Good data is the foundation of digital organizations because every decision, customer experience and autonomous action depends on its accuracy, completeness and trustworthiness. With increasing adoption of AI propelled by data-intensive platforms, the stakes are exceptionally high—poor data can result in financial loss, regulatory fines and reputational damage.
As AI-driven autonomous workflows become pervasive, pursuing trust via data assurance is a strategic initiative. However, implementing comprehensive data assurance is fraught with challenges. Modern enterprises operate in complex and dynamic environment comprising hybrid networks and multiple data pipelines. These diverse, distributed data pipelines lack visibility and control, making data validation fragmented, limited to isolated checkpoints rather than a continuous and integrated process.
NewVision has transformed the data assurance journey of large global organizations with robust validation solutions. Partnering with leading businesses across industries, we have empowered organization with trustworthy data and garnered deep insights. Below we highlight major challenges in implementing comprehensive data assurance and learnings to overcome the gaps.
BRIDGING GAPS IN DATA ASSURANCE – SIX CHALLENGES AND HOW TO SOLVE THEM
1 Poor Quality Data undermines trust and decision-making
When data is flawed, the best laid plans go haywire, however meticulous the execution may be. This can have far reaching consequences in different industries—in audit and tax, poor data quality can lead to non-compliance and misreported filings while poor quality data in insurance hampers risk assessment.
How NewVision Fixed: Implement quality gates across the data lifecycle for continuous data validation to catch errors early, build confidence and prevent downstream impact.
- Lack of sufficient test data limits coverage and risk detection
Siloed data sources and regulatory restrictions such as HIPPA and GDPR create data paucity. It is difficult to get sufficient data such as frauds, anomalies in claims or production defects. Test coverage cannot simulate real-world complexities and becomes incomplete without large and diverse data sets.
How NewVision Fixed: Use AI-generated synthetic data to overcome lack of data diversity. AI uses advanced technologies such as LLMs and Generative Adversarial Networks to mimic the structure and property of real-world data without compromising privacy or data confidentiality.
- Tool-specific automation leads to siloed validation, redundant effort, and fragmented visibility
While tool-specific point solutions accelerate tasks in isolated cases, it does not support holistic data validation. For example, ETL, BI and application teams will test data separately resulting in redundant efforts, gaps in testing standards, delayed error detection, root cause analysis and impact assessment.
How NewVision Fixed: Implement unified testing automation framework across UI, API and data layers for reliable and scalable testing framework that cuts across tools, environments and workflows. It provides end to end visibility ensuring traceability, robust governance and higher re-usability of testing assets.
- Manual report validation is slow, error-prone, and unscalable
Manual data checking across dashboards, applications and spreadsheet is time consuming and unscalable. As applications grow, complexities increase giving rise to inconsistent validation and absence of traceability.
How NewVision Fixed: Implement automated reporting with custom tooling and strategies with tailored QA frameworks mapped with unique business logic, thresholds and data relationships.
- Overdependence on UI testing leads to slower feedback and fragile tests
UI testing cannot validate underlying data in the pipelines, ETL, API and databases, or test the business logic. UI test validates only what is visible on the screen.
How NewVision Fixed: Embrace a testing pyramid optimized for data-heavy applications with 70% data layer automation, 20% API, and 10% UI. Most data-heavy applications embed business logic at the ETL, aggregation and transformation stages, so focus testing on data ingestion, schema validation, integrity, consistency across sources, data drift.
- Misaligned team skill sets result in poor automation adoption and inconsistent quality
Businesses often falter in acquiring the right automation and testing skills, leading to inconsistent test coverage and unreliable automation. For example, QA professionals with expertise in UI testing lack exposure in API testing and data validation.
How NewVision Fixed: Deploy a test skill pyramid including UI, API and data layers, supported by a structured organizational model to facilitate collaboration, ownership and continuous learning. A Center of Excellence provides training; organizes re-usable libraries and best practices; and maintains testing frameworks and tool alignment.
Strong Data Test Strategy is Key to Building Business Trust
Business decisions are only as good as the insights that power it. Today, trustworthy data is not just essential, but the cornerstone of a high-performance organization. If data is the new oil, trust is the new gold.
A digital strategy sans data assurance strategy will wobble as it scales and send ripples across the organization. As organizations scale AI-powered digitalization from pilot to production, the time to get started with data assurance is now, sooner than later.
If you want to know more about data assurance or how NewVision can transform the data testing strategy, write to us at contact@newvision-software.com.
