When it comes to data management, the data lifecycle (also known as the information lifecycle) refers to the full-time period during which data is present in the system. This lifecycle includes all the stages your data goes through from the time it is first captured to the time it is deleted from your servers.
The first stage is data creation. During this phase, you obtain information in a variety of ways; you may generate data from data entry, acquire existing data from other sources, or receive data from devices. This stage essentially refers to the point when data first enters your system, regardless of how it was obtained.
Second, data processing is carried out, which entails data cleansing and the preparation of raw data for later analysis. The data is normally reformatted, summarized, and then validated in order for it to be usable.
The data is often analysed during the third stage of the data lifecycle, which is the most important stage of the process. In this stage, a great deal of data analysis, such as statistical analysis, data modelling, and the use of artificial intelligence may be performed on the data.
When we speak of the fourth step, we are referring to the exchange and publication of data. The findings of the analysis are obtained, and we may be able to make forecasts and make business decisions based on the information obtained from the results.
As soon as all of the decisions have been made in the last stage, the data is archived for future reference, which is referred to as archiving.
In an organization,
Consider the case of a person who is a citizen of a foreign country. He possesses a large number of documents, including his driving licence, password, and other identification cards.
All of those have a picture of him on them. One day, he is pulled over for speeding and is taken into custody. He now has a photograph that is included in the criminal database as well.
Now, all of those photographs have different properties that have been allocated to them, such as the DL Number for a driving licence or the __ID__for a criminal database, and these IDs are utilized throughout the system to identify the images or the person.
These IDs may be used by a user to gain access to information through the public system, which is why they are referred to as public keys.
These public keys are particularly distinct, may have a variety of characteristics, and may be stored in a variety of formats, whereas their generation, such as the DL number, may have StateCode-Year-Date-Unique number in their public keys as the dynamic structure allows keys to be stored in a variety of formats and they can be generated in vast quantities.
When we establish or design a public key structure for data lifecycle management, we often gain a number of benefits. The structure only needs to be developed and tested for one application, and if it is successful, it may be readily implemented or added to another.
As long as the capability for accessing and preserving the data is established once, the time to market inside the business is reduced significantly.
The use of public keys in conjunction with __versioning__and validity allows for the creation of a complete lifecycle for an entity, which improves troubleshooting, error-correction, audit-ability, and other aspects of the entity's operation.
If there is an error that needs to be corrected, it is simple to fix it by updating the link between the public key and the entity object.
The whole history will be available to us, and troubleshooting will be simpler: after all, the incorrect public key was used for the object at some point in time—it may have been used in interfaces, for example.
When dealing with large amounts of data, the use of public keys becomes necessary since public keys aid in the tracking down of data, its attributes, and the history of the data.
It is necessary when we need to look for information on a specific attribute.
Since these public keys may describe the same properties in multiple systems, and because the public key types and features may differ per instance, it is recommended that maintenance be performed on a per instance basis.