Data Testing Automation: Lessons from TSB Bank's Migration Meltdown

# Get ready for the next event!

We have for you the start of an awesome lineup of events this October. Please join us on Thursday the 17th for a evening of conversation and insights on data automation. Refreshments and food will be provided, graciously sponsored by iceDQ.

iceDQ is a comprehensive platform for data reliability, offering unified data testing, monitoring, and observability. Trusted by enterprises in banking, insurance, and healthcare, it supports both development and production environments. iceDQ automates data migration, ETL pipeline, big data lake, and BI report testing, identifying issues early in the data lifecycle. In production, its AI-powered observability detects anomalies and reports incidents, ensuring robust, reliable data processes for smooth operations.

Headliner Talk:
● The Challenge: The rise of AI and big data initiatives like cloud migrations, data lakes, warehouses, pipelines, and BI/CRM systems is pushing businesses to manage vast troves of data. These complex projects, often characterized by terabytes of data and background processes, pose unique challenges for traditional QA automation tools primarily designed for user interfaces (UI).
● The Opportunity: While application testing automation is well-established, a critical gap exists – a lack of specialized data testing tools and expertise. This gap presents a significant opportunity to leverage data testing automation.
● The Case Study: In 2018, one of the largest British retail banks, TSB, botched a major customer data migration project. Millions of customers were affected as TSB's website went down for more than three weeks. The chaos led to a UK parliamentary enquiry and a £294.9 million regulatory fine for TSB. The bank's CIO was sacked and also personally fined £81,620.

Takeaways from this Session:

1. Differences between data testing and application testing.
2. Why dedicated data testing automation is essential.
3. Core concepts of data testing.
4. Various data testing techniques and their implementation.
5. How TSB's migration failure highlights the need for thorough data testing.
6. Career opportunities for QA engineers in data testing

Special thanks to iceDQ for sponsoring this event and providing food and refreshments! And of course we couldn't meet without our go-to spot Excella/ATX offices!

The Agenda
• 5:00 - 6:00 - Meet and Greet
• 6:00 - 6:15 - Introductions and Announcements
• 6:30 - 7:30 – The Talk
• 7:30 - 7:45 - Closing Remarks
• 7:45 - 8:30 - Networking

About Sandesh Gawande:
Since 1996, he has led major data engineering projects for top banks, insurance, and healthcare companies like Deutsche Bank, JP Morgan Chase, and MetLife. His expertise spans DataOps, Data Architecture, Data Testing, and Big Data.
As co-founder of iceDQ, the #1 platform for Data Testing, Monitoring, and Observability, he helps institutions automate cloud data migration, ETL testing, and implement data monitoring solutions.

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