The Philosophy of Separation - Data Transformation and Assertion with Qualytics
As the digital age advances, ensuring the accuracy and integrity of data has become paramount. Among the myriad principles that guide data quality efforts, one principle merits special attention: the separation of data transformation from the application of data quality checks. This isn’t just an operational distinction; it’s a philosophical approach that amplifies the potential of both procedures.
Enterprises face potential data issues, some of which they’re aware of (termed ‘known unknowns’) and actively monitor, often driven by regulatory or compliance mandates. Then there are ‘unknown unknowns’, issues that remain unnoticed until they cause disruptions.
In this realm, our software platform at Qualytics stands out with its two-pronged approach to address both types of data quality concerns:
- Data Transformation: Converting, joining, and aggregating data, thereby enhancing its business context.
- Application of Data Quality Checks (or Assertion): Validating that this transformed data adheres to enterprise expectations along all dimensions of data quality, ensuring it is fit for purpose.
By separating these activities, each is optimized for a proactive approach to ensuring data quality for the enterprise.
Unpacking the Philosophy
At the heart of Qualytics is the distinct separation of data transformation from assertion. By doing so, our platform’s machine learning algorithms are able to discern patterns and relationships within the rich context of the transformed data. This boosts the efficacy of the platform’s fully automated efforts to detect future anomalies (unknown unknowns).
Further complementing this is the application of data quality checks on the transformed data. The platform’s intuitive interface empowers data stewards to craft precise controls addressing those ‘known unknowns’, aligning with business requirements due to the enriched context of the data.
The Symbiotic Relationship
This philosophy breeds a symbiotic relationship. Richer data transformation enhances machine learning performance. In contrast, clear transformed data facilitates the creation of precise human-authored controls. Thus, Qualytics offers a dual advantage: robust automated checks due to machine learning and an intuitive interface for addressing specific known issues.
A Tale of Transformation: A Financial Institution’s Odyssey
Our collaboration with a major financial institution paints a vivid picture of the complexities faced when intertwining data transformation with assertion checks.
Anti-patterns Observed:
Muddled Accountability: With the conflation of transformation and assertion, when a data quality issue was flagged, it was hard to determine whether the root cause was due to an error in the transformation logic or if the data genuinely represented an unexpected characteristic.
Extended Debugging Time: Teams often spent excessive time in troubleshooting mode. Was the transformation logic flawed, or was the original data source compromised?
Lack of Clear Metrics: Due to the overlapping processes, it was challenging to pinpoint specific metrics that contributed to failed assertions. This obscured visibility into the data’s journey, making it tough to identify improvement areas.
Eroding Trust: With every false alarm or prolonged debugging session, stakeholders’ faith in the system was eroded. It became common to question the system’s outputs, leading to increased manual checks and double validations.
With Qualytics, we untangled this web. A clear boundary was set between transformation and assertion, creating individualized zones of accountability. This not only enhanced transparency but also boosted efficiency. Data issues could now be diagnosed rapidly and precisely.
What emerged was a robust system where stakeholders no longer had to play detective. The once cloudy and suspicious landscape transformed into a transparent, expert-led narrative, where each phase of data’s journey was clearly marked, validated, and trusted.
Conclusion
Separating data transformation from assertion goes beyond technicalities; it’s foundational. And with Qualytics, businesses don’t just get reliable data; they glean insights with unmatched precision and clarity.