This keynote presentation discusses how the rise of the Internet of Things (IoT) is changing the nature of certain products by enabling them to build large ecosystems and complements that elevate them from the mundane to the strategic. This has important implication for energy and energy efficiency given broader forces that are reshaping the energy landscape, namely the rise of denser networks (physical, grids, pipelines, fiber, etc.), growth in digital information and the opportunity for new forms and power of analytics and the shift to platform business models that harness network effects by building large ecosystems and incentivizing complements that increase the value of the platforms. Linked to this is the rise of the API Economy, which is creating a new ways to exchange valuable information. In short, a new “energy data layer” is emerging with powerful implications for the future energy intelligence, productivity and efficiency.

1. energy intelligence
Peter Evans, PhD
Vice President
Center for Global Enterprise
Photo by Maria Carrasco Rodriguez
Rise of the Data Layer
2015 SEEA & AESP Southeast
Conference
Atlanta, GA
October 28-30th, 2015

2. What do these items have in common?
2
Essential items… but lack ecosystems  not strategic

3. 3
> 8,500 apps
How about these items?
>11,000 developers have
accessed Nest’s APIs
> Billions of calls
Essential items… growing ecosystems  strategic

4. IoT is changing the fundamental nature of certain products
From Necessary but Mundane  Strategic
4

5. Path to the Internet of Things (IoT)
Mainframe
PC
Web
Connected
Client/ APIs
Standard narrative of evolutionary change

6. Energy: Complex forces of change
Age of
Platforms
New business models that
achieve that leverage
networks and intelligence
Age of
Networks
Mesh networks
linking physical,
digital and social
Digital Age
Surge in information
about energy for
insight and improved
decision-making
Energy

7. Source: Rahul Basole and Peter Evans, API Economy
Visualized, Center for Global Enterprise, 2015
Rise of the API Economy
7

8. API Economy: Amazon vs. Walmart
Source: Peter Evans and Rahul Basole, with data from
ProgrammableWeb, Center for Global Enterprise, 2015
Social media / web
Job search / work
E-commerce
Tools / analytics / big data
Payments
API Clusters
Messaging services
Walmart
Amazon
Companies
Enterprise
Amazon SNS
Alexa Web Inform
Amazon
Marketplace
Amazon
SimpleDB
Amazon Product
Advertising
Amazon
CloudWatch
Amazon
Flexible
Amazon
Redshift
Amazon SC2
Amazon S3 Amazon
Mechanical TurkAmazon RDS
Amazon DynamoDB Amazon Queue Service
Walmart
8

9. Rise of IoT
Information Infrastructure Companies
9

10. IoT information infra companies
New locus of value creation, capture and competition
Agriculture
Physical Layer
Energy
Physical Layer
Healthcare
Physical Layer
Digital Layer Digital Layer Digital Layer Digital Layer
Transportation
IoT information infrastructure companies
Physical Layer

11. Energy and the data layer
Intelligence about energy is dramatically expanding
Source: John Canny, “Designing with Data”, UC Berkeley, EECS, July 2013
11
1. Volume and velocity of data growing at
- machine level
- facility level
- fleet level
- network level
New dynamics
2. Expanded monitoring/automation
3. Shift from the reactive to the predictive
4. Experimentation with app stores
5. Many more players in the ecosystem

12. Open API mashups
Source: Peter Evans and Rahul Basole with data from
ProgrammableWeb, Center for Global Enterprise, 2015
Energy and sustainability APIs lag social, mapping and images
Social Mapping Images Energy Sustainability
Currently there are very
few open API mashups
focused on energy and
sustainability

13. Age of Platforms
13

14. Business Models—Platforms
Platform companies are found in a growing number of industries
Source: CGE Platform Database, 2014
CompaniesSectors

15. Power of network effects
1
connection
Value of the system increases with more users
2 phones
10
connections
5 phones
66
connections
12 phones

16. Platform business
16
Expanding value through matching, interaction and innovation
Platform
Innovation
Software
developers MatchingSupply +
Demand
Interaction
Ecosystem

