Discovering Data Transformation

different types, techniques, benefits and challenges

What is data transformation?

It is the process of converting, cleansing and structuring data into a usable format that can be analyzed to support decision making and promote growth of an organization.

Data transformation is used when data needs to be converted to match that of the target system. Organizations today primarily use cloud-based data warehouses because they can scale their computing and storage resources in seconds.

Cloud-based organizations, with this massive scalability available, can skip the ETL process. Instead, they use a transformation process that converts data as the raw data is loaded, a process called extract, load, and transform.

The data transformation process can be handled manually, automated, or a combination of both.

The data transformation process can be:

  1. Constructive: where data is added, copied, or replicated;
  2. Destructive: where records and fields are deleted;
  3. Aesthetic: in which certain values are standardized,
  4. Structural: which includes the columns that are renamed, moved and combined.

At a basic level, the data transformation process converts raw data into a usable format by removing duplicates, converting data types, and enriching the dataset.

This data transformation process involves defining the structure, mapping, extracting the data from the source system, performing the transformations, and then storing these transforms in the appropriate dataset.

The data then becomes accessible, secure and more usable, allowing it to be used in a multitude of ways. Organizations perform data transformation to ensure compatibility with other types by combining it with other information. Through data transformations, organizations can gain valuable insights into operational and information functions.

Given the massive amounts of data from disparate sources that businesses are confronted with on a daily basis, data transformation has become an essential tool. Facilitates the conversion of data, regardless of its format, to integrate, store, analyze and extract for business intelligence.

How is data transformation used?

Data transformation works on the simple goal of extracting data from a source, converting it into a usable format, and then delivering the converted data to the target system.

The extract phase requires data to be pushed into a central repository from various sources in its original raw, unusable form. To ensure the usability of the extracted data, it must be transformed into the desired format by performing a series of steps.

The data transformation process takes place in five phases.

  1. Discovery: The first step is to identify and understand the data in its original source format with the help of data profiling tools.
  2. Mapping: The transformation is planned during the data mapping phase. This includes determining the current structure and the resulting transformation required.
  3. Code generation: The code, necessary to carry out the transformation process, is created in this phase using a data transformation platform or tool.
  4. Execution: The data is finally converted into the selected format with the help of the code. Data is extracted from source(s), which may vary. Subsequently, transformations are performed on the data. Once transformed, these are sent to the destination system which could be a data set or a data warehouse.
  5. Review: The transformed data is evaluated to ensure that the conversion had the desired results in terms of format. Importantly, not all data needs transformation. Sometimes they can be used as is.

Data transformation techniques

There are several data transformation techniques used to cleanse and structure data before it is stored in a data warehouse or analyzed for business intelligence.

Nine of the most common techniques are:

  1. Revision. Ensures that the data supports the intended use, organizing it in the required and correct way.
  2. Handling. This involves creating new values from existing ones or changing the current data through calculation. Manipulation is also used to convert unstructured data into structured data that can be used by machine learning algorithms.
  3. Splitting. Splitting involves splitting a single column with several values into separate columns with each of those values.
  4. Combination/Integration. Records from tables and sources are combined to gain a more holistic view of an organization’s activities and functions. Pair data from multiple tables and datasets, and combine records from multiple tables.
  5. Data smoothing. This process removes meaningless, noisy, or distorted data from the dataset. By removing the outliers, trends are more easily identified.
  6. Data aggregation. This technique collects raw data from multiple sources and turns it into a summary form that can be used for analysis.
  7. Discretization. With the help of this technique, range labels are created in continuous data in an attempt to improve its efficiency and facilitate analysis.
  8. Generalization. Low-level data attributes are transformed into high-level attributes using the concept of hierarchies and creating successive levels of summary data.
  9. Attribute construction. With this technique, a new set of attributes is created from an existing set to facilitate the mining process.

Why do companies need data transformation?

Organizations use data transformation to convert data into formats that can be used for different processes.


There are several reasons why organizations should transform their data. The transformation makes disparate datasets compatible with each other, which makes it easy to aggregate data for in-depth analysis.


Transformation helps consolidate data, structured and unstructured. The transformation process also allows an enrichment which improves their quality.

What are the advantages?

Data has the potential to directly impact an organization’s efficiency and bottom line.

It plays a crucial role in understanding customer behavior, internal processes and industry trends. While every organization has the capacity to collect an immense amount of data, the challenge is to ensure that it is actionable.

Data transformation processes enable organizations to reap the benefits of data.


Data usage

If the data collected is not in an appropriate format, it often ends up not being used at all. With the help of data transformation tools, organizations can finally realize the true potential of the data they have accumulated as the transformation process standardizes it and improves its usability and accessibility.


Data consistency

Data is continually gathered from a variety of sources which increases inconsistencies in the metadata. This makes organizing and understanding data a huge challenge.


Better data quality

The transformation process also improves the quality of the data which can then be used to acquire business intelligence.


Compatibility between platforms

Data transformation also supports compatibility across data types, applications, and systems.

Faster data access

It is easier and faster to recover data that has been transformed into a standardized format.


More accurate insights and forecasts

The transformation process generates data models which are then converted into metrics, dashboards and reports that enable organizations to achieve specific goals. Key performance metrics and indicators help companies quantify their efforts and analyze their progress.

What challenges does it meet?

High implementation cost

The data transformation process is expensive. Depending on the infrastructure, software and tools used, the cost of the solution varies and tends to be higher considering the extra resources that have to be hired, the IT resources and the license of the tools used.


Intensive resource

The transformation process requires a lot of resources. When you perform transformations in an on-premises data warehouse, you create a huge computational burden, which consequently slows down other operations. However, this is not a problem when using a cloud-based data warehouse as the platform is able to scale easily.

Data transformation also requires the expertise of data scientists, which can be costly and distract attention from other tasks.


Errors and inconsistencies

Without proper expertise, many problems can arise during the transformation that could hinder the final results. Whether it’s a poor transformation that results in bad data or a migration that fails and damages data, there are risks.

Data transformation helps organize data and make it meaningful, which improves its overall quality.


This cross-system compatibility provides strong support for features such as analytics and machine learning. Given the large volume of data generated by new applications and emerging technologies, organizations are relying on data transformation processes to manage data more efficiently and effectively.


Data transformation not only helps organizations get the most value out of their data, it also ensures that it can be managed in simpler ways without feeling overwhelmed by the sheer amount of it all.