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Business Agility in Data Science
Speaker
Selvaraaju Murugesan
Head of Data Science, Kovai.co
AGILE NETWORK INDIA
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
Mental Health Advice
Practising Agile in waterfall organization will drive you MAD
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
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
Scrum? What?
Burndown?
Time
Challenges
Organization resistance
Hinder in delivering
MVP
Stakeholder need
Show me what I you have then
we will tell you what we need
Product owner
Lack of direction
Challenges
User stories
Inability to prioritize
Documentation
No documentation
produced
Micromanagement
Focus is on capacity
maximization
Business Agility?
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
Good things
Team work
Mix of introverts and
extroverts
Stakeholder need
Tell me what you need
Product owner
Focus is on the flow
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
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
Challenges
Product Owner
Burnouts
Appraisal
Promised vs Delivered
Budgets
Follow the money
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
Business Agility Drivers
Porter's five forces
Speed matters
Show me the Benjamins!
Good things
Objective
Team’s laser focus on
outcome
Freedom
All resources are lined up
Business growth
Revenue generation activities
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 !!
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
Easy to say but hard to
implement
Hard to implement even harder to
optimize
Thank You

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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
  • 3. Mental Health Advice Practising Agile in waterfall organization will drive you MAD
  • 4.
  • 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
  • 9.
  • 10. Challenges Organization resistance Hinder in delivering MVP Stakeholder need Show me what I you have then we will tell you what we need Product owner Lack of direction
  • 11. Challenges User stories Inability to prioritize Documentation No documentation produced Micromanagement Focus is on capacity maximization
  • 13.
  • 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
  • 19. Challenges Product Owner Burnouts Appraisal Promised vs Delivered Budgets Follow the money
  • 20.
  • 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
  • 24. Show me the Benjamins!
  • 25. Good things Objective Team’s laser focus on outcome Freedom All resources are lined up Business growth Revenue generation activities
  • 26.
  • 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