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Data @ the core of Enterprise Agile
Mathew AniyanProgram Manager, Microsoft
Abstract
• Agile adopts an empirical approach to software development. One of the key aspects of a successful Agile Implementation is how quickly we can react to change. For this, we need to ensure that data flows seamlessly from customer to the Agile team. This data should form a critical part of our decision making.• Is the customer successful in using our product or service?• Which features are customer most interested in?• Where are the friction points in usage?• Where are the failures happening in our product?• How is the customer engaging with our product over time?• and many more similar questions.
• In this talk, I discuss best practices in data collection, analysis and visualization and how data can make your Agile process and thereby your business more effective.
Agile Manifesto - http://www.agilemanifesto.org/
Principles behind the Agile Manifesto• Welcome changing requirements, even late in development. Agile
processes harness change for the customer's competitive advantage.
• At regular intervals, the team reflects on how to become more effective, then tunes and adjusts its behavior accordingly.
Data as the lifeblood of Agile
• Helps understand changing requirements• Measures progress against goals
Before we start: Privacy
• Classify your data• Seek customer permission• Security• Who can access?• How to secure?
• Err on the side of caution
Design your data
Start with the Business Questions
How will you visualize the data?
What decision will you make?
A Sample Business QuestionDemo
Data Collection Infrastructure
Hot PathDemo
Warm PathDemo
Cold PathDemo
SurveysA sample questionnaire
Analysis – Per Incident
• Root Cause Analysis• 5 Whys
Analysis – Per Day
• Daily trends• Operational
Analysis – Per Week
• Operational• Learning focused
September 7
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
8 9 10 11 12 13
14 15 16 17 18 19 20
9/8/2015 - 9/15/2015
Mean Time
• To Detect• To Fix• Between Failures
Analysis – Per Month
• Business focused• Experiments
Machine Learning
• K-Means clustering
Visualization Samples
Availability
Usage Funnels
Map Visualizations
Cohorts
Flow Diagrams
TreeMap
Animated Bubble ChartsDemo
Tables with Sparklines
Summary
• Use data to drive your decisions• Plan your data• Develop the right data collection infrastructure• Analyze at needed• Use visualizations to communicate with data effectively