The Art & Science of Looking at the FutureD96B0887-4D81...Millennial Household Formation Gen Z...

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The Art & Science of

Looking at the Future

Garry Golden

January 29, 2020

Themes of Change

Warm upForesight

Discussion Next Steps

SESSION FLOW

Next ten years

Last ten years

2009 – 2019 2020-2030

WARM UP: MORE OR LESS CHANGE AHEAD

2009 2019

Why Software is

Eating the World…

THE SLOW PACE OF FAST CHANGE

Our ability to develop solutions based on…

❑ Hindsight❑ Insight ❑ Foresight… the ability to anticipate and lead change

APPLYING THREE VIEWS ON CHANGE

FORESIGHT

HOW TO FUTURE-PROOF OUR SOLUTIONS

Dominance of Model

Horizon 1

2020 2027 2035

Horizon 2 Horizon 3

Three Horizon Model of Change

WHAT FORCES INFLUENCE OUR FUTURE SOLUTIONS

Trends

Choices

Events

Plausible Future

Possible Futures

Preferred Future

Forecasts

Scenarios

Visions

Warm upForesight

Discussion Next Steps

Themes of Change

CHALLENGE OF TALKING ABOUT THE FUTURE

Addressing Social Foundations Feeling ‘Futuristic’

Self-Work on Equity & Justice

Purpose, Happiness vs Helplessness

Imperative of Being Local

Globalization

Techno-Solutions Empowered Self

Growth

Transportation & Mobility

THE FUTURES OF…

Changing Nature of Work

Civic Engagement

DemographicTransitions

DEMOGRAPHICS AS DESTINY

Sector Implications for:

Rural vs Urban Labor Markets Education and Labor

Industry Make-up Infrastructure Spending Electorate Sentiment

POLICY & SOCIALS NORMS IN TRANSITION

Source: populationpyramid.net

Demographic Dividend

METRO VS RURAL IMPLICATIONS

Metropolitan Pyramids Rural & Small Town Pyramids

METRO & RURAL ELECTORATESHennepin

Dakota

Koochiching

Renville

MINNESOTA’S DEMOGRAPHIC DESTINY

Source

Demographic Storylines

❑ Aging Populations

❑ Millennial Household Formation

❑ Gen Z College + Careers

❑ Diversity

❑ Economic Bifurcations

❑ Urban (Metro)

❑ Small Town / Rural

AGING POPULATIONS

❑ Around 2020, Minnesota's 65+ population is

expected to eclipse the 5-17 (K-12) population, for

the first time in history.

❑ By 2030, 1 in 5 Minnesotans will be an older adult.

LEARNING CURVE: AGE-FRIENDLY COMMUNITIES

Joined in 2016

“Aging-friendly” communities are communities that provide affordable, accessible

housing, multiple modes of transportation, access to community services, and

opportunities for engagement for all residents, regardless of age or ability.

SOLUTIONS FOR AGING IN PLACE

CEAM: UNIVERSAL BENEFITS OF AGING-FRIENDLY COMMUNITIES

Mobility &

Walkability

Co-location

of Services

Public Social

Experiences

Transportation & Mobility

THE FUTURES OF…

Changing Nature of Work

Civic Engagement

DemographicTransitions

ANTICIPATING TRANSITIONS

Incremental Innovation

within an Era

Transformational Innovation

across a New Era

S-CURVE ERA TRANSITIONS

Slow Change‘Emerging’

Next Era Disruptions

Rapid Change‘Accelerating’

Plateau of Change‘Diminishing Returns’

ERAS OF MOBILITY

Muscle

Automobile

Boat

Aviation

What is next?

❑ Electric Vehicles

❑ Autonomous

❑ Low-Volume Production

❑ Sub-orbital Space

Rail

FORESIGHT: MONITORING SIGNALS OF CHANGE

THE FUTURES OF MOBILITY

Electrification Autonomous Micro-mobility

City Engineering Implications:

Funding (Fuel/Use); Types of Vehicles; Infrastructure; Stakeholders

DEFINING ELECTRIFICATION

The Missing Message

Electric = the Motor

Electrons Molecules

What will govt mandate…?How will OEMs respond …?

Uncertainties inElectrification of Vehicle Fleet

STAGE ONE = FRAGMENTATION

Fuel-based EVsPlug-in EVs Hybrid ICEs

Thinking Beyond Passenger Vehicles:

Rail Marine Trucking Aviation/UAVs

Autonomous Last Mile / Micro Transit Robotics

DEBATING: MARATHON NOT SPRINT

… Elon Says Game-OverBatteries have Won!

… meanwhile OEMs betting onintegration & fuel-based EVs

More than three-quarters of executives (78% global; 82% China; 85% U.S.) say fuel-cell electric mobility will be the real break-through for electric mobility.

