What is Data Transformation? Steps and Techniques

“Data is a precious thing and will last longer than the systems themselves.”

-Tim Berners Lee

 

Concept of Data Transformation

 

Data Transformation is a simple process in which data is converted from one format to another. Usually, data is in excel sheets, database files, XML format, or any other. In data transformation, we can convert data from any one format to another. 

 

This data is validated and cleansed. It is a very important step in the data management process.  It also includes other processes like- integration, migration, preparation, and data warehousing. The data transformation is also called ETL. 

 

It stands for extract, transform and load. Extraction involves extracting the data from multiple internal and external sources.

 

In the next step this raw data is cleaned. Once data is cleaned it is formatted so that it can be used in the operational setup and processes. It is also put in data warehouses, lakes and repositories so that it can be used for business and analytical purposes. 

 

Data transformation not only converts the data into other formats but it also helps in removing duplicates from the data. For organizations that rely on data, it is important to transform the data to get the desired results. Organizations must always look for ways to get the data in a cost-efficient way and use it effectively as well. 

 

Thus, data transformation is a part of harnessing the data. If done right, it will help in easily accessing, and storing the data. Safety concerns, consistency can also be dealt with.

 

There are so many reasons ‌organizations use data transformation. The number one reason is that organizations have tons of data because of digitization and thus they should know how to mine the data correctly and put it to use. 

 

Next reason is that the systems that are AI-enabled or using machine learning run with the help of data. Another reason is to check the accuracy. It means that organizations have to check if the systems are running effectively and accurately. This data has to be integrated with multiple other data sources which are in different formats.

 

Considering these 3 reasons we can clearly say data transformation plays the main role in the management and integration of data. Ensuring that data in one system is compatible with the data in another system is mandatory. Without compatibility, data will not run, and to make it compatible we need to transform it.

 

There are multiple challenges faced in data transformation that an organization faces like:
 

1. The cost involved in transformation is very high. Experts are required to transform the data and the fees they charge is also very high. Thus, high cost is a big challenge in data transformation.

 

2. It can reduce the speed of other operations. Transformation needs resources and the load it puts on other systems can lower their speed. The intensity of using the on-premise data transformational tools can hamper the speed of other ongoing operations.

 

3. The people required for data transformation must be trained and skilled. Hiring such experts can be a gruesome task. Moreover, the need for such trained experts has risen so much that every organization needs them. 

 

Retaining such experts is another challenge for companies. Thus, the retention of data experts is another challenge faced by organizations.

 

4. Another big challenge in data transformation is aligning the activities to the priority areas of the business.

 

Advantages of Data Transformation

 

Data Transformation offers two main benefits to every organization that is:

 

1. The data is transformed into a better and more organized version. After transformation, the data becomes more useful for humans as well as the computer. Heavy files are converted into a compact form that is easy to understand. The data in compact form can be analyzed quickly and yields better outcomes.

 

2. After transformation, the quality of data also increases. All the applications are protected from data-related issues like- null values, indexing issues, compatibility problems etc.

 

Also Read | Benefits of Blockchain in Data Transformation

 

Steps in Data Transformation

 

“We are moving slowly into an era where big data is the starting point, not the end.” – 

-Pearl Zhu

 

Even if a company deals with less data,  it has to transform it sometimes for some purpose.  We are surrounded by data that is stored in so many formats. We should be able to convert it from one format to another. 

 

Learning the basic steps in the data transformation process is a must for every person. The whole process can be brought down into 4 simple steps of digital transformation that are given below.

 

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Steps in Data Transformation

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1. Data Interpretation

 

The ‌foremost step is to interpret the data that you have. Understanding the ‌data you have is important to know what ‌data you need when you transform it. Your computer recognizes the file based on the extension you put in while saving it. For a video file video.avi or excel file .xls. The main issue with this is that the data that is inside your file might not be what you saved it as. 

 

It depends on the user which extension they put. Just by changing the extension of the file, you are not doing data transformation. It is a whole new concept and not that simple. 

 

So interpreting the data means using the tools that can dive deeper into your data and learn about its structure rather than looking at the name or extension of the file. You can use LINUX for interpreting the data. Once you have interpreted the data you have to decide the target format.
 

2. Data Quality Check

 

When you have interpreted the data and decided what format you want to transform it to. The next step comes into the picture. It is a data quality check. By doing a quality check you can identify any redundancies or corruptions or maybe some errors that are present in your data. 

 

If your source data is corrupt or wrong then your transformed result will be wrong and your final report will also be wrong. Ultimately all the next steps will be wrong. Therefore, a quality check is very important before translating the data.

 

3. Data Translation

 

Once the data quality check is done the next step is data translation. It means taking the existing data and fitting it into the new format or the targeted format that you want. 

 

For example- Imagine you are transforming an HTML file that was written using an outdated version of HTML into the latest version i.e. HTML5. Then in the data translation phase, all the formatting done as per old HTML version will be changed as per HTML5. 

 

Just like the <dir> tag used in old HTML with <ul> which is used in the latest version. So translation is about changing the individual datasets as well as restructuring the whole file to fit the targeted format. 

 

An example of complete restructuring can be converting a CSV file into an XML file. A CSV file has lots of commas and changing each one will require a lot of time. Therefore, completed restructuring is done in this case as per the XML format.

 

4. Post Data Quality Check

 

The final step in the data transformation process is the post-translation check. Now after the data is translated it is necessary to check if the formatted data is accurate and can be used maximally. 

 

To do that, you have to perform another data quality check. This check will also find out all the irregularities or errors or issues that were done in the translation process. Even though you did a quality check before translation, this check is equally important. 

 

Data before translation can be error free but that does not mean that the data after formatting is also clean.
 

Also Read | Data Re-Processing

 

 

Techniques of Data Transformation

 

“The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – is going to be a hugely important skill in the next decades.”

-Hal Varian

 

Given below are some ‌techniques of Data Transformation:

 

1. Smoothing

 

Smoothing as the name suggests involves cleaning the data. All the distorted data is removed during the data transformation. It means meaningless data is eliminated. It also detects if there is some pattern in data or some modifications.

 

2. Aggregation

 

Aggregation as the name implies means collecting data from multiple sources and then combining or aggregating it into one format. Putting it into one format will help in analysis and report generation. Organizations that deal with tons of data can use aggregation techniques to sort and analyze their data.

 

3. Discretization

 

In this technique, the data is divided into some intervals and labeled accordingly. It is done so that data can be analyzed easily. Analyzing fewer data can be done more efficiently. Decision trees are used to put huge volumes of data into compact forms.

 

4. Generalization

 

Low-level data attributes are converted into higher ones in generalization. It helps to get a clear snapshot of the data. This transformation from lower to higher is done using hierarchies etc.

 

5. Attribute Construction

 

In this new attributes are created from the existing data attributes. So, the attribute construction technique involves using the existing dataset to create new attributes.

 

Also Read | Data Augmentation

 

The organizations can structure the data that they have into any format they want. Now we see that businesses are shifting to the modern technology-based system. Data is present in such a huge volume that multiple softwares are being invented to reduce the data load. 

 

The budget of companies and their resources are also limited. Now with these limited resources how can the companies meet the standards as well as the rising competition? 

 

Companies are looking for the best tools available and data transformation is the solution. The future of this technique is so big that companies are heavily relying on it. They are shifting to clouds and ELT to make the data more interactive and useful.