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Extending JIRA to Enable High Volume KPI Benchmarking - Keyur Patel
The document discusses enhancing Jira's capabilities for high-volume KPI benchmarking at Intel, addressing issues like scattered data across spreadsheets and limited analytics. It outlines a structured approach to centralize data, standardize quality metrics, and automate processes to improve visibility and efficiency in benchmarking. Key takeaways emphasize the importance of reusability, automation, and maintaining a standard methodology across teams, while suggesting the benefits of expanding Jira's functionalities through an integrated ecosystem.
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Extending JIRA to Enable High Volume KPI Benchmarking - Keyur Patel
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- 2. Extending JIRA toEnable High-Volume KPI Benchmarking KEYUR PATEL • SOFTWARE QUALITY • INTEL • @keyurpatel_r www.linkedin.com/in/keyurpatel00/
- 4. Agenda! 1. Traditionalapproach followed by problem statement 2. Explore Jira capabilities outside of bug tracking 3. Key takeaways for you to implement this in your teams
- 5. Problem with traditionalapproach! • All the data is spread across millions of XLs - limiting management visibility • Lacking data traceability against code check-ins • Limited data analytics, with 2-D statistics • Slow turn around time for model changes QA LAB Stakeholders
- 6. But… where dowe start?" KEEP IT SIMPLE STUPID Scalable – to support all business unit groups across Intel Build it FAST! Ship it OR get out of the way
- 7. Our Approach …advance the art of benchmarking across Intel.
- 8. 1 Jira REST2 Invent within the scope of available tool set! 4 3 Reporting and Analytical tools
- 9. Blue print! Teams/ Projects Intel Benchmarking Libraries (KPIs) Standardize: Consistent Quality Standards, Metrics and Calculation Methods across Intel Benchmarking KPIs like… • User experience • Performance against key benchmarks Power consumption • Power consumption • Audio quality • Graphics quality • …
- 10. Blue print! QALab / Testing tools Validation Errors Flat File Jira REST web services Project Specific Benchmarks Jira import Script Teams / Projects Intel Benchmarking Libraries (KPIs) Weekly test data Standardize: RE-USE benchmarking Consistent Quality standards Standards, and best Metrics methods and amongst Calculation Methods projects across across Intel Intel JIRA – Collect, Service, and Report Automate: Eliminate manual efforts of tracking individual benchmarks Drop off folder
- 11. Project specific KPIs Weekly / Daily measurements from QA LAB Threaded discussions History and recent activity Owners, watchers and timestamp Supporting files and data
- 12. Blue print! Email Validation Errors Flat File QA Lab / Notification Testing tools Dashboard: Data fed into Jira and ready for dashboard consumption (real time) Relational Database Jira REST web services Project Specific Benchmarks Jira Import Script Teams / Projects Intel Benchmarking Libraries Weekly test data Jira plugin charts Enhanced data analytics Drop off folder
- 13. History trend: Abilityto track historical data and analyze KPI trends within Jira.
- 14. Impact" • Eliminatedhuge problem of data residing in distributed systems via manual collection ( = !Spreadsheet Hell) • Single source of truth • Sleek and easy to use front-end GUI, that everyone knows • Traceability over Benchmark measurements against Code check-ins … Make benchmarking a snap!
- 15. Key takeaways: #atlassian • Primary goal: 100% Re-use and Maximize Automation• It’s easy to get lost in the features of the product - don’t neglect the point that you have to develop, deploy and most importantly maintain it easily • With any large organization, creating a standard methodology that all teams buy into is a challenge. This is an upfront exercise which requires high level sponsorship and direction. • Leverage plug-ins and tools from Atlassian ecosystem to avoid re-creating the wheel. E.g. Native support for triggering field transfers from parent issue to linked issue(subtask) using Innovalog workflow extensions plugin!
- 16. Expanding JIRA Capabilities! • Implementing similar functionalities would be greatly benefitted by an expansion of Jira capabilities natively in data warehousing and configuration management. • A Robust, Atlassian backed ecosystem would be a key enabler for Jira customers to similarly expand the tool’s capabilities beyond just defect tracking
- 17. Thank you! KEYURPATEL • INTEL SOFTWARE QUALITY • @keyurpatel_r www.linkedin.com/in/keyurpatel00/ I would really like to thank BlackPearl for partnering on this effort. For more details on implementation please visit their booth.
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- 19. For detailed questionson implementation please contact! ALM Tool Development.Atlassian Experts