I have spent a decade balancing my time between Data and Software QA.

The quality of a system is defined by its behaviour against the designed specifications. Be it a business or a technical use case. The quality of data is defined by the degree of its usefulness in the decision-making process. The underlying thesis of either approach is to determine the reliability of the system and/or the data.

The process of quality assurance is to design tests to catch anomalies in the system.

My journey in the software QA world has been full of ups and downs.

In the software testing world, the domain always piqued my interest.

I ventured from breaking down the software in FinTech to Telecom and majorly in the Healthcare world. The fascination with the way system was designed and trying to break the same systems allowed me to explore the entire software development process rather than just programming.

The automation bridged the gap between the worlds of the domain and technical stack.

My journey in the Data QA world was a happenstance.

The world of data, as we know it now, is borderline infinite.

When I first started exploring the Data Quality Assurance process, the sheer volume of tasks and the expertise required felt daunting. I started in healthcare and fair to say it was like being exposed to an entirely different world. The data consumed me.

The practices and approaches that were followed for quality control not only fascinated me but taught me the value of data to a particular system.

Surfing through the two worlds bound by data.

Here I was surfing between two worlds. Even though the approaches and practices may differ, I was doing the one thing I liked to do the most. It was finding flaws in design and exploring anomalies that may cause a system to fail.

Today, while I sift through the data and work with the maintenance of underlying systems, I enjoy this pure connection that Quality Assurance has allowed me to build between system and data.