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What is Data Transformation & How Does it Work?

January 8th, 2024

Data is a valuable resource that helps your business make better decisions and gain a competitive edge. But how can you make customers, rivals, and business information easily accessible to everyone in your organization? The solution is data transformation.

What is Data Transformation?

Data transformation is the process of converting, structuring, and cleansing raw data from one format to another to make it ready for analytics and consumption. It is a critical part of the data integration process and consists of multiple steps, such as filtering your data based on specific rules and joining different fields to get a consolidated view of all your data.

The ETL (Extract, Transform, Load) process extracts raw data from a source, converts it into a usable format, and sends it to a target system. Raw data is unusable in its original form, so you must transform it to use it.

Most ETL tools also provide predefined functions that you can use to change your data quickly and efficiently. Here are some of the steps that are involved in this process:

  • Identify the structure of the source files and extract data from them.
  • Next, map the data from the source file to the transformation tool.
  • Apply the transformation (i.e., filter, sort, cleanse, or aggregate data).
  • Finally, send the transformed file to the destination.

Benefits of Data Transformation

Data serves various purposes for organizations and businesses, from analyzing supply chains, customer behaviors, and internal processes to creating automated data-driven workflows. However, for these purposes, it must be in a usable format, and data transformation ensures that data achieves this usability, resulting in the following benefits:

  • Gaining maximum value from data: Most organizations are unable to gain maximum value from data due to factors such as volume, complexity, quality, and lack of tools and skills. Forrester reports that organizations never analyze up to 73% of all data. Data transformation is a process that can help them overcome these challenges by changing the structure and format of raw data to make it more suitable for analysis. This improves data quality and facilitates analysis, enabling them to leverage more effectively in decision making.
  • Standardizing data: Data coming from multiple sources may be inconsistent, making it difficult to use in analytics. Data transformation helps you standardize data.
  • Improved accuracy and compliance: Transformed data undergoes cleansing, validation, and enrichment, improving its consistency and reliability. The improved data quality makes it easier to ensure regulatory compliance.
  • Performing faster queries: Transforming data allows you to store it in a standardized format. Standard formats enable faster queries, making it easier to retrieve data quickly.
  • Enhancing data quality: Transforming data can help you eliminate data quality issues such as missing, inconsistent, duplicate, or invalid values. As a result, you can avoid costs and risk associated with poor data quality.

How Data Transformation Works

The goal of data transformation is straightforward – extract data from the source, change it to a usable format and load it to a destination. It is an essential part of ETL pipelines.

Data transformation allows you to clean, reshape, and compute new data dimensions and metrics based on your needs. Traditionally, it takes anywhere from hundreds to thousands of lines of code to build ETL or ELT pipelines. However, with a no-code ETL tool, you can easily build these pipelines on a visual canvas, known as a dataflow.

  1. Identify sources: First, you must identify data sources you must transform. Those sources can be databases, Excel, delimited files, legacy sources, or even cloud connectors. Some data extraction tools also allow you to extract data from unstructured files such as PDFs.
  2. Data mapping: In this phase, the actual transformation is planned. You must decide where the data is sourced and where it will be saved. The goal is to change the source data to a format suitable to the destination. For instance, data from source files in XML or Excel format needs to be transformed into SQL format. To make the format suitable for analytics, you may have to perform several operations, such as adding, deleting, copying, and replicating fields and records.
  3. Choose Transformation: Here, you choose the relevant transformation that fits with your planned data mapping and put the data transformation process into motion, converting the data into the desired output. Here are a few transformations you can leverage:
    • Filtering is the process of selecting a subset of data that meets certain criteria or conditions. For example, only loading records from a certain period, such as after January 1st, 2010, is a filtering operation that excludes data from before that date.
    • Enriching is the process of adding new information or value to existing data. For example, creating a new column titled Body Mass Index (BMI) by calculating values from the age, height, and weight columns is an enriching operation that adds a new measure of health to the data.
    • Splitting is the process of breaking down a complex or composite data element into simpler or more atomic parts. For example, dividing the Full Name column into separate columns for First Name, Middle Name, and Last Name is a splitting operation that separates the name components.
    • Joining is the process of combining data from different sources or tables based on a common attribute or key. For example, combining data from HR and Finance to generate employee payroll reports is a joining operation that merges data from two departments using the employee ID as the key.
    • Removing duplicate data is the process of eliminating redundant or repeated data elements from a data set. For example, removing duplicate records of the same order from the Orders table is a data operation that ensures data quality and accuracy.
    • Review: Once the transformations are applied, you must check the transformed data to ensure it has been formatted correctly.

