Post on 24-Feb-2019
Department of TechnologySam Orth, Chief Information Officer
From Open Data to Engaged CommunitySmart Columbus can pave the way through radical collaboration(AKA Pathfinding in a New Direction)
CIO Solutions GalleryThe Ohio State UniversityFebruary 23, 2017
Smart Cities: Multiple Entry Points• Smart Transportation
• Internet of Things
• Sensors, Sensors Sensors!
• IoT Management
• Intelligent Lighting
• Mobile Health Monitoring
• Data Sharing
• Wireless Vehicle Charging
• City Operations
• Vehicle to Grid
• Digitalized Signage & Information Kiosks
• Much, much more
McKinsey & Company $11.1 Trillion worldwide over the next 10 years
Multi-Modal Trip Planning ApplicationCommon Payment
SystemMobility Assistance for People with Cognitive
Disabilities
Smart Mobility Hubs
Smart Street LightingPedestrian Collision
Avoidance
Connected Electric Automated Vehicle
• Electric Autonomous Transit Shuttle• Automated Routes
• COTA to logistics• Transit center to corporate campus• Remote parking to retail center
15 Projects
Connected Vehicle
Environment
Smart Street
Lighting
Transit Pedestrian Collision
Avoidance System
Integrated Data
Exchange (IDE)
Multi-Modal Trip Planning Application
Common Payment System
Mobility Assistance for People with Cognitive Disabilities
Smart Mobility Hubs
Connected Electric Automated Vehicle
Delivery Zone Availability
Enhanced PermitParking
Event ParkingManagement
Truck Platooning
Oversize Vehicle Routing
Interstate Truck Parking Availability
ENABLING SYSTEMS & APPLICATIONS
DISTRICTS
CCTN
IDE
RESIDENTIAL
COMMERCIAL
DOWNTOWN
LOGISTICS
PUBLIC APPLICATIONS
DATA MGT PLATFORM
CONTROL SYST & APP
Smart Columbus Electrification Plan’s
(Vulcan Foundation) 5 Elements
12
PUBLIC APPLICATIONS
METRICS
METRICS
METRICS
PUBLIC APPLICATIONS
Electricity Supply
De-Carbonization
Fleet Electrification
(EVs)
Transit, Autonomous Vehicles,
& Multi-modal Systems
Consumer Adoption of
Electronic Vehicles
EV Charging Infrastructure
Smart Columbus: Transportation Focus• Dedicated short-range communications (DSRC)
• Short to medium range one or two-way telecommunications designed for automotive use
• 75MHz of spectrum in the 5.9 GHz band dedicated by FCC in 1999 for intelligent transportation systems
• DSRC was developed with a primary goal of enabling technologies that support safety applications and communication between vehicle-based devices and infrastructure to reduce collisions. DSRC is the only short-range wireless alternative today that provides:
•Fast Network Acquisition
•Low Latency
•High Reliability when Required
•Priority for Safety Applications Interoperability
•Security and Privacy
Smart Columbus: Transportation Focus
Vehicle to Vehicle (V2V)
I’m slowing down quickly,
slow down I’m sensing slippage alert
motorists here
Vehicle to Infrastructure (V2I)
LOCATION, DIRECTION &SPEED
41°25'01"N and 120°58'57"W 67.2MPH
DSRC BASIC SAFETY MESSAGE
20 TO 30X PER SECOND
Smart Cities: Infrastructure that supports a future of IoT
©2016 Silver Springs Networks, All Rights Reserved
SENSOR CONTROL FABRIC
SENSOR DATA MANAGEMENT PLATFORM
SENSOR MANAGEMENT & OPERATIONAL PLATFORM APPLICATIONS / THIRD-PARTY INTEGRATIONS
SENSOR NETWORKING PLATFORM
SENSORS & DEVICES
L7:6
L5
L4
L3:2
L1
McKinsey & Company $11.1 Trillion worldwide over the next 10 years
Smart Cities: Infrastructure that supports a Smart foundation• Sensor Gateway/Hat
• Traffic, Utility, Parking Meter Sensors Ctrl.
