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ANIn Coimbatore July 2023 | Business Agility in Data Science by Dr.Selvaraaju Murugesan
1. Business Agility in Data Science
Speaker
Selvaraaju Murugesan
Head of Data Science, Kovai.co
AGILE NETWORK INDIA
2. Disclaimer
All the characters in this story are fictious
Opinions expressed are solely my own and do not express the views or opinions of my employer
All visuals used in this presentation is to enforce the intensity of the argument; It does not
support violence
5. Scene setting
1. Data scientist in a team of 5
2. Service organization
3. Team – PO, SM, 2 Dev, 1 BA
4. Evolving Scrum practices
5. Zero training
6. Learn as you go
7. Fail fast..learn fast culture
6. Dashboard process
Responsible Delivery Manager Data Analyst Data Analyst Data Scientist Delivery Manager
Activity Dashboard
requirements
Efficiency – time
savings, cost
reduction $,
revenue generation
Data acquisition
Technical
documentation
Dashboard Design Peer review within
data science team
Stakeholder
presentation
Outputs Word doc PowerBI reports / visuals / dashboard
Sprint # 1st
2nd
Discovery stage Dashboard design Presentation
14. Scene setting
1. Product Owner in team of 26
2. Service organization
3. 3 teams
1. Dashboard
2. Digital product
3. AI features
4. Team is trained on Agile practices
5. All processes are streamlined
6. Whole org is on waterfall model
15. Good things
Team work
Mix of introverts and
extroverts
Stakeholder need
Tell me what you need
Product owner
Focus is on the flow
16.
17. Agility
• Scrumban approach
• Scrum for individual team
• Kanban for Delivery Managers (Scrum Master)
• Product owner is a bumblebee talking to all
stakeholders all the time
• Communication is the key
• PO & SMs calendar was shared
• Business outcomes are attributed to team’s effort
• Leadership buy-in
• 20% slack time built-in; Scrum Master
orchestrates
• React to change -> Anticipate change
• PO is the key
• Delivering outcomes - iteratively
18. Agility
• Delivery Managers - Process governance
and optimization
• Minimum Viable Documentation –
Documentation culture
• Process shortcuts are encouraged
• Portfolio approach of building common
capabilities
• Ability to foresee and lay foundational
work for future capabilities
21. Scene setting
1. Data science – team of 5
2. SaaS Product organization
3. Team – PO, Data scientist and analysts
4. Scrum practices
5. “Get things done” at any cost
27. Challenges
Product bugs
Low quality -> iterate ??
Scope clarity?
Roadmap
Planned vs delivered
Productivity challenges?
Team morale
Lack of interest and
motivation
Drop everything : Go there !!
28. Best practices
• When speed matters
• Sacrifice quality and cost
• Everything Everywhere All at Once
• Communication is vital
• Trust is sacrosanct
• Lean process, Agile leadership, small team
• Can’t scale
29. Easy to say but hard to
implement
Hard to implement even harder to
optimize