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DEMYSTIFYING INNOVATION FOR
MATURE ORGANIZATIONS:
A SILICON VALLEY PERSPECTIVE ON INNOVATION
CULTURE
IKHLAQ SIDHU
Founding...
My Perspective:
Sutardja Center for Entrepreneurship & Technology
College of Engineering, UC Berkeley
Approach Berkeley Me...
LOTS OF
ACTIVITY
CHALLENGE
LAB
GLOBAL
PROFESSORS
NZTV
SELF-DRIVING
COLLIDER
DATA-X CHATBOT COLLIDER SCET IN TAIWAN
JOHN BA...
• DETECTION OF FAKE NEWS
• PREDICTION OF LONG-TERM ENERGY PRICES
TO SOLVE WALL STREET PROBLEM
• PREDICTION APPLICATIONS ST...
Harrah’s Casino:
Knowing your customer
Service provider of
Gambling and
Casinos
Entry Card
Pain points
Intervention
Refere...
1. Knowing your customer, better targeting and relationship.
E.g. Target, Disney, Netflix
2. Improving physical product or...
Top Business Models for Using Data
1. Knowing your customer, leading to
better targeting and relationship.
E.g. Target, Di...
Data-X in
2-4 Day Formats
• Technical Workshops
for Leaders
• Data/AI Strategy for
Management
• Planned:
• Hong Kong (Fint...
Innovation Journeys
Experimentation
Adaptation / Pivots
Learning
Searching
Working
Business
Model
Scale
Operations
Measure...
Early stage projects
have more unknown
variables.
Early stage = higher
risk and higher
expected reward.
Searching Phase Sc...
Some companies have been
able to adapt and transform
while others were not
Technical Drivers:
• Data
• Algorithms
• Roboti...
Some companies have been
able to adapt and transform
while others were not
Technical Drivers:
• Data
• Algorithms
• Roboti...
Identify
the Stage
of Your
Product
Business Investment Readiness
System Test,
Launch, Ops
Technical Readiness
System/Subsy...
What is the path for
transformation or
business model change?
Adding, Letting Go, and
Change Management
Dimension B
Dimens...
The Greater Silicon Valley: An Innovation Accelerator
My other perspective is
Silicon Valley
Silicon Valley – What is it like?
• Approx. 6M people
• 400,000+ tech jobs (~25% of the work force)
• More millionaires & ...
What Allows a Firm to Adapt
Innovation Leadership 3 Leadership sets
culture
Culture for operations and/or Innovation 2 Cul...
Our model has adapted:
Business training is not
the only key element
Our effectiveness formula is:
• Depth in an valued ar...
End of Section
IKHLAQ SIDHU
Sutardja Center for Entrepreneurship & Technology, IEOR, UC Berkeley
sidhu@berkeley.edu
scet.b...
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Demystifying Entrepreneurship for Mature Organizations (Germany)

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Demystifying Entrepreneurship for Mature Organizations”, Europa Park, Baden-Wurttemberg, November, 2017

Published in: Technology
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Demystifying Entrepreneurship for Mature Organizations (Germany)