• Smart Utility or Lighting Pole
• Smart LED Lighting
• Safety or Traffic Cameras, Audible Detector
• DSRC Radio
• Environmental Sensors (Particulate, Wind, Precipitation)
• Cellular Radios
• Power
• Ethernet
• Fiber Connectivity
Evolution of the IT Industry – Change is the only constant
1960 1970 1980 1990 2000 2010 2016
`InternetInternet
Commercially-
viable corporate
enterprise
computing based
on Mainframe
computing – only
affordable by
largest school
districts and
governments
Emergence of
Midrange
computing
platforms
Time Sharing
Personal
Computing Era
Begins
Spreadsheets
Databases
Word Processing
Computing starts
to migrate out of
the Datacenter
Emergence of
LANs and Client-
server
applications
Beginning of
commoditization
of the PC
Emergence of
Internet-based
client-server
applications
Commoditized
PCs, LANS,
Servers, Storage
Emergence of
smart phones,
personal digital
assistants
Consumer devices
are ubiquitous –
personal
computing is finally
personal
User expectations,
of technology
services accelerate
Systems
integration is
transparent to the
customer in the
“cloud” but more
complex
The Internet of
Things
The network is the
application
Cloud-computing
Big-data
Network-devices
Always-connected
Wearable-
computing
Smart-vehicles
Cyber-Threats
User expectations
at an all time high
1981: Apple goes magnetic with its 5 MB
hard drive, $3,500
1995: Seagate introduces its 1GB
drive for $849
2007: Hitachi introduces the first TB
disk for $399
$700/MB
.002¢
Evolution of the IT Industry – Change is the only constant
1960 1970 1980 1990 2000 2010 2016
`InternetInternet
Commercially-
viable corporate
enterprise
computing based
on Mainframe
computing – only
affordable by
largest school
districts and
governments
Emergence of
Midrange
computing
platforms
Time Sharing
Personal
Computing Era
Begins
Spreadsheets
Databases
Word Processing
Computing starts
to migrate out of
the Datacenter
Emergence of
LANs and Client-
server
applications
Beginning of
commoditization
of the PC
Emergence of
Internet-based
client-server
applications
Commoditized
PCs, LANS,
Servers, Storage
Emergence of
smart phones,
personal digital
assistants
Consumer devices
are ubiquitous –
personal
computing is finally
personal
User expectations,
of technology
services accelerate
Systems
integration is
transparent to the
customer in the
“cloud” but more
complex
The Internet of
Things
The network is the
application
Cloud-computing
Big-data
Network-devices
Always-connected
Wearable-
computing
Smart-vehicles
Cyber-Threats
User expectations
at an all time high
PR
ESEN
TATI
ON
AP
PLI
CA
TIO
ND
ATA
Traditional N-tier Application Architecture •N-tier applications grew from the goal of developing applications focused on business process automation needed to run the organization
•Enterprise applications evolved for common business management needs
•General Ledger, accounts payable, accounts receivable, payroll, benefit management, invoicing, purchasing, asset management and others merged to form Enterprise Resource Planning platforms•Help desk, 311 and sales management applications morphed into service desk and customer relationship management platforms
•Niche solutions emerged for lines of business or or as operational control systems
•Traffic management•Utility billing (Water, Electricity, Gas)•Taxation collection•Building zoning and permitting•Fleet management and routing
AP
PLI
CA
TIO
N IS
TH
E D
ATA
Data Becomes Tethered To Their Applications • N-tier application growth results in federated environment of enterprise and point solutions with individual databases spread across the organization
• Attempts to solve data integration challenges have not been completely successful due to technical challenges, resource requirements, complexity and costs
•Application to application integration•Data Warehouses•Service Oriented Architectures