17. Street lighting
applications
platform
Building
efficiency
platform
Emerging energy platforms
17

18. 18
Race to build new platform ecosystems
Apr Jul Oct Jan Apr Jul Oct Jan Apr
2013 2014 2015
Austin Energy*
Green Mountain*
National Grid*
Reliant*
Southern California Edison*
Infinite Energy
Columbia Gas Ohio
ComEd
Npower
CPS
Energy
Direct
Energy
CamHydro
Essent
Lampiris
Electric
Ireland
Hydro
One
Bounce Energy
ConEd
SolarCity
Source: Media, press releases and Nest website: https://nest.com/energy-partners/
* Subsidiaries of NRG
**Rush Hour Rewards and Seasonal Savings
Nest Labs grows its utility partnerships**

19. $5.8 billion
Innovation on the energy data layer

20. Startups… energy data layer players
Energy supply/ management
Home automation
Distributed energy management
Data/ Analytics
Energy Intelligence
Building automation
Startup Clusters
Source: P. Evans, CGE with data and visualization
powered by Quid, 2015
Top 50 Companies-- $5.8 Billion… VC funding, IPOs and M&A
OpTerra Energy Group
Crowd ComfortBuildingIQ

21. Wave of “energy intelligence” startups
Companies building value with energy information
Source: P. Evans, CGE, 2015

22. Applications

23. 23

24. Macro approaches
1
Determine key building parameters
and begin load disaggregation.
DETECT ATTRIBUTES
Generate unique models of how the
buildings is, and could be, performing.
2 CREATE ENERGY MODELS
Compare building to efficient model.
3 COMPARE PERFORMANCE
Target best prospects, automate audits and track efficiency savings
Data Sources: Meter + Weather + Building info
Source: Retroficiency, MIT Platform Strategy Summit, July 2014
Analytic steps

25. Micro approaches
New platform solutions are emerging that can efficiently gather the
sensor data from humans, improving information flows between building
occupants and facility managers, boosting comfort and productivity.
Building occupants
report conditions
Source: CrowdComfort, MIT Platform Strategy Summit, July 2014
Tapping “human-based” sensor technology
Facility managers receive
aggregated “comfort reports”

26. Energy efficiency’s new golden age?
Admonishment
Shift from …. Automation
1970s
Today
Yesterday and today…

27. IoT speed and scale
Jeff Immelt, GE Minds & Machines
conference, San Francisco, Nov. 2012
Tim Cook, Apple Special Event,
San Francisco, Sept 2014
Consumer Internet is ahead… Will the Industrial Internet Catch up? When?

28. Platforms and the 21st Century Enterprise
Example
companies
Platform
ecosystem
Hierarchal
organization +
physical assets*Structure
Asset Heavy
Daimler Moovel
Johnson Controls Panopix
GE Predix
Samsung Tizen
Asset Light
Google Google Play
Uber Uber app
Airbnb Airbnb app
Salesforce AppExchange
* Includes HQ, other rooftops, retail outlets, manufacturing plants, service shops, etc.
Platform

29. Age of networks, digital and platforms
Revenue $ 30 trillion
Assets $121 trillion
# Employees 64.8 million
Source: Fortune Global 500 2013 and Center for Global Enterprise.
Countries
Collective Size
Uncertain implications for the world’s 500 largest firms

30. Future of energy
1. Growing importance of the energy data layer
2. Shift in value capture from assets to data
3. Ability to scale across markets/ service territories
4. Power of platforms to harness growth
Harness the forces of change…

31. 31
Nov. 10th, 2015
Book Launch
Event
Columbia
Business School
from 9:30am to 11am
New York City
Growing Global examines
the challenges and
opportunities in today’s
global economy and offers
practical lessons that will
help prepare present and
future business leaders
for the next phase of the
new enterprise.
Growing Global book launch

32. energy intelligence
Peter Evans, PhD
Vice President
Center for Global Enterprise
Photo by Maria Carrasco Rodriguez
Rise of the Data Layer
2015 SEEA & AESP Southeast
Conference
Atlanta, GA
October 28-30th, 2015