We understood everyone preaches Data Quality however don’t have an out of the box platform for an end-to-end data quality across both business and technical users. Bad data will never go away but understanding and fixing where possible using automation is much more important with growth in data.
The efforts spent towards traditional rules based DQ measurement haven’t scaled and newer thought process such as observability without the lack of semantics or business context results in more false positives than being useful. The net result is that organizations spent time and money without any value generation.
With growing data and modern data management, it was time to pragmatically shift away from traditional ways and move into a new way of “Data Quality First” approach.
An approach that works for all types of users – business and technical. That’s what we did. We worked with various clients to understand the challenges across various ecosystems, users, different maturity life cycle and built a platform with three guiding principles in mind. Over the next year, we did various deployments and solution accelerators across a wide variety of verticals and enhanced the platform to be more stable, provide immediate ROI and more importantly user friendly, self-learning as it goes to adapt to your unique organization needs.
