Validata Blog: Talk AI-powered Testing

According an IDC research, the world is predicted to create 175 zettabytes of data in 2025, up from 33 zettabytes in 2018. The continuous growth in data volume, variety and use also includes sensitive data across a complex application landscape, both on-premises and in the cloud.

Artificial Intelligence augments testing to be zero-touch and autonomous

While DevOps and “continuous everywhere” transformation requires the ability to assess risks with every “sprint”, with speed and quality, testing on the other side has become the bottleneck to taking full advantage of more Agile methods to software development.

ETL usually involves moving data from one or more sources, making some changes, and then loading it into a new single destination. Traditional ETL platforms have been handling data following this ‘extract, transform and load’ model. That’s pretty straightforward. However, with the explosion of data and cloud adoption, they have been trying to expand their support to new data sources and data types and the cloud or hybrid environments prevalent today.

From a recent survey amongst banks and financial services organisations, testing is considered to be the number one factor that causes delivery delays, with testing costs accounting for approximately 30% of the overall IT budget.