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Data transformation is the process of converting data into a different format that is more useful to an organization. It is used to standardize data between data sets, or to make data more useful for analysis and machine learning. The most common data transformations involve converting raw data into a clean and usable form, changing data types, removing duplicate data, and enriching data.
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 benefit an organization. During the process of data transformation, an 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. Organizations may transform data to make it compatible with other types of data, move it into the appropriate database, or combine it with other crucial information. Organizations 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
- Data is easier to digest and manage: 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.
- Expensive process : cost of licensing, resources and hiring.
- Resource intensive: Can slow down other operations
- Needs expertise
- Businesses can perform unnecessary data transformation
Data Transformation Process
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 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.
A Look at 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 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.