At Intel, KPIs are measured and reported weekly across multiple, global projects. In this session, we'll cover the business case and architecture behind developing an end-to-end process management system for KPI benchmarking, tracking, and monitoring using JIRA for more than 6,000 users.
4. Agenda!
1. Traditional approach 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 traditional approach!
• 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 do we 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 REST 2
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!
QA Lab /
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
14. Impact"
• Eliminated huge 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!
KEYUR PATEL • 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.