The RevGen Data Quality Engine gave our client the visibility and control they needed to ensure high quality reporting
Our client was well along the path of modernizing their data; however, data quality issues consistently cropped up, impacting their progress and sowing doubts about the accuracy of reporting. So RevGen got to work, deploying our Data Quality Engine: an automated, scalable solution that measures, monitors, and improves data quality across an organization.
For this client, we integrated the solution with their existing Databricks and Power BI tools to proactively identify and report on data issues through a rule-based framework which highlights gaps and inconsistencies for quick and easy remediation. The platform also provides business users, not just IT, with clear, actionable insights that support ongoing data quality and the evolution of their data governance.
This client had the same data challenges we’ve encountered time and again: fragmented and inconsistent data governance, manual resolutions, no standardized processes, and difficulty identifying and resolving data issues across domains.
Data governance can only help data quality when it is applied consistently across all data and domains. When certain governance rules are ignored or forgotten, often because they’re difficult to implement, or used in one department but not another, the governance plan can’t work reliably.
One of the client’s biggest issues was manual processes for data quality, resulting in several misallocations, and lengthy resolutions in attempting to fix issues, especially when they affected multiple teams.
Because of all the issues, reporting was often considered unreliable, and decisions were made reactively. There was limited ability to scale data usage even as they recognized how critical data would be to their growth as a company.
As we have worked with this client on their data modernization initiatives, we were able to leverage our institutional knowledge to develop targeted data quality recommendations and validate those through stakeholder requirements sessions. Then, we defined and implemented seven “business rules” and thresholds, quantifying their impact with business executives and subject matter experts. Last, we operationalized the data quality monitoring through our Data Quality Engine (DQE) which was integrated with Power BI.
A critical part of implementing the DQE was ensuring it tracked issues that were strategically important to the business and could be easily understood and addressed by business users, not just technical teams.
The DQE has the ability to add as many automated rules as necessary, however, in our thorough research, we identified seven that would provide the most critical information into common data quality issues. These rules surfaced the main issues quickly, reducing manual investigation time.
With the integration into Power BI, the DQE provided new dashboards with clear visibility into failures, trends, and statistical analysis for targeted remediation. These dashboards also provided summary scores to specific rule violations, so domain owners could accelerate issue resolution.
One of the main benefits of the DQE’s modular architecture is that it enables easy integration to additional data sources in the future and can support evolving rules and requirements. Even through just the few weeks of trial to full implementation, it expanded from just a handful of checks to thousands, adapting to new domains and business needs.
The DQE achieved its main goal incredibly quickly: increase the trust in the company’s data and enable better, more confident decision-making.
Even with just seven rules, the DQE enabled business stakeholders to review and act on over 2,500 identified data issues through transparent reporting and structured escalation to the appropriate resolution teams.
The new, reliable and transparent reporting increased confidence in data-driven decisions while the automated monitoring reduced manual checks and rework. Plus, the improved governance alignment led to increased compliance, feeding greater overall data accuracy.
As part of the ongoing DQE governance, we helped our client set up a forum for centralized rule management and stakeholder engagement, allowing the DQE to scale and change alongside the business’ data needs.
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