needed. Data availability is of top priority in today’s business environment. An effective data lifecycle management strategy must ensure authorized users gain access whenever possible. So that business operations are not impeded.

An effective data lifecycle management strategy must be employed and maintained to ensure a coherent flow of data within an organization.