Data life cycle is a term used in the data management field to describe a process of managing an asset from creation, through use or consumption, to final disposal.
The three main goals of data lifecycle management are:
Data Security and confidentiality: Preserving and protecting the data assets in accordance with existing legal and regulatory requirements.
Availability at all times: Ensuring that both historical and current information is available for retrieval in order to support decision making, planning, testing/evaluation, etc. Verifying that business processes have been followed when processing data assets.
Long Term Structural integrity: Valuing company time investment by supporting efficient uses of resources.”
In this article you will learn about what Data life cycle is and what the three main goals of Data life cycle management is.
What is data life cycle?
The term “data life cycle” has its origin in the life cycle of software products and data lifecycle management (DLM) applications. Data applied here refers to any digital information, which can be stored, processed and transmitted. Digital information might include documents, databases or other records.
All digital information eventually needs to be disposed of after it’s out of date or no longer used by the organization. Data Life Cycle (DLC) refers to the process of managing an asset from creation to final disposal.
The term “data life cycle” was coined by Ray Ozzie at Lotus, back in 1985. Ray Ozzie was working on Lotus 1-2-3, an early spreadsheet software product that required users to enter data into the program, then save it, then re-enter it later when needed.
This process could be repeated ad infinitum, but Ozzie realized that there were significant overhead costs associated with compiling and saving the data each time the program needed to be used. This led him to develop a tool called “Data Abstraction Layer.” Using this technology he created Lotus Development Corp. Digital Research Group, that later became the core of Lotus Notes product.
Data life cycle management is the set of tools and procedures that support management of enterprise data. The goal of data life cycle management is to create a process that allows the organization to gain maximum value from their information assets.
This includes capturing insights and improving efficiencies wherever possible .
Data Life Cycle Management (DLM) tools are used to help an organization manage their information assets throughout their lifecycle. Apart from managing data requirements, Data Life Cycle Management can also be used to manage products, processes, applications and people within an organization .
Data life cycle management software is used by all kinds of enterprises regardless of if they are public or private sector companies.
(3) Three Main Goals of Data lifecycle explained with examples
1. Data Security and confidentiality:
The first goal of data lifecycle is the security of data. Preserving and protecting the data assets in accordance with existing legal and regulatory requirements. The data is the driving force in the digital age and it is only fair that when needed, people can access it and still trust in its confidentiality. Data has been collected, stored and analyzed continuously for over a decade with no clear boundaries or limits to what information organizations are allowed to have access to.
However, with the rise in cyber security, data privacy has become a major concern for many companies. For example, GDPR came into effect on 25 May 2018 and covers personal data, such as your name, address, e-mail address and IP address you share on platforms like Facebook and Google etc. which can be used for commercial or marketing purposes.
Confidentiality of this data is being challenged on a daily basis, with data being hacked by either intentional or unintentional means. The latest example of the latter is the hacking of data held on National Health Service (NHS) computers.
2. Data Availability at all times:
The second goal of Data lifecycle is availability of data. Ensuring that both historical and current information is available for retrieval in order to support decision making, planning, testing/evaluation, etc. The presence of information in some form does not guarantee its usefulness, especially in the IoT (The Internet of Things) sphere where even trivial data may be used for malicious purposes.
For example, if some IoT device has data that is still “raw” and not ready to be processed, it needs to be transferred and stored instantly without any delay so that it can be used or accessed when needed.
As organizations continue to move towards digital services and channels, the demand for digital data has increased exponentially. A survey carried out by research firm Gartner in 2013 shows that the global processing of 2.3 zettabytes (250 billion gigabytes) of data will be surpassed by 2021.
Verifying business processes have been followed when processing data assets. Data needs to be used for its intended purpose and companies need their data reported correctly at all times (e.g., year end audit).
3. Long Term Structural integrity:
Valuing company time investment by supporting efficient uses of resources is the third goal of data lifecycle.
Huge amounts of data are being produced in an effort to maximize sales, sales leads, sales deals, sales results…all the way to sales. All this data management is causing significant issues in the field of information security and business process efficiency.
The data life cycle should be managed with a focus on product life cycles so that the organization can meet SLAs easily. This is because product rollout can be delayed due to long-term maintenance activities since it will take more time for the organization to resolve bugs affecting the operations of the product after roll out rollout. So proper data management can help reduce costs and time for maintenance activities with long term structural integrity.
There are three main stages in Data Lifesyle Management Process:
- Creation, where data assets are created in a system and linked to a business process.
- Archiving, where data assets are transferred from a systems to an archive.
- Disposal, where data assets are destroyed or made obsolete, usually by destruction or removal from the system(s) in which they were held. Any old computer hardware can be considered disposable under this definition. Software is usually not considered disposable from the point of view of the software industry.
Advantages of employing data lifecycle management in business
The three main goals of data lifecycle brings about these advantages:
- Understanding the current and future business needs.
- Creating a process that can be automated and carried out with a minimal amount of human intervention. This will help the organization save time, money and most importantly ensure faster data transfer without any human error.
- It automates most of the processes related to transferring data from one place to another in a secure way .
- It helps with ensuring that there is no loss in quality or security of the data when transferred from one place to another . It also helps in providing accountability when data is being transferred from one system to another.
- It also ensures that there is no redundancy or decrease in quality when moving between systems. Data which has been transferred is error-free and this helps in ensuring that the accuracy of data also remains the same.
- It also provides a reliable processing environment that helps with ensuring that the data which is being processed is not susceptible to errors, loss of information or loss of integrity.
- It also ensures that there are no conflicts between data which can be transferred from one system to another . This helps ensure that there are no conflicts between data which is being processed by different systems .
- It also ensures that there are no conflicts within a single system. This ensures that there are not any errors due to conflicting records between different systems .
- It also ensures that there are no conflicting records between different systems.
Conclusion of the Three Main Goals of Data lifecycle
Data Lifesyle Management (DLM) is a structured framework that can help drive business strategy, services, planning and implementation. By creating the higher level strategy for the organization, it helps plan for how data will be managed throughout its lifecycle.
The three main goals of data lifecycle is to make sure that 1. Security and confidentiality of data is maintained, 2. That the said data is readily available when needed and finally 3. To maintain long term integrity of such data.
It helps in developing detailed plans that can be followed by the members of an organization . It also helps in developing strategies for retention of data. With DLM the organizations are able to ensure continuity in service provisioning by establishing corporate policies regarding data disposal so that stakeholders are aware of their responsibilities towards the retention of data.