Data-Driven
I'm a huge believer in Data when it comes to QA and I see one of QA's primary function to be able to collect, generate, analyze and visualize Data to better inform the rest of the development team about a project's Quality at any given time during development.
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When done right, Data can also be used to showcase trends, develop predictions and prevent disasters.
I have extensive experience with all of these aspects.
Metrics
Some of the metrics I like to collect and utilize are: A Bug Burndown Chart, Test Coverage, Test Case Completion, Open by Priority, Open by Severity, Crash Rate, Crash Percentage, Performance Gains, Memory Usage, Frame Rate Gains, Issue Severity Distribution, Newly Opened Issues, Newly Resolved Issues, Newly Closed Issues, Issue Priority Changes, Priority Distribution across all issues, Bug Distribution Rate, Bug Closure Rate, Bug Re-Open Rate, Bug Regression Rate, Bugs Discipline Distribution
Reporting
I have experience with creating tailor made Dashboards in both Jira and through Spreadsheets that highlight whatever metric is useful for stakeholders and creating reports in various formats based on stakeholder needs.
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Reporting is also important in other aspects such as regular dedicated QA Reports outside of Dashboards for various stakeholders.
Data Collection
I have experience with manually collecting data through Jira filters or automatically collecting data through Jira plugins. I also have experience working with tools like Tableau to work with telemetry data.
Data Visualization
As mentioned above, I have experience with creating dashboards that visualize data and I find the visualization of data to be important as it makes it easier to digest for various stakeholders.
Insight-based Decisions
I like using data to find trends and present those trends to stakeholders in order to align around a common strategy.
Feedback Loops
I find continuous feedback to be crucial within QA and try to foster a feedback culture within the QA Team and also help gather and summarize feedback for stakeholders.
Predictive Analytics
With enough data, trends can sometimes start to show and predictions can be made, which can be useful to avoid future problems. By analyzing data I like to try and be proactive and get ahead of any of these potential issues.
Live Analytics
I have experience working with live analytics like telemetry as well as Steam Hardware Survey data to make decisions on testing strategies.