  1. 1. DEMYSTIFYING INNOVATION FOR MATURE ORGANIZATIONS: A SILICON VALLEY PERSPECTIVE ON INNOVATION CULTURE IKHLAQ SIDHU Founding Director Sutardja Center for Entrepreneurship & Technology IEOR Emerging Area Professor Department of Industrial Engineering & Operations Research, UC Berkeley
  2. 2. My Perspective: Sutardja Center for Entrepreneurship & Technology College of Engineering, UC Berkeley Approach Berkeley Method: ØEntrepreneurship ØInnovation Leadership
  3. 3. LOTS OF ACTIVITY CHALLENGE LAB GLOBAL PROFESSORS NZTV SELF-DRIVING COLLIDER DATA-X CHATBOT COLLIDER SCET IN TAIWAN JOHN BATTELLE FOUNDER WIRED MAGAZINE NEWS COVERAGE 14 Courses 1600+ Undergraduates 80+ Ph.D / Graduate Students 100+ Executives 12+ Global Partners MARRISA MAYER
  4. 4. • DETECTION OF FAKE NEWS • PREDICTION OF LONG-TERM ENERGY PRICES TO SOLVE WALL STREET PROBLEM • PREDICTION APPLICATIONS STOCK MARKET, SPORTS BETTING, AND MORE • AI FOR CRIME DETECTION, TRAFFIC GUIDANCE, MEDICAL DIAGNOSTICS, ETC. • A VERSION OF ZILLOW THAT IS RECALCULATED WITH THE EFFECTS OF AIRBNB INCOME AND MANY MORE… My newest course: IEOR 135 Applied Data Science with Venture Applications Sample Data-X Projects
  5. 5. Harrah’s Casino: Knowing your customer Service provider of Gambling and Casinos Entry Card Pain points Intervention Reference: Supercrunchers
  6. 6. 1. Knowing your customer, better targeting and relationship. E.g. Target, Disney, Netflix 2. Improving physical product or servicer with complimentary information: E.g. UPS, FedEx 3. Data-driven reliability or security E.g. GE, BMW, Siemens 4. Information Brokers, Arbitrage, and Trading Opportunities: E.g. Investment funds. 5. Improving the customer journey/experience.. E.g. Harrah’s 6. Functional Applications: HR/Hiring, Operations etc.. Eg Walmart, Baseball, Sports 7. Efficiency or better performance per dollar cost. E.G. General IT, SAP, etc 8. Risk Management, regulation, and compliance Eg. Compliance 360 Top 8 Business Models Using Data
  7. 7. Top Business Models for Using Data 1. Knowing your customer, leading to better targeting and relationship. E.g. Target, Disney 2. Information based better services. E.g. UPS, FedEx 3. Data driven reliability. E.g. GE and Siemens 4. Information Brokers, Arbitrage, and Trading Opportunities: Investment funds. 5. Improving the customer journey/experience.. E.g. Harrahs 6. Functional Applications: HR/Hiring, Operations etc.. 7. Efficiency or Better Performance per dollar cost 8. Risk Management, regulation, and compliance Usage Models • Efficiency (save money) • Wallet Share (top customers spend more time and money with you) • Brand alignment (It reinforces how people think positively about the company) Value to Business Customers More Value
  8. 8. Data-X in 2-4 Day Formats • Technical Workshops for Leaders • Data/AI Strategy for Management • Planned: • Hong Kong (Fintech) • Prague • Philippines • Silicon Valley class Master TECHNICAL LEADERS for LEARN CUTTING EDGE TECHNOLOGIES IN AI, DATA SCIENCE & MACHINE LEARNING. Data X A Framework for Harnessing Technologies and Algorithms aligned with Business Strategy
  9. 9. Innovation Journeys Experimentation Adaptation / Pivots Learning Searching Working Business Model Scale Operations Measures Executing Disruption
  10. 10. Early stage projects have more unknown variables. Early stage = higher risk and higher expected reward. Searching Phase Scaling Phase The best people in each phase of innovation are different Characteristics of People in the Search Phase Characteristics of People in the Scaling Phase Skills Experimentation, adaptation, learning customer + technology Scale, operations, measures, accounting Motivation Change the world Don’t deviate from a working process Characteristics Comfortable with unknowns Likes plans, avoid unknowns
  11. 11. Some companies have been able to adapt and transform while others were not Technical Drivers: • Data • Algorithms • Robotics • Network Connectivity Structural Drivers: • Business Model Adaptation • Shorter Cycles Adapted Disrupted
  12. 12. Some companies have been able to adapt and transform while others were not Technical Drivers: • Data • Algorithms • Robotics • Network Connectivity Structural Drivers: • Business Model Adaptation • Shorter Cycles Adapted Disrupted • Did “they” get it. Culture, external awareness, learning behaviors. • Did “they” get it. Alignment: Top vs Middle • Timing: over-compensate vs denial • Have alignment, but cannot execute (tactical) • Have alignment, but have challenges with Acquisitions
  13. 13. Identify the Stage of Your Product Business Investment Readiness System Test, Launch, Ops Technical Readiness System/Subsystem Demonstration Development Progress Feasibility Research Insight Story / Value Validatio n Business Model Sales Process Complete Ecosystem Operational Metrics X Y Z
  14. 14. What is the path for transformation or business model change? Adding, Letting Go, and Change Management Dimension B Dimension A X Z Company or Project: Today Company or Project: Next
  15. 15. The Greater Silicon Valley: An Innovation Accelerator My other perspective is Silicon Valley
  16. 16. Silicon Valley – What is it like? • Approx. 6M people • 400,000+ tech jobs (~25% of the work force) • More millionaires & billionaires per capita than anywhere else in US and Europe • Thousands of experienced entrepreneurs. • 50% of all venture investing in the US • 2,000+ angel investors, 29 of the 100 largest US companies have HQ in SV • Decisions are made at lightning speed • Follow up in hours, not days or weeks Data from USMAC
  17. 17. What Allows a Firm to Adapt Innovation Leadership 3 Leadership sets culture Culture for operations and/or Innovation 2 Culture supports tactics Story / Adaptation Ecosystem Operational Innovation Financial Innovation or Diversity with filters 1 Tactics and process: Everyday activities Three Layers That Effect Innovation in an Existing Organization
  18. 18. Our model has adapted: Business training is not the only key element Our effectiveness formula is: • Depth in an valued area • Entrepreneurial “behaviors and mindset” Our programs and projects provide this. Our model has adapted: Business training is not the key element Skill in a Core Area Innovation Behaviors and Mindset “Psychology of Innovation” High Potential Too Narrow Street smart, but lacking depth
  19. 19. End of Section IKHLAQ SIDHU Sutardja Center for Entrepreneurship & Technology, IEOR, UC Berkeley sidhu@berkeley.edu scet.berkeley.edu

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