Today, businesses have the privilege of having limitless opportunities to arrive at better decisions and achieve greater results, thanks to the continuously increasing volume of data.
Now, it is up to an organization to make sense of the information they have about their customers, competitors, and their own business, and turn them into pieces of information that are accessible to all members of the enterprise. This is where data transformation comes in.
What is Data Transformation?
To put it simply, data transformation is the process of applying changes to data to make it more valuable and sensible to you. These changes may include aggregating, merging, filtering, splitting, enriching, summarizing, joining, or weeding out duplicate data.
The goal of data transformation is to convert the data from its format from the source system to the format of the system where it is going. It is also a crucial part of processes such as data integration, data conversion, and other data management tasks. It’s a key part of these processes as it aids in making data standardized and consistent between different datasets.
How Does Data Transformation Work?
Depending on the situation, the exact nature of the process of data transformation can vary. Below are some of the most common steps in the data transformation process.
Discovery and Profiling
This is where you make sense of and interpret the data at hand. You ask, “Is my data tabular? Or is it three-dimensional?” In terms of the attributes, you may have to check whether there is any data missing, or if there are additional metadata.
Mapping
This is where you plan the actual transformation process. If the goal of the transformation is to make data compatible with applications, this is the stage where you determine which parts of the data need changing, and which ones to be left as is.
Workflow Creation
This is done by either writing a script or using a data transformation tool. The workflow will depend on various factors, such as the expertise of the team, the transformation requirements and whether they will change over time, and the goals of the data transformation (enrichment, compatibility, etc.)
Execution
The old data will be presented in a new way after the workflow is run. For example, if you are transforming an old HTML file into the latest standard, HTML5, outdated HTML tags such as <dir> will be replaced with <ul>, a tag supported by modern HTML.
Review
Lastly, you need to check if the data has been formatted accurately. Document any issues, and adjust the workflow based on the findings.
Why is Data Transformation Important?
Businesses invest in acquiring features and technologies that will help them get a hold of information about customer behavior, supply chains, internal processes, and competitors, to name a few. But, what good is having this enormous amount of data if it cannot be used or made sense of? Below are some benefits of data transformation.
More Organized Data
The process of data transformation makes data more streamlined, making it easier for both computers and humans to understand them. Computers take longer to process data that is complicated, and processing raw data may also introduce errors.
Protection for Software and Apps
The data transformation process enriches the quality of your data, lessening the possibility of putting your software and applications at risk of encountering challenges such as duplicate data, null values, wrong indexing, and the like.
Compatibility
Data management costs can add up if you are using different tools for different types and formats of data. With the use of data transformation, you can ensure compatibility between different applications, systems, and data types.
Easier Retrieval
Your data, after being transformed and standardized, is stored in a source location, making it easy to access and retrieve. This can translate to more accurate and effective communication, easier knowledge sharing, and better customer service, to mention a few.
More Maximized Data
Data transformation organizes information in such a way that they are easily accessible and retrievable. When this is the case, data is maximized to be used for business intelligence. This data can be used for many purposes, such as reaching a wider audience in marketing, improving conversion rates, driving automation, and enhancing marketing and sales techniques.
Scalability
With the quality of data enriched, it is set up to perform in modern analytical data frames and databases. As your business grows, your data can grow with it.
Final Thoughts
As businesses are compelled to handle massive amounts of data today, they are faced with the challenge of making sure that the data they have is maximized, streamlined, and used for their own advantage. Data transformation aids in ensuring that data is used for business intelligence by refining, standardizing, and consolidating various types of data.
