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What Is ETL?

Understand the importance of ETL processes and best practices needed to support HR operations in the long run.

2M Read
Visier HR Glossary

ETL (Extract, Transform, Load) is a crucial concept in the field of people analytics. ETL involves extracting data from a range of sources, transforming it into a consistent format, and loading it into a data warehouse or other repository where it can be analyzed.

What is the importance of ETL?

ETL is important for ensuring accuracy in the analysis of data so you can get insights that allow you to drive reliable and accurate decisions. From a people analytics perspective, data may be stored across various HR systems, departments, or divisions. 

Additionally, consolidating that data and checking its accuracy can help you make more confident people decisions. ETL also allows data to be collected from a variety of different systems or sources and transmitted into a single, unified location (in a consistent format) to enable analysis. 

What best practices are needed to support ETL?

One critical best practice is thoroughly understanding data sources, including the structure, quality, and semantics of the data. It’s also important to ensure that the data fed into ETL processes is as clean as possible. 

Automating ETL processes like data profiling, cleansing, transformation, and validation can ensure the reliability and validity of the data. Finally, you should document ETL processes thoroughly, creating a knowledge repository for data quality rules, metrics, and procedures for handling errors.

How does Visier support ETL processes? 

Visier People® automates and simplifies ETL and data cleansing processes to deliver accurate workforce insights. You can configure data pipelines from your platform into Visier, guaranteeing that these pipelines are fully automated, secure, and monitored. 

This way, you’re able to extract, transform, and load data so that it will best support your people analytics needs. Visier's API can also be integrated into a pre-existing ETL process if the data is at a suitable level of granularity and quality.  

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