What Is Data Transformation: Definition, Challenges, And Benefits?
Advancement in technology and its increased usage provides researchers, businesses, and governments with valuable information about people, animals, and different aspects of the environment. A variety of tools have been developed to mine, process, analyze, store, and manage data. Data is the new gold. Astute business executives know this. That explains why there is so much talk about data in business and technology circles. Most organizations now employ a team of professionals to set up systems for data collection and management.
Data comes in different forms and structures. It requires a skilled data scientist or engineer to handle it appropriately. There are different processes involved in handling data. One of the key processes in dealing with data is called data transformation.
Definition Of Data Transformation?
In simple terms, it is the process through which data is converted from one form to another. It involves getting data that is stored in one format and changing it to another. Most times when you hear people talking about data transformation, they are referring to large volumes of data being processed by large organizations. everyday people perform basic data transformation. This includes simple processes like converting a document from Text file (TXT) format to portable document format (PDF). Other examples of data transformation include converting speech to text, comma-separated values (CSV) to extensible markup language (XML), etc.
Big organizations and government bodies deal with large volumes of data. They require advanced systems and tools to effectively store and manage data. Whenever you are handling such volumes of data, it may necessitate that you use different storage systems and applications. As long as you use different systems to handle data, you will occasionally be required to transform data from one format to another.
Who Can Transform Data?
It depends on the volume of data you are dealing with. Basic data transformation needs like converting from PDF to JPEG format requires basic computer knowledge. There are online tools where you simply upload the file you wish to convert and they do the rest. However, when you are dealing with large volumes of data, you need specialized skills. This is where professionals like data scientists and data engineers come in.
What Are The Processes Involved In Data Transformation?
Data is converted from one format to another in about 3 steps depending on the circumstance and methods used. Below are the common steps of data transformation.
You must first identify the kind of data you possess. Interpreting data is a difficult process, especially where files are given differing extension names from what they actually a. for example, a document can be given the extension. AVI. That is why you need sophisticated computer applications to interpret data accurately.
Data quality assessment
After data has been interpreted, it is ready to be checked for quality. This helps you to discover and get rid of corrupted files. If this is not done, you may experience difficulties when translating data.
This is the process by which data is restricted to meet required specifications. After the translation is complete, you can now assess it for quality.
Some Of The Common Tools Used In Data Transformation
To effectively convert data from one format to another, you require a special set of tools. Here are some of the tools popular among data scientists and engineers.
- dbt: It is most preferred by analytic engineers who have an SQL background. It is free for individual usage. But if you have a large team to collaborate on data transformation, you are required to pay $50 for each team member per month.
- Hevo: this is perfect for those who don’t like writing code. It provides an automated process to manage data. It has a simple user interface you’ll find no trouble navigating. Hevo is free for basic usage, but if you need more customization and richer features, the starter plan starts at $249 per month.
- IBM InfoSphere DataStage: this is a cloud data transformation tool. It has an in-built search and the error highlighting features make it easy for you to identify problem areas. It costs $2500 per month for the starter package.
- Airflow: This is more than just a data transformation tool. It can help you streamline all data-related projects. Airflow is a top-quality open-source application.
Challenges Of Data Transformation
- It is expensive and Tools used for data storage and data transformation are costly. And hiring data professionals can cost you a fortune.
- Data transformation requires a lot of computing power and This may limit or slow down other departments in an organization.
- Since experienced data professionals are few, it is difficult to hire and retain quality talent.
Benefits Of Data Transformation
Data transformation plays a key role in helping businesses utilize different formats of data. Businesses collect data from different sources. The only way they can make use of this data is by converting it to a compatible form. For any business to stay competitive in this digital age, it must have the ability to gather and analyze data. This way, they can gain critical insights into different aspects of their operations. Data transformation helps to organize data so that it is easy for people and computers to make use of it.
When data can be accessed in different formats, productivity is enhanced. It enables organizations to easily share data with different partners. They can always provide the data in the most applicable formats.
It promotes data security and control. Data can be transformed into a secure format and managed accordingly. Data transformation reduces time wastage. People don’t have to spend a lot of time trying to figure out how to convert data to specific formats. Businesses are in a better position to make decisions about current and future plans if they have access to quality data.
Data transformation is a critical component of doing business today. It is very difficult for businesses to stay ahead of the competition without quality data. This is why companies must invest generously in data transformation tools and personnel.