#bi testing

We sat down with Adrian Thibodeau, Technology & Quality Engineering Leader, and Shrikant Patil, Chief Product Officer at iceDQ for a candid, experience-led conversation on a truth most data teams don't talk about enough. Here's the uncomfortable question we opened with: “Do data teams actually know the customers whose experience lives or dies by the quality of that data?” Spoiler: most don't, and it's not because they don't care. It's because they're often several steps removed from the end user, with no visibility into how their pipelines feed critical decisions downstream. What you'll learn in this episode: 🔹 Why technically perfect pipelines still generate customer complaints. 🔹 How the absence of customer journey context keeps teams stuck in firefighting mode. 🔹 Why "99% accurate" data can still be catastrophic. 🔹 Why AI without context is just faster, more confident wrongness. 🔹 What high-performing data teams actually do differently - culture, habits, conversations. If you're leading data, quality, or product teams - this one's for you.

Looking for a reliable Power BI report testing tool? iceDQ helps automate validation across reports, dashboards, and semantic models to ensure data accuracy and consistency. It enables businesses to test KPIs, visual elements, and datasets before deployment, reducing risks caused by incorrect insights. With support for DAX queries and automated checks, it simplifies testing workflows and improves reporting reliability. This solution ensures your Power BI reports are always aligned with underlying data and business rules, helping teams make confident, data-driven decisions.

Get an in-depth understanding of big data testing with a complete guide covering strategy, tools, and automation techniques. This resource explains how to validate complex data pipelines, including ETL workflows, data migration, and large-scale processing in distributed systems. It outlines essential testing approaches such as functional testing, performance testing, and data validation to ensure accuracy and consistency.

A global pharmaceutical company worth $130 billion faced challenges validating complex clinical pathology data for SEND submissions to the FDA. With in vivo study data stored across various EHR platforms and Oracle® systems, manual validation processes were time-consuming—taking up more than 1,200 hours each year and creating bottlenecks in compliance and approvals. They chose iceDQ to automate their SEND data validation process.

iceDQ’s advanced in-memory validation engine helped streamline checks across key domains like LB (Lab Results), CL (Clinical Observations), and MI (Microscopic Findings), aligning data with CDISC SEND standards. The result? A 95% reduction in validation time, 60% fewer resources required, and full alignment with FDA expectations.

The tool also enabled seamless integration with SAVANTE for SEND file creation and Pinnacle 21 for error-free data outputs. By automating data ingestion, validation, and reconciliation—especially from HL7 streams into systems like Oracle and Cerner—iceDQ made it easier to manage clinical trial data efficiently and compliantly.

iceDQ is a purpose-built platform with integrated data testing, data monitoring and AI based data observability capabilities.

Building a strong foundation for data quality is vital to making strategic decisions. With the iceDQ v2.0 User Training, you'll gain the technical knowledge and strategic mindset to design, monitor, and optimize a reliable data quality framework.

What You Will Gain:

7 progressive chapters guiding you from basics to advanced features.

Over 30 interactive videos to reinforce concepts visually.

Real-world business examples to bridge the theory-practice gap.

Final certification to demonstrate your expertise.

Practical tools and workflows you can deploy immediately.

This course empowers professionals to handle data complexities, whether in healthcare, finance, retail, or any other domain. You’ll learn to automate validations, set up monitoring dashboards, and ensure compliance with data standards.

Your data systems deserve more than just monitoring—they need reliability. The iCEDQ Data Reliability Engineering Course walks you through key concepts and real-world scenarios that help you build trust in your data processes. From pre-deployment testing to continuous pipeline health monitoring, the course provides practical solutions for data loss, delay, or corruption issues.

Here’s what you’ll master:

Data issue detection & resolution

Root Cause Analysis (RCA)

Automated alerting mechanisms

Monitoring best practices

Built by experts who understand data pipeline challenges inside and out, this course offers immense value for professionals in data-driven roles.

🔗 Check out the full course here