What Is Data Transformation?

Data transformation is the process of converting data from one form to another to make it more useful and easier to analyze as part of a decision support process. Data transformation involves extracting data from a source, converting it into a useful format, and delivering it to a destination or user. This is otherwise referred to as the ETL (Extract Transform Load) process. 

Why is data transformation needed?

Data transformation is required by organizations that want to make data more useful and meaningful to end users. When properly formatted, cleansed, and validated, business leaders can be more confident that the data is relevant, reliable, and sustainable.

How does data transformation impact HR?

HR is a business function that heavily relies on data. In fact, the people analytics field has emerged as a critical element of HR. This means it’s also a core need for organizations concerned about making the most effective and efficient use of their human resources. 

Data transformation impacts HR by allowing you to use data to make informed decisions related to every aspect of HR—from recruitment and talent acquisition to onboarding, talent management, performance evaluation, professional development, and more. 

Advanced data analysis like predictive analytics now allows HR leaders to use data to identify trends and make proactive recommendations and data-driven decisions.

What are the types of data transformation?

There are several types of data transformation you should consider. These include:

• Data cleansing. Identifying and correcting inaccuracies or inconsistencies in data.

• Data integration. Combining data from two or more sources.

• Data conversion. Converting data from one form to another. 

• Data consolidating. Pulling data from various sources into one single dataset. 

• Data enrichment. Enhancing data by adding additional data obtained from other sources.

What is an example of data transformation?

An example of data transformation in HR includes transferring data from employee engagement surveys. For instance, free-form text can be transferred into a structured format with responses categorized based on key phrases or sentiments extracted from the source documents. This makes the information easier to understand and analyze. 

There is an even wider range of other opportunities for the use of data transformation in HR, including, for example, analyzing candidate pools or creating personalized L&D programs.

Read more on data transformation:    

• 10 Ways Visier Helps With Your Most Common Data Challenges

• How to Ingest People and Business Data Into Visier

• How Visier Turns Complex Source HR Data Into Meaningful Insights