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What Is Data Transformation?
Data transformation is the process of converting data from one format to another. The most common data transformations are converting raw data into a clean and usable form, converting data types, removing duplicate data, and enriching the data to enhance business intelligence. During the process of data transformation, a data analyst will determine the structure, perform data mapping, extract the data from the original source, execute the transformation, and finally store the data in an appropriate database.
Transformed data is usable, accessible, and secure to benefit a variety of purposes. Data analysts and data scientists use data transformation when preparing for data integrations and analysis. This process makes data compatible with other types of data, moves it into the appropriate database, or combines it with other crucial information. Analysts benefit from transforming data by gaining insights into vital operational and informational internal and external functions. In addition, data transformation makes it possible for organizations to transform data from a storage database to the cloud to keep information moving.
Benefits and Challenges of Data Transformation
Benefits:
- Easier digestion and data management: Refined metadata
- Improved data quality and protection
- Compatibility between applications and types of data
- Maximum value from data: standardize data to improve accessibility and usability
Challenges:
- Expensive process : cost of licensing, resources and hiring.
- Resource intensive: Can slow down other operations
- Needs expertise to ensure data quality
- Businesses can perform unnecessary data transformation
Data Transformation Process
By understanding and leveraging the data transformation process, organizations can unlock the full potential of their data, driving better decision-making and operational efficiency. Here are key steps:
- Data discovery: The first step involves identifying and understanding the data in its source format. This helps establish what the desired data format is and how to achieve it.
- Data mapping: In this phase, the actual data transformation process is planned.
- Generating Code: A code is created to run the actual transformation process. These codes are often generated with a data transformation tool.
- Executing the code: The panned data transformation process is put into motion using the generated code. The data is converted to its desired format.
- Review: This is the process of checking if the transformed data has been correctly formatted.
The Role of Data Transformation in ETL
In the ETL process, data transformation serves as the key step where data is modified or reshaped to fit the needs of the target system. Here’s how it fits within the overall ETL framework:
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Extract: Data is pulled from multiple source systems, such as databases, APIs, or flat files.
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Transform: This stage focuses on converting the extracted data into the required format. Data is cleaned, enriched, aggregated, or standardized, depending on the business requirements.
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Load: Finally, the transformed data is loaded into a data warehouse, database, or other target systems for further analysis.
The transform phase in ETL is vital because it ensures that data from various sources, which may have different formats or structures, is converted into a uniform format that can be easily analyzed and used across the organization.
Data Transformation Tools
This data transformation process of converting sets of data values from a source format to a format consistent for a destination data system often requires tools. Data element to element mapping can be complicated and requires complex transformations that require lots of rules, which is why successful data analysts use these tools to help simplify the process. This on-going process of shaping, standardizing and enriching data to conform to the right analytic outputs, has long been considered tedious, time-consuming, “janitorial” work. Worse yet, when it comes to complex or large volumes of data, the work is relegated to the small number of valuable resources with advanced data science skills, regardless of whether they have the business context or not. In short, the data transformation process has historically been fraught with roadblocks and frustrations, often consuming way more time than the actual analysis. Until recently there haven’t been a lot of data transformation tools available to help solve the challenges of IT organizations.
Predictive Data Transformation
At Alteryx our goal is to radically accelerate the process of transforming data and reduce the time it takes to analyze information and get the most out of your data. We are focused on fundamentally changing the experience of transforming data and providing delightful experiences with data. This means more than transforming data. It means creating shareable, reusable processes to help technical and non-technical users get to know the shape and structure of their data. When done well, this process lays the foundation for successful and repeatable analyzes.
Alteryx Designer Cloud Data Transformation Tools
To extend transformation capabilities to non-technical business users, the Alteryx Designer Cloud data wrangling experience includes predictive data transformation. Users can click, drag or select over the specifics of the data they would like to transform and, with every interaction, Designer Cloud generates a ranked list of suggested transformations for the user to evaluate or edit. This iterative feedback loop is always occurring throughout the use of Designer Cloud, constantly taking inputs from the data and the user to intelligently recommend new options.
A New Look at Data Transformation
As a key player in modern data transformation tools, Designer Cloud’s predictive data transformation allows analysts to work more intelligently with their data without having to learn new skills. By using Designer Cloud the transforming of data is not only easier, but faster and more fun, too.
Try a new way to transform your data, try out Designer Cloud today.