OEMs: LONG GAME, DIVERSE NEEDS

BEV CHALLENGES TO SCALE

OEM Cost-to-X

vs Daily Use Demand

Uptime for Fleets &

Recharging in Urban

Markets

Full Costs of

Grid Management

‘Duck Curve’ to ‘Dragon Curve’ Battery pack = 400 miles

Daily Need = 40 miles

EVs MEET VARIABLE RENEWABLES

‘Duck Curve’ ‘Dragon Curve’

WE WILL BUILD OUT FOR BEVs

Planning for BEVs❑ Fleet / Workplace

Charging Networks

❑ Business Models

+ Rate Design

❑ Policies for

Controlled Charging

❑ Incentive Models

❑ Selling Infrastructure

to Public (Parents)

35

V2G Vision = EVs as Dispatchable Energy

Austin Sustainable and Holistic Integration of Energy Storage and Solar Photovoltaics (SHINES)

VISION OF VEHICLE TO GRID (V2G)

THE ‘OTHER’ PATH TO ELECTRIFICATION

Electrons Molecules

Marine

UAVsHydrail

Trucking

OEM: COST CURVE; SUPPLY CHAIN SIMPLICITY

GERMANY: INFRASTRUCTURE COST COMPARISON AT SCALE

SCENARIO: DIGGING UP FOR INTRA CITY H2 PIPELINES

40

Fueling EVs

STUDIES: EVs & FUTURE OF CITY PIPELINES

Fueling EVs

WIND TO HYDROGEN VIA ‘POWER TO GAS’

https://www.diigo.com/user/garrygolden/ptg

CEAM: LEADING AN INFORMED CONVERSATION

Electrons Molecules

City-State Infrastructure ❑ Address Decline of Funding

(Fuel charge vs Use-Charge)

❑ Anticipate Market Dynamics Shape FleetsLong-term Vehicle Cost Curve (kW) Battery $80-100 kW (at volume)Fuel Cells $20-30 kW (at volume)

❑ Policies Need to Address: Total Cost of System Management not just Total Cost of Ownership

THE FUTURES OF MOBILITY

Electrification Autonomous Micro-mobility

City Engineering Implications: Funding (Fuel/Use); Types of Vehicles; Infrastructure; Stakeholders

THE SLOW PACE OF FAST CHANGE

Autonomous❑ Multi-decade Transition❑ ‘Campus’ & Point to Point❑ Transit Systems ❑ ‘Flow’ & ‘Safety’ > Cool

SCENARIO: PLACE AS A SERVICE

SURUS Platform Silent Utility Rover Universal Superstructure

SCENARIO: CITIES AS COORDINATOR

TO DO: ANTICIPATE DATA COORDINATION

TO DO: ‘HUMANIZING’ AUTONOMOUS SYSTEMS

TO DO: PREPARE FOR HACKERS

BLOOMBERG-ASPEN INSTITUTE RESEARCH

http://avfutures.nlc.org/

THE FUTURES OF MOBILITY

Electrification Autonomous Micro-mobility

City Engineering Implications: Funding (Fuel vs Use); Types of Vehicles; Infrastructure

IS THE BIG STORY MICRO-MOBILITY?

MICRO-MOBILITY SKEPTICS

MICRO-MOBILITY BIFURCATES

Deliver Robot

Take Over

Form Factor

vs Service Model

Seasonal

Dynamics

Transportation & Mobility

THE FUTURES OF…

Changing Nature of Work

Civic Engagement

DemographicTransitions

DATA-DRIVEN INNOVATIONS

What might be the

most valuable types

of data in our future?

How might data, advanced

analytics and AI transform the

city engineer experience?

In the News

IN THE NEWS

True False

Harvard Business School is piloting a program with Experience.ai to capture experience data from learning, project performance and decision processes within case study groups. Harvard’s vision is for every student to retain rights to experience data and build a critical personal digital asset for the future.

Context of the Creepy Line

Data driven Workflows around the..

