Mesut Durukal from Indeed will dive into measuring and ensuring quality deliverables. 
Learn to identify key quality metrics, track them effectively, and manage automated collection for better insights and decision-making.
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Hi there,
If you couldn’t catch the session live, don’t worry! You can watch the recording here:
Here are some unanswered questions that were asked in the session:
Does monitoring involve only providing visibility?
As QA processes evolve, what strategies can be employed to ensure comprehensive monitoring of QA results? How can organizations set up automated systems and alerts to maintain high-quality standards and swiftly respond to any emerging issues or trends?
Does monitoring involve only providing visibility and snapshots, or should it also include features that alert or warn you if something is likely to go wrong?
How do you determine which metrics are most critical for monitoring the success of a project or process?
How can metrics be used to identify early warning signs of deviation from a project’s goals or timelines?
How can we ensure that the metrics we track in QA are truly indicative of product quality, and what steps can we take to avoid becoming too focused on numbers that might not reflect the user experience?
How can I ensure the metrics I’m monitoring accurately reflect the project’s progress?
For quick reads, are there monitoring metrics that are both quick and easy to acquire and also sufficiently accurate?
QA metrics are a subset of product metrics, so can we say measuring QA metrics is the true definition of product quality?
How can I ensure the analyses are perfect to grow the project?
Can we use any external services to measure the exact metric of the session?
What would you recommend to store the metrics results in the Cloud?
How can we use predictive analysis and quality control to identify patterns in monitoring data, and how can we predict future trends or anomalies based on these patterns?
How to effectively update the test cases master sheet of the project while monitoring and adding test cases based on new metrics?
Can you recommend other specific metrics to measure embedded-software quality? Thank you!
Can you share the best way to guide Goal-Question-Metric sessions to help an organization identify metrics of interest? Thanks.
What depth and breadth of human oversight should be leveraged when checking on automated monitoring metrics collection?
Here are some of the Answered Question from this session:
What role do predictive analytics and AI play in enhancing the monitoring of software metrics?
Mesut Durukal: Predictive analytics and AI enhance monitoring by analyzing historical data to identify patterns and predict future issues. They help in proactively addressing potential problems before they affect the software, improving overall reliability and performance.
