Sometimes before using something, we have to change one or several aspects of it to adapt to our purposes. The most common example is text, as in some cases, we need to translate text from one language to another so that it could be used in the context we want it to be used in. When it comes to data, a sort of analogy to translation is data transformation, which is the process of converting data from one format to another. Needless to say, that this procedure is a must if we want our data to be versatile and useful in many situations.
Data management procedures
As we start to look at data transformation and how it works, the first thing that should be understood is that data transformation is part of a larger host of data management procedures.
This means that when we talk of data transformation, we should also look at the procedures it relates to and goes along with. One of such key processes is data wrangling, which goes from collecting necessary data all the way to fully prepare it for usage. Data transformation is needed to structure the raw data so that it gets organized in a single dataset and ready for the following steps of wrangling.
Similarly, data transformation is used in data warehousing, which aims to store large amounts of historical and new data in an accessible format. The more data is stored, the more necessary it is to give it a singular structure not to get lost in it.
From this, it is clear that data transformation is a crucial step in many data management procedures that involve organizing and structuring data. Transformation is what provides that necessary change in data that makes it possible to move forward with these management procedures.
Data transformation usually follows the same basic steps that allow us to give the dataset the structure we need. These steps start with inspecting the data in order to decide what is best to be done with it. Then proceeds the defining of rules which will allow generating the code for data transformation. Finally, the final product is reviewed to make sure that the desired result is achieved.
Usually, data transformation is performed by combining manual work and tools that assist with the technical tasks of the procedure. Depending on how much of transformation it is from the initial data to the final result we are aiming at, data transformation may be rather simple or more complex.
Why data transformation matters
Learning a basic understanding of the basics of data transformation points to why it is so important. Generally speaking, data transformation is what makes a single batch of data usable in different ways. In other words, it makes the dataset universally applicable, as we are able to change its structure in order to use it for our particular goals.
Additionally, data transformation helps to share the data among the departments of the same company. Since particular departments may use different formats for storing their data, in order to make it shareable and usable for other departments, the data needs to be transformed.
Furthermore, transformation allows increasing data quality in at least two major ways. Firstly, through this process, the data is cleaned, removing errors and redundancies. Secondly, organizing and structuring the data makes the overall quality of the dataset higher as any further deficiencies are easier to spot and overcome in usage.
Finally, through this process, data becomes more readily usable and accessible for users with different skill sets. In addition to being a benefit in itself, this also enhances the efficiency of data utilization, saving time and ensuring steady workflow when working with information.
These are just a few of the major benefits of data transformation. But even aside from these, it is clear that the importance of data transformation for business is immense. Utilizing this procedure allows extracting the most business value from this crucial asset, that is, data.
Into the future
There is no question that the transformation procedure is important for any business that is working with a lot of data. It is a necessity in order to fully utilize the data.
The question that businesses need to answer is what sort of data transformation is needed and how it should be performed. Of course, the answers to this question will be relative to the particular needs of a particular situation.
However, there is a piece of general advice that all interested might take as a guideline. Since data transformation is such a crucial process, there are always developments and innovations to improve it. Therefore, it is always a good idea to look into the new ideas and technology that allow us to perform data transformation better than before.
This procedure is certainly staying as an important part of data management. Thus, it is worthwhile to take a peek at how it will look in the future.