Unified Data Quality Approach

Convergence of Data Quality, Data Observability and Data Discovery


Traditional Data Quality relies on manual processes such as identifying critical data elements, associating with business terms and manually creating business rules, and linking assets manually. Below are the steps a typical enterprise organization goes through before achieving a baseline measurement level. Unfortunately, this approach is cumbersome, does not scale with growth in data, and, more importantly, “it doesn’t work!” as it’s heavily based on people and processes.

Traditional Data Quality is complex, slow, and takes time!

With the growth in data and the adoption of modern cloud architecture or hybrid with data spread across in cloud and on-premise, both the producers and consumers of data are shifting away from the traditional ideologies around Centralized Data Ownership towards new principles around “Decentralized Data Ownership”. Instead of flowing the data from domains into a centrally owned data lake or platform, domains (SMEs) themselves host and serve datasets in an easily consumable way. Furthermore, today we see modern data teams using a variety of data architectures such as Cloud Data Warehouse, Data Lakehouse, Data Fabrics, and Data Mesh to suit their varying needs. This requires different personas ( Data Engineers, Scientists, Stewards, Analysts, etc.,) to look at the same data through different lenses but still come together and collaborate while observing, and measuring data quality in their own ways.

This requires modern data teams to move away from manual rules-based to automated, collaboration-focused and an unified data quality approach converging both data quality and observability

With Modern Data Quality platforms such as DQLabs, various personas can come together and collaborate in a meaningful way to make organizations more data-driven, and deliver reliable and accurate data. 

  • Data Engineers aka producers of the data not only have to be robust across data modeling, pipeline development, and software engineering but also have to ensure important data always meet reliability or SLAs. This requires executing a variety of data quality checks around completeness and freshness of data as they move data in and out of the pipeline to massive warehouses or Lakehouses and also adopting anomaly detections and their use in pipelines. With Modern Data Quality platforms such as DQLabs that leverage observability,  “Data Reliability” using Data Contracts/ SLAs are easily met as agreed upon with consumers/internal/external stakeholders in terms of data availability. 


  • Data Scientists or Data Analysts aka consumers of the data,  want to ensure that the data they use for building reports and analytical models are business fit. This evaluation and need for data quality change from time to time and also dictates the need for continuous data quality monitoring and measurement that is fit for purposes and uses the context of the data. This is now easily accomplished using DQLabs by performing aggregate analysis, deterministic rules, statistical measures, data accuracy, integrity, custom checks, and other dimensions of data quality that are necessarily not the focus of upstream or producers of data. 


  • Data Stewards or SMEs manage and enforce quality governance programs even in decentralized data ownership environments with a set of business terms that are identified as vital for the organization’s strategy. This requires consistent data quality checks and monitoring to identify known issues across the organization’s varying data assets irrespective of where and how it is defined in the technical metadata. With DQLab’s automated discovery of semantics and consistent quality checks, a path for “Agile” Data Stewardship or Federated teams is made possible.
  • Data Leaders or Business Stakeholders focused on what could make their next big win and want to understand how they could use the data on hand to enable new or improvised strategies toward positive business outcomes. This requires understanding the quality of the KPIs and KRIs they lean on to make decisions. With DQLabs, now not only can you have baselined values of business KPIs/KRIs but also continuously measure to provide leaders a top-to-bottom-down view in identifying new opportunities.


Now you can relate to how the data minds and business minds of the organization view the same data but relate and measure data quality in different ways using a modern data quality platform such as DQLabs.