THEME: DATA DRIVEN COLLABORATION

Social Data

Health Data

Learning & Doing

Experience Data Device +

Infrastructure

“I did this…”

Course Real World

3 hours 300 hours

“I did this…”Activity Statement Capture

Assumption for 2020s: Experience Data emerges inside workplace as our most valuable dataset

ORGANIZATIONS NEED ‘DOING DATA’

EXPERIENCE DATA SHOWS OUR JOURNEY

❑ Sarah read an article on blockchain for automating compliance

❑ Sarah opened an Evernote folder on blockchain solutions

❑ Sarah watched a Youtube video introducing the Ethereum blockchain

❑ Sarah searched for Ethereum Meetups in NYC

❑ Sarah attended the Crypto Compliance conference in NYC

❑ Sarah created a List of ‘Ethereum Developers’ (People) on Twitter

❑ Sarah interviewed the Head of Blockchain Solutions at JPMorgan

❑ Sarah mentored with Joe Lubin co-Founder of Consensys

❑ Sarah demonstrated her pilot Ethereum application at a NYC Meetup

❑ Sarah taught a Coursera MOOC on Ethereum for KYC / AML

❑ Sarah was hired as Developer of Blockchain Compliance Solutions at Fidelity

“You can’t manage what you don’t measure.”

CAPTURING COLLABORATION DATA AT SCALE

L&D TEAMS CAPTURE DATA ACROSS TEAMS

LEARNING RECORD STORE (LRS): ‘DOING DATA’ FUNNEL

Source

By 2030 will we capture the doing data of city engineering work? … project partner work?

BY 2027, EXPERIENCE DATA = PRIZED DIGITAL ASSET

Social Data Experience Data

HOW MIGHT WE MAKE SENSE OF IT?

Tables = Past Graph Thinking = Future

Bob

Actors Meetup

Alice

Boston

Theatre Music

Review

Liked

Liked

Node

Relationship

Node

RISE OF GRAPH ANALYTICS

EXPERIENCE GRAPHS: UNDERSTANDING THE JOURNEY

Mirrors Real World Profiles, Pathways & Outcomes

Actor verb noun

I did this…

SIGNAL FROM PRODUCT DEVELOPERS

SIGNAL: MILESTONE TO ALIGN GRAPH VENDORS

RISE OF KNOWLEDGE GRAPH ANALYTICS

Knowledge Graphs Data finds Data Help turn data into knowledge so humans and machines may understand the nature of entity connections and relationships.

Use cases in search, NLP, security, recommendations, training and other AI-driven applications

KNOWLEDGE GRAPHS = CONNECT SILOS

❑ Transparency in CollaborationWe expect experience data to help city engineers collaborate, design, budgeting, supervise and manage projects.

❑ Recognize Potential Creepy Line Concerns Include plans to address issues of privacy/compliance and how we might develop policies on ownership vs access of employee experience data on city projects and beyond.

COMMUNICATE VALUE OF EXPERIENCE DATA

LEARNING MORE

Graph Analytics Experience Analytics Learning Record Stores

Follow Signals via Garry’s Social Bookmarks (‘Tags’)

❑ http://diigo.com/user/garrygolden/graph

❑ https://www.diigo.com/user/garrygolden/knowledgegraph

❑ https://www.diigo.com/user/garrygolden/xapi (ExperienceAPI)

Transportation & Mobility

THE FUTURES OF…

Changing Nature of Work

Civic Engagement

DemographicTransitions

ERAS OF MEDIA & COMMUNICATION

One to One

Broadcast: One to Many

What is next?

❑ AI Assistants

❑ IoT – Device to Device

❑ Deep Fakes

Social: Many to Many

DO MORE OF THIS!!

VALUE CHAIN OF PROJECT/COMMUNICATION DATA

Roadmap for Products and Business Intelligence ❑ Descriptive Analytics

Reporting Tools; KPI Dashboards

❑ Predictive AnalyticsForecasting; Decision Support

❑ Prescriptive AnalyticsGuiding Outcomes Suggesting Intervention Complexity of Relationship Management

Val

ue

Off

eri

ng

Descriptive AnalyticsWhat happened…

Predictive AnalyticsWhat might happen…

Prescriptive AnalyticsWhat should happen…

PUBLIC PLANS EMBEDDED W/ DATA SCI TOOLS

How might our public plans integrate data science tools that help stakeholders understand data, models and simulations?

CIVIC ENGAGEMENT VIA DATA SCIENCE TOOLS

Preparing for Horizon 2 & 3 Integrating our public plans with platforms, tools and widgets used to work with data and reveal how models and algorithms generate insights into our work and community needs.

THANK YOU

garrygolden@gmail.com

Launch Conversation on Who do we want to be…?

“I” Shaped PersonSuccess via Specialization

“T” Shaped PersonSuccess via Integration

What do we want to be as T-Shaped Individuals?

“T” ShapedProfessional Community

Also Trained in ….?

Psychology Data Science Crypto / Blockchain Restorative Practice _________________________________

Ethics Behavior Science Cyber Security Aging Systems ThinkingExperience Design Service Design

In Five Years…

In five years

… what is a function, department or role that does not exist today but will deliver our most innovative solution?

… which non-traditional organization becomes our most valued partner?

Generate weekly questions that spur conversations about the future of your organization.

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