Even if data transformation is not the primary process or part of the routine for your company, understanding the key steps involved in the data transformation process is crucial, especially as businesses nowadays are collecting more data than ever before.  
 
As data gets collected, stored and analyzed in several formats and in massive data sets, having the power to perform the basic steps of data transformation from one format and form to the other is common and required for most businesses, as they need to take advantage of this never-ending amount of information. 

Working with raw data can be a challenging task for your business and the data users inside your company, so taking advantage of data transformation for the ever-increasing volume of data will give you limitless insights to make better decisions and help on growth and revenue. That’s why we’ll explain below the four crucial steps for data transformation to help you understand better not only your business but also the customers, competitors and the market in general. 
 

What Is Data Transformation?

Data transformation is simply the process of performing a conversion of data from one format to another. The changes are usually performed at the source on the format, structure, or values in order to make the data match the destination system. For most businesses, data transformation gets completed as a component of data integration and data management tasks.
 
For organizations using on-premises data warehouses, data transformation is a step inside the ETL (extract, transform, load) process, and its complexity will depend on what kind of changes the company will perform before reaching its target system and which tools are involved in the process, but the transformation gets completed on the source before being loaded to the target.
 
But for companies that have already adopted a cloud-based data warehouse, the data transformation process can be fully automated and with a scale of compute and storage resources that can happen in seconds. The main benefit of cloud platforms is the possibility of skyping the preload information step, and allowing the users to load the raw data warehouse and then go for the ELT (extract, load, transform).
 
Data transformation is a powerful process to help increase business efficiency and make the analytic process better, enabling organizations to make better data-driven decisions. The structure of the data will be defined during the process, so data transformation can be:

• Constructive: in which the data transformation will add, copy and replicate data;
• Destructive: the process will delete fields and records from the system;
• Aesthetic: all data will be standardized during the transformation to match the requirements and parameters of the target system;
• Structural: the data will get reorganized by a process that can rename, move and combine columns. 

There are several ETL tools available that can fully automate the process of data transformation, but few offer the power and flexibility of EmissaryTM, with its easy-to-use, intuitive interface.  EmissaryTM is designed to be used by anyone, from business users to technical users and developers.  It makes transforming data easy, even when dealing with custom, previously unknown data formats.
 
As the data streams between different sources, ensuring compatibility is the only way to make it all work seamlessly. And that’s where the data transformation process should step in: by giving companies the power to organize and convert their data from any source into the format that will allow them to integrate, store and analyze the data.
 

Explaining Data Transformation in Four Steps

The process will vary according to the situation and the company’s needs, but the steps we’ll list below are the most common and will mostly be present when you perform a data transformation process.
 

Step 1: Data Interpretation 

The first step in the data transformation process is to interpret your data in order to identify the type of data being handled and determine what it needs to be transformed into. 
 
Data interpretation is crucial, and although it sounds easier, can become harder than it looks as most operating systems make assumptions about the data format, based on the source application and the extension on the file name – for example, a text doc being automatically transformed to be open as a Microsoft Word file. 
 
But those “labels” that can be automatically applied could be different from what you need and from what the name suggests. The data users can even add manually which extensions they want to use, but in most cases, changing the name doesn’t transform the data.
 
That’s why being accurate when interpreting the data requires tools and deep knowledge of how the data is structured and what is inside the database, instead of just making the assumption based on the file name.
 
It’s also key to determine the target format before this process even begins. But if the user is not sure what format will work for the target system, the best process is to read the dataset and then understand the tool that will receive the transformed data, to see which formats it supports.
 

Step 2: Data Quality Check

Once the right format for the data was figured out, it’s time to run a quick data quality check to identify problems, missing values, and corrupted information within the database or directly in the source, which could lead to bigger issues further down on the transformation process. 
 
Some companies add the quality check automatically right after the data is interpreted. This way, both processes can happen almost simultaneously and won’t take much time inside the data flow.
 

Step 3: Data Translation

After ensuring that the data quality of the source data is according to your standards, it’s time to start translating the data. Data translation is the process of taking each part of the source data and replacing it with the formatted data – or the data that fits the target system.
 
Data translation is not only the act of replacing each individual piece of data with new pieces but restructuring the entire file according to the needed format.
 

Step 4: Data Quality Check After The Translation

Performing a data quality check after the translation process will ensure that the data will be 100% useful and that it was rightly translated. This quality check will look for inconsistencies, missing information, broken values or any other errors that may have appeared.
 
Even if the data was already error-free before the data translation process, problems can be introduced during it, so it’s crucial to run a quality check again.
 

Benefits of Data Transformation 

Data has the potential to transform your business, improve efficiency, generate insights that will lead to growth and revenue, and so on. Whether it’s data about customers, supply chain, competitors or any other indicator that your company wishes to track, using a data transformation process will help to achieve all the goals, and the benefits can include:

• You will extract the maximum value from the data: in order to use business intelligence to analyze the information and generate insights, the data needs to be standardized to improve accessibility and usability. 
• Data will be managed with more efficiency: as data comes from several sources, and the amount of sources never stops growing, eliminating the inconsistencies will make the challenge of organizing and understanding the data much easier. Data transformation gives you the power to understand what’s in your data set. 
• Make the information easier to find: when the data is transformed, standardized and stored in the right locations, it can be easily found and retrieved. 
• Ensuring data quality: data quality is a major concern for most companies, as the risks and costs of making the wrong decisions without the right data to support them can be a dealbreaker. Data transformation will ensure data quality during the process and will reduce the issues of inconsistencies, missing values or errors.
• Increasing the compatibility: as companies need to transfer the data between several systems and tools, they will require to transform the data in different ways, sometimes more than once. Data transformation gives the possibility of doing so while keeping the data integrity and quality.
• Making the data useful: businesses collect tons of data every day and let it sit around without any purpose. Data transformation will standardize the data, and transform it into something that can be analyzed and used.