The document discusses the challenges and strategies of fostering innovation within mature organizations, emphasizing the importance of a conducive culture and utilizing emerging technologies. It highlights various applications of data science and innovation models, showcasing successful case studies in improving customer relationships and operational efficiency. Additionally, it compares innovation dynamics in Silicon Valley with broader organizational practices, suggesting that adaptability and the right leadership are crucial for effective transformation.

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. My Perspective:
Sutardja Center for Entrepreneurship & Technology
College of Engineering, UC Berkeley
Approach Berkeley Method:
ØEntrepreneurship
ØInnovation Leadership

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. • 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. Harrah’s Casino:
Knowing your customer
Service provider of
Gambling and
Casinos
Entry Card
Pain points
Intervention
Reference: Supercrunchers

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. 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. 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. Innovation Journeys
Experimentation
Adaptation / Pivots
Learning
Searching
Working
Business
Model
Scale
Operations
Measures
Executing
Disruption

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. 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. 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. 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. 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. The Greater Silicon Valley: An Innovation Accelerator
My other perspective is
Silicon Valley

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. 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. 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. End of Section
IKHLAQ SIDHU
Sutardja Center for Entrepreneurship & Technology, IEOR, UC Berkeley
sidhu@berkeley.edu
scet.berkeley.edu