What is data transformation?

The transformation process generally follows 6 stages:

1. Data discovery — In the first stage, data teams must identify and understand applicable raw data. It also helps analysts/engineers know the transformations to be done when data is sampled or profiled.
2. Data mapping — In this step, analysts establish how many and which fields are modified, matched, filtered, joined, and aggregated.
3. Data extraction — In this phase, data is moved from a source system to a target system. Extraction may include structured data (databases) or unstructured data (event streaming, log files) sources.
4. Code generation and execution — After the extraction and loading process is complete, the data must be transformed to make it suitable for BI and analytic use. Analytics engineers often do this by writing SQL or Python to transform data programmatically. This code is then executed daily or hourly to ensure the analytic data is up-to-date and relevant.
5. Review — After the implementation, the code has to be reviewed and verified to ensure that the right and appropriate implementation has been made.
6. Sending — The last step is to send data to its target destination.

An analyst or engineer will identify the data structure in the data transformation process. The most typical kinds of data transformation are:

• Constructive — The data transformation process involves inserting, duplicating, or moving data around.
• Destructive — The system deletes fields or records.
• Aesthetic — The transformation aligns the data to specific standards or parameters.
• Structural — The database is restructured through column rename, movement, or combination.

Furthermore, there is data mapping, which is saved using the right database technology.

Data processing involves gathering and organizing data to be readily available for analysis, error checking, and data formatting. Data transformation, on the other hand, consists in changing the form of data to make it more meaningful or suitable for analysis.

In other words, processing makes data fit for use, while transformation makes helpful data.

Data transformation tools are software solutions designed to undo, fix, order, and improve raw data to be used for analysis, reporting, and business intelligence. They access data from many sources, perform transformations such as selecting, calculating, or normalizing, and store the transformed data in databases, data warehouses, or visualization tools.

Key functions of data transformation tools:

• Data cleaning — To remove duplicates, fix errors, and standardize formatting.
• Data integration — To combine data from many sources, including databases, Application Programming Interfaces (APIs), and cloud storage.
• Data mapping — To match the data elements from different data sources to a single data model.
• Aggregation and Summarization — To facilitate analysis by presenting metrics calculations in a consolidated form.
• Data enrichment — Adds more information from other sources to the datasets.

Let's say you have a file in the audio format and want to use it in the text analysis tool. Although opening the audio file in a text editor (for example, using cat myfile.wav in the Linux terminal) is possible, it will lead to incomprehensible garbage. The audio needs to be converted to text to make the content meaningful.

This can be done by listening and writing down or using speech recognition tools. It has to be so because automation is vital on a large scale. It enables organizations to manage and analyze vast amounts of voice data, which can be indexed, searched, and prepared for further analysis by machine learning algorithms, text analysis, or customer journey analysis.

This example demonstrates that data transformation is about formatting structured data and making unstructured data ready for use in other platforms. Organizations that deal with various types of information (audio, video, images, and text) encounter the problem of integrating traditional information systems with new-generation AI-based analysis platforms, where data conversion is a crucial step of digital transformation.