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How the Data Transformation Process Works

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How the Data Transformation Process Works

A high-level overview of the data transformation process and why it is critical for enterprises working with incoming big data sets.

· Big Data Zone ·
Free Resource

As your business grows and evolves, so does the number of data formats and applications you must also support. Whether an enterprise is trying to onboard a new trading partner or ensure that it meets all the requirements a customer has, data is coming from many different places.

The last thing your enterprise wants is to be difficult do business with. You need to be able to communicate efficiently with the members of your digital ecosystem in order to expand and take on more customers. That’s why efficiency in the data transformation process is so valuable to an organization: companies that can handle data formats of any size, shape, or form are the ones that are going to thrive in the age of the cloud.

How the Data Transformation Process Works

So, what is data transformation exactly?

Data transformation converts data from one format, whether it’s a database file, an XML document, or something else, to another. The data transformation tools and techniques are critical because data can reside in many different locations and formats, and enterprises must have the ability to convert data depending on the unique needs of its business ecosystem. The end goal of data transformation ensures data is readable when it moves from one application or database to another.

Occasionally and also important to note, it is possible that some data needs to be cleansed before it is actually transformed. Data cleansing takes the data and prepares it for transformation because it removes any inconsistencies, errors, or missing values. From there, the data is ready to be transformed.

Steps in the Data Transformation Process

Through the data transformation process, a number of steps must be taken in order for the data to be converted, made readable between different applications, and modified into the desired file format.

Step 1 - Data Discovery

The first step in the data transformation process begins when you identify and truly understand the data within its source format. Data profiling tools do this, which allows an organization to determine what it needs from the data in order to convert it into the desired format.

Step 2 – Data Mapping

The data mapping phase of the data transformation process lays out an action plan for the data. Data mapping is often the most expensive and time-consuming portion of an integration strategy because it encompasses data validation, translation, value derivation, enrichment aggregation, and routing.

Step 3 – Code Generation

When data must be converted, a code must first be created that actually runs the data transformation “job.” Centralized integration platforms are able to generate the code to simplify the task for enterprises.

Step 4 – Code Execution

Once the code has been created and the data transformation process is fully planned, it’s time to execute the code. The code is put into motion and converts the data to your desired output.

Benefits of Using Data Transformation Software

Data transformation tools and techniques have become such valuable resources for today’s enterprises that the question becomes where can you find the technology to handle all of this data? A centralized integration platform that provides any-to-any data transformation tools and data mapping solutions with an engine to fully automate the connection, transformation, and integration of business-critical data exchanges would be ideal.

Look for solutions that:

  • Support end-to-end processes by creating one-to-many ecosystem data exchanges between any internal system, cloud, and trading partner application utilizing EDI, XML, or APIs
  • Drive data efficiency and eliminate integration process bottlenecks by consolidating integrations to a single, easy-to-use platform
  • Automate data mapping and reduce the time and cost of building and maintaining data transformation software and mapping processes
Topics:
data transformation ,big data ,data cleansing ,data discovery ,data mapping

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