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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
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.
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.
