Life cycle refers to all the phases of data during its existence, right from creation to its distribution and reuse. As you can see above, data goes through various phases before becoming obsolete. Just like the human life cycle involves birth, childhood, adolescence, adulthood, and death, the life cycle of data is as follows:
- Collection: Data is first collected through various sources. Sources can be internal or external; internal sources are existing databases, customer queries, and so on, whereas external sources are social media, news, websites, and blogs.
- Selection: Since data is collected from many sources, it may happen that all the data collected might not be necessary. Hence, the next step is to select only that data that is relevant for the particular operation.
- Cleaning: Once the data has been selected for a particular use, it needs to be For this purpose, data needs to be freed from noises, errors, or inconsistencies.
- Transformation: It might also happen that the data is not ready in the appropriate format for further processing; for this, we’ll transform it.
- Study: Once data is transformed accordingly, it is studied. This is an important step. Here data is used to discover new patterns, insights, and information. This is also known as data mining.
- Implementing: The information, patterns, and insights so discovered are used in machine learning. Using the information discovered, machines are programmed to solve a given problem with the information provided. This further enhances the process of machine learning.
- Conclusion: Finally, once this whole routine is completed, results are presented using charts, graphs, or maps. Data visualization helps people involved understand the data better.
The data life cycle isn’t necessarily divided into as many steps as mentioned above. There are alternative forms in which life cycles are divided into more or lesser stages than those mentioned above.
The data life cycle is important to study because it allows us to understand the process involved in extracting information from the data and what improvements can be made to the process for it to be more efficient. The data life cycle management is also important because it allows the organizations to make better use of data in more than one operation. It also helps in improving the storage, reuse, and disposal of data. All of these consequently help the organization in becoming more efficient, saving costs, and making the most of the data that they already possess.
In today’s data-sensitive environment, it is of utmost importance that organizations can manage data as efficiently as possible. The Data life cycle also highlights the complications faced in data analysis. For instance, dealing with inconsistencies, noises, and errors can be a tedious task. If such erroneous data is further passed on and used for the analysis, the results from the entire process will be misleading, and the organization could face huge losses.
Frequently Asked Questions (FAQs)
- What is the data life cycle?
Data life cycle refers to the entire life of data, right from its creation to conclusions drawn from it.
- What are the alternative methods of understanding the data life cycle?
The method presented above is the 7 step model of the data life cycle. In this, you can see, the life cycle is divided into seven steps. Similarly, there are 4, 5, and 8 step models in which the life cycle of data is divided into 4, 5, and 8 stages, respectively. For your convenience, you can use any of these to study the life cycle, or, if possible, you can also divide the life cycle as you deem fit (but only for your own understanding).
- Which of the alternative methods is better?
When it comes to actual implementation, all the models yield the same result. The variety in the steps is provided only for students to understand the life cycle better. Here, the effectiveness of a model varies from student to student.
- Why should one study data life cycle?
Data life cycle helps us understand the process of data analysis a little better and also provides insights into what data goes through before it provides some useful information. We can also understand the processes involved and how we can be more efficient in extracting information from the data.