Besides these steps, you may need to perform other customized operations to tailor the data to your desired format. Once the data has been transformed, you can load it into the target destination to put it to work. Remember that not all data require transformation—sometimes, the source data is suitable for immediate use.

Data Transformation Types

The data transformation capability is available in various data integration tools. A good thing about this process is that you can do it in several ways. Each approach comes with its unique benefits and limitations. Let’s look at some of the standard transformation techniques.

On-Premises Data Transformation

On-premises data transformation is the process of using a data management tool or application installed and hosted on the organization’s servers or infrastructure to perform data transformation tasks. It helps with increased regulatory compliance and better security management. Most tools come with drag-and-drop functionality, which means that non-technical employees can transform data and convert it into a usable format.

Cloud-Based Data Transformation

Cloud data transformation is the process of using a software tool or service that is hosted and accessed on the internet or a cloud platform to perform data transformation tasks. The pay-as-you-go feature of most cloud-based tools allows businesses to scale up and down as and when necessary, which is one of the reasons why cloud-based tools are popular.

While saving crucial enterprise data on a third-party cloud-based server has its share of security concerns, one benefit of transforming data on the cloud is short installation and configuration times. This means businesses can make changes to their data without experiencing significant delays.

Manual Data Transformation

Manual data transformation is the process of manually writing code or scripts to manipulate and transform data from one format or structure to another. This process requires a high level of technical expertise and can be time-consuming, error-prone, and costly, especially when dealing with multiple file formats.

This requires a professional on the team dedicated to this task, increasing the risk of human error and inaccuracy.

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Data Transformation Techniques

Transformation techniques are different from data transformation types. There are multiple ways to transform your data, but techniques vary according to your goals and needs. The following are some of the most used data transformation techniques:

1) Data Smoothing

Data smoothing is used to remove meaningless or distorted data (also called noise) from a dataset. Smoothing algorithms help you highlight random fluctuations and outliers, making it easier to remove unnecessary information.

For instance, data smoothing is crucial in helping traders and investors identify patterns and trends in stock prices. It helps remove noise and helps them plan better to accommodate  seasonality and volatility of stock prices. Traders leverage data smoothing techniques like the moving average to calculate the average price of a stock over a specific duration.

Learn how to perform data smoothing in time series analysis.

2) Data Aggregation

Data aggregation is one of the most crucial data transformation techniques. It’s the process of standardizing data gathered from multiple sources by storing it in a desired format. It makes data easy to retrieve and present.

Aggregated data can help you in several ways. For instance, you can aggregate data from your website, email campaigns, CRM system, surveys, and social media to more accurately analyze customer demographics, behavior metrics, and preferences.

3) Discretization

Discretization (also called binning) is another useful technique for data transformation. It helps transform continuous data into a series of data intervals and replaces continuous attribute values with interval labels, making data easier to analyze.

For instance, patient age in healthcare datasets is stored numerically, but transforming these numerical values into intervals (e.g., patients aged 18 to 25) makes it easier to study patient dynamics.

4) Generalization

Generalization is a data transformation technique that allows you to convert lower-level data into higher-level data based on a predefined taxonomy or hierarchy. For instance, you can use this technique to replace cities in a dataset with their country or continent. Likewise, timestamp values such as 2023-08-17 15:26:43 can be replaced with August 2023, 2023, or 2020s.

5) Construction of Attributes

This technique can help you generate new attributes based on existing attributes. For instance, if you have a dataset logging the date and time of transactions, it can help you create new attributes such as day of week, month, season, hour, etc. These new attributes can then help identify trends or patterns, such as seasonal effects, peak hours, and more.

6) Scaling

Scaling in data transformation is a technique that transforms the values of a dataset into a common scale. One example of scaling is min-max, which scales the data to a range between 0 and 1. Scaling helps preserve the relative order and distance of the original values. There are other types of scaling techniques, such as z-score, log, and clipping.

7) Normalization

Normalization (pivot) and denormalization (unpivot) are two techniques that transform the data from rows to columns or vice versa. They help you optimize, enhance, and protect your data for different purposes. Normalization (pivot) reduces the data size and complexity by grouping it into categories or dimensions. Denormalization (unpivot) increases the data speed and richness by splitting it by attributes or values.

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Astera: The Easy Way to Transform Data

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Astera is a complete data integration platform that allows users to extract, map, transform, and load their data in a code-free environment. In addition, it also includes functionality for other types of transformations, such as data profiling capabilities, allowing users to get a holistic view of their data. You can also profile data to compare its pre- and post-transformed statistical overview.

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