DEMYSTIFYING INNOVATION FOR MATURE ORGANIZATIONS: A SILICON VALLEY PERSPECTIVE ON INNOVATION CULTURE

IKHLAQ SIDHUFounding DirectorSutardja Center for Entrepreneurship & TechnologyIEOR Emerging Area ProfessorDepartment of Industrial Engineering & Operations Research, UC Berkeley

My Perspective:Sutardja Center for Entrepreneurship & Technology

College of Engineering, UC Berkeley

Approach Berkeley Method: ØEntrepreneurshipØ Innovation Leadership

LOTS OF ACTIVITY

CHALLENGE LAB

GLOBAL PROFESSORS

NZTV

SELF-DRIVINGCOLLIDER

DATA-X CHATBOT COLLIDER SCET IN TAIWANJOHN BATTELLE

FOUNDER WIRED MAGAZINE

NEWS COVERAGE

14 Courses1600+ Undergraduates80+ Ph.D / Graduate Students100+ Executives12+ Global Partners

MARRISAMAYER

• 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 ApplicationsSample Data-X Projects

Harrah’s Casino: Knowing your customer

Service provider of Gambling and Casinos

Entry Card

Pain points

Intervention

Reference: Supercrunchers

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 complianceEg. Compliance 360

Top 8 Business Models Using Data

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

Data-X in 2-4 Day Formats

• Technical Workshops for Leaders

• Data/AI Strategy for Management

• Planned:• Hong Kong (Fintech)• Prague• Philippines• Silicon Valley

classMaster

TECHNICAL LEADERSfor

LEARN CUTTING EDGE TECHNOLOGIES IN AI, DATA SCIENCE & MACHINE LEARNING.

Data XA Framework for Harnessing Technologies and Algorithms

aligned with Business Strategy

Innovation Journeys

ExperimentationAdaptation / Pivots

LearningSearching

WorkingBusinessModel

ScaleOperationsMeasuresExecuting

Disruption

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

Some companies have been able to adapt and transformwhile others were not

Technical Drivers:• Data

• Algorithms

• Robotics

• Network Connectivity

Structural Drivers:• Business Model Adaptation

• Shorter Cycles

Adapted

Disrupted

Some companies have been able to adapt and transformwhile 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

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

Validation

BusinessModel

SalesProcess

CompleteEcosystem

OperationalMetrics

X

Y

Z

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

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 & 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

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

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

End of Section

IKHLAQ SIDHUSutardja Center for Entrepreneurship & Technology, IEOR, UC Berkeley

sidhu@berkeley.eduscet.berkeley.edu