About Us

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.

Data Quality First

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.

Self Learning

Semantics (business context) based automated Data Quality measurement and monitoring

Self Service 100% DQ Automation

Automate as much as possible but with relevance and self-learning capabilities

Enables all types of users

Serve all type of users – Business (Data Quality Stewards, Catalog, Governance) and technical (DataOps, Data Scientist, DevOps, Data Engineers etc.,)

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.

As a result, today you get to enjoy an Out of the box Actionable Data Quality Platform for all needs.

Best Practices

EVENTS

The rise of the Modern Data Stack and the Modern Data Quality Platform

The data producers, consumers, and leaders deserve an ecosystem that delivers the data that is relevant to them – one size fits all approaches and solutions no longer cut it in this modern data landscape. Data minds and Business minds see data in different ways even when working on the same data for the same business outcomes. You need a platform that promotes Decentralized Data ownership culture to improve data relevance and data collaboration.

Abstract:

With the growth in data, there is an explosion of modern architectural thinking (Data as Product, Multi-Cloud) that has led us to Cloud Datawarehouse, Lakehouse to Data Fabric, and Data Mesh adoptions. With this growth and expansion of technologies, both the Data Producers and Consumers of data are shifting away from the traditional ideologies around Centralized Data Ownership towards new principles around “Decentralized Data Ownership”. Further, requires a tight collaboration across different persona to meet business needs and the data quality needs.

This requires not just looking at metadata but going beyond metadata and looking under the data to derive insights from top to bottom and tying directly to business outcomes. We at DQLabs believe that a comprehensive modern data quality check should go across these three levels – Data Reliability, Business Fit Measures, and KPI metrics.

In our second installment of the Defining Data Relevance webinar series, Raj Joseph, Founder and CEO of DQLabs, and Sanjeev Mohan, industry expert and Principal at SanjMo, unpack the complexities of the Modern Data Stack and Modern Data Quality Platforms and the ever-growing need for Data Relevance.