Data Transformation Steps, Techniques & Tools

Data flows into an organization from many sources and in many formats, and the same can be said about the way data is stored. But, when you’re ready to use and apply data, it has to be structured properly to work together. 

Data transformation steps help position data to be usable. We’re going to look at how data transformation tools can be used to streamline the process, ensure accuracy, and save time.

Coming Up

What is Data Transformation?

What are the Steps in Data Transformation?

What are Data Transformation Techniques?

What are the Benefits of Data Transformation?

What are the Challenges of Data Transformation?

What are Data Transformation Tools?

Closing Words

What is Data Transformation?

Data transformation refers to the operations by which source data is formatted and structured to then fit into target systems and processes. 

In order to move data successfully between systems, data transformation typically involves ETL (extract, transform, load) pipelines to process raw data. 

In some instances, data scientists and analysts manually manage data transformations to model data. However, with a large volume of data and growing business uses, many companies utilize data automation software to create data warehouses and data lakes. 

When the data is stored in data warehouses and data lakes, it is readily accessible to be called upon to analyze to gain insights. 

Automation software connects your data systems, automates data cleansing and data transformation, and can create customizable dashboards and reports for key stakeholders and business leaders.

What are the Steps in Data Transformation?

To reap insights from data, it must be transformed from its raw starting point into usable information. 

Data transformation steps look like this: 

1. Data Discovery

You begin by identifying and understanding the source format of your data. A data profiling tool is helpful at this stage. Or, if you’re using an ELT workflow, then the data will be extracted from its original sources and loaded into the target data warehouse. 

2. Data Mapping

Once in the data warehouse, it’s time for data exploration, or data mapping. During this phase, you get to see how the data looks and identify if any information is missing. Data mapping sets the action plan for the data and can end up being time-consuming without the help of automation software. 

3. Data Transformation

During the data transformation stage, the main work takes place. At this step, you’re aware of how the data is structured as well as how it needs to be structured. 

This step consists of two main efforts, namely:

• Generating Code: At this step, the code generation is performed so that you can transform data into the required format. 
• Executing Code: Once the code is ready,  the work really begins. This is the process of editing the format from the source system into the right format for the target source. 

Transformation can be either light or heavy. Light transformation includes renaming tables and fields, casting fields correctly, and creating uniformity. Heavy transformation involves adding business logic, data aggregation, and the like. 

4. Review: Data Testing

Once data is modeled and ready to go, you can test it out in action by ensuring column values fall into the expected range, checking model relations line up, etc. 

5. Data Documentation

When the testing has proven the data to be in good standing, you can expose results to end users. Making data transformations usable and impactful requires documentation. 

Documentation covers and outlines the purpose of the data model and transformation in the first place, as well as defines key metrics and business logic that has been applied. 

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Data is like a living and breathing organism–more of it comes into your business every day and these processes have to be applied on a recurring basis to make use of data. 

Data automation software standardizes and automates the process so that data can continuously be used to drive value. Along with streamlining the data transformation process, data automation software connects your data systems, centralizes information, and maintains accuracy at every step of the way. 

What are Data Transformation Techniques?

Before data moves into a data warehouse for business intelligence and analysis, there are different data transformation techniques that can be used. Some of the most common techniques include: 

1. Manipulation

Data manipulation creates new values from existing data by way of computation. It can also apply machine learning algorithms to change unstructured data into structured data. 

2. Revising

Data revision cleans up data by removing redundancies, ensuring formatting capabilities, and validating records, for example.

3. Aggregation

Data aggregation pulls raw data from various sources and creates a summary form that can then be used for analysis. To exemplify, data aggregation in practice looks like deriving an average or sum of a dataset through the use of statistics. 

4. Combining

When you integrate records from various tables and datasets to get a cohesive view of it all, that’s an example of data combining. 

5. Separating

On the other side of combining sits data separating, which divides data values into parts for granular analysis.

What are the Benefits of Data Transformation?

Having a vast amount of data doesn’t help any organization unless it can run through the necessary data transformation steps. Once processed, data can be used to understand industry trends, customer behavior, and even to optimize internal workflows. 

Data transformation provides several benefits, including improved: 

1. Data Quality

Since data is cleansed and given the necessary attention during the data transformation steps, the quality and integrity is protected. 

With the help of automation software, you can speed up the process by 85x and reduce errors by 90%. Since your systems are connected, you have more time to focus on high value-add tasks, rather than manual data entry and transformation responsibilities. 

2. Data Consistency

Organizational understanding of data improves given that the data is organized into sets and made consistent for its application. 

3. Data Utilization

One of the most important outcomes of data transformation is enabling the ability to use data. Data transformation steps allow for data’s potential to be reached as it is made accessible. 

4. Data Access

Once data is prepared into a standardized format, it becomes easier to retrieve and access. This is especially true with the use of data automation software that securely stores data in a centralized location with cloud-based capabilities.

What are the Challenges of Data Transformation?

Effectively transforming data requires adequate attention, planning, and foresight.

Some organizations may face these challenges: 

1. Cost

The cost of data transformation depends on the tools being used. Software licensing and the cost of highly-trained individuals can quickly add up. Alternatively, organizations can make use of customizable and cloud-based solutions that are easy to use and cost effective. 

2. Expertise

When people without proper training are tasked with data transformation, there is a large chance of errors occurring. They may not be able to notice typos or incorrect data if they don’t know what is considered to be a permissible range of values. 

3. Suitability

Businesses may use different tools that require different data formats and require data to be changed back and forth between systems. 

What are Data Transformation Tools?

There are many different types of tools that exist to make data transformation steps easy to accomplish. Such tools are:

1. Custom ETL Tools

ETL tools are designed using programming languages, which can be labor intensive and expensive to build, test, and maintain. This approach will require detailed documentation and a skilled team to develop. 

2. Cloud-Based Platforms

Cloud-based tools operate under a software-as-a-service model which makes them affordable, secure, and accessible. 

Cloud-based solutions also allow organizations to scale their data transformation needs as their business grows. They are also easy to use and offer great customer support. 

3. Open-Source

Open-source tools are free to use and allow teams to develop, build, and maintain their own ETL process. That being said, they require coding knowledge and individuals who understand the application to be utilized. 

Closing Words

As you can see, completing data transformation steps with the aid of data transformation tools takes a lot of the pain out of the process. You get to save time, reduce errors, operate efficiently, and be able to trust the quality of your data with full confidence. 

Automation software is made for everyone–it’s intuitive, cost effective, and ready to work for you. With a robust solution, you can automate the data transformation steps, as well as many other critical finance functions.