•Data management challenges emerge•Data definitions •Meta data•Data quality •Data ownership
•Business intelligence tools emerge to help make sense out of data (e.g. Cognos)•Enterprise consumption of data for decision support and application consumption remain challenged
Evolution of Chaos
Doc Mgt
Cloud Techs
EDI
Data Marts
Time Consuming
Costly
Complex
Transformation
XML / JSON
Flat Files
Logs
Industry
Standards
People & ProcessThanks to Informatica for their permission to use this slide© 2017 Informatica
INTEGRATED DATAMaster/Reference Data
(meta)Enterprise DW
Operational data stores
BUSINESS BRAINS & ANALYICS
DATA VIRTUALIZATIONQuery Engine
Semantic ModelingSecurity
APIs
ARCHIVAL
DATA LAKEStructured &
Unstructured Data
DA
TA V
ISU
LATI
ON
Untethering Data from its Database Data Management Platform: Order from Chaos
Goal: Enable data analytics and application consumption that create value for the public
INTEGRATED DATAMaster/Reference Data
(meta)Enterprise DW
Operational data stores
BUSINESS BRAINS & ANALYTICS
DATA VIRTUALIZATIONQuery Engine
Semantic ModelingSecurity
APIs
ARCHIVAL
DATA LAKEStructured &
Unstructured Data
DA
TA V
ISU
LATI
ON
ENG
AG
EMEN
T LA
YER
RESEARCH
APPLICATIONS
IDECOLS DMP
Partners
InsideCity-wide Collaboration
OutsidePublic Engagement
• Federal• State• City• NGO
Data As A Service: It’s not Open it’s not Private, it’s both
Data That Enables Safety – Creating safer street environments and
more effective safety services
Health – Reducing infant mortality, increasing
access to healthcare and improving the
environment
Livability – Better access to transportation
options and better access to information
Efficiency – Enabling city departments to
evaluate, publish and act on key performance
indicators
Prosperity – Encouraging investment and
improving economic conditions in targeted areas
Innovation – Providing data and analytics in
ways that allow for unplanned and unimagined
uses
Data-As-A-Service: An Organizational Regional Core Competency• Data is a line of business not a
place to store data• Without a focus on data
management, e.g. quality, data strategies fail
• Value from data comes from the ability to form the right context and questions around its use
• Data analytics is a core competency, not a piece of software
• A data-first strategy turns around the old n-tier data paradigm
Core IT Capabilities
1
2
3
4
5
6
7
Data Governance
Data Architecture
Data Quality
Data Context
Data Analytics
Infrastructure
Peo
ple
Pro
cess
Dat
a
Tech
no
logy
7
S
T
R
E
A
M
S
This is how we must think about the components of data management in the enterprise to derive meaning and value
Data Asset Development
A Data Management Example• Delivering accurate data for business operations and public use
311
Suzizie M Long
Gramble DriveSt. Louis Park, 55416
Phone: 952-542 -025
Taxation
Suzzy M Rapp
5353 Gamble Dr
Saint Louis Park, MN
Phone: 952-542-0257
Email: suzzyrapp@gmail.com
Suzzie M Long
Gramble DrSaint Louis Park, MN 55416
Phone: 952-542-025
Customer ID 12377
Suzzy M Rapp
5353 Gamble DrSaint Louis Park, MN 55416-1509Phone: 952-542-0257
Email: suzzyrapp@gmail.comCustomer ID722834
CleanseThanks to Informatica for their permission to use this slide© 2017 Informatica
311
Suzzie M Long
Gramble DrSaint Louis Park, MN 55416
Phone: 952-542-025
Customer ID 12377
Taxation
Suzzy M Rapp
5353 Gamble DrSaint Louis Park, MN 55416-1509Phone: 952-542-0257
Email: suzzyrapp@gmail.comCustomer ID 722834
Cleanse Recognize
A Data Management Example
Thanks to Informatica for their permission to use this slide© 2017 Informatica
311
Suzzie M Long
Gramble DrSaint Louis Park, MN 55416
Phone: 952-542-025
Customer ID 12377
Ohio Taxation
Suzzy M Rapp
5353 Gamble DrSaint Louis Park, MN 55416-1509Phone: 952-542-0257
Email: suzzyrapp@gmail.comCustomer ID 722834
Cleanse Recognize Resolve
Single Source Of Truth
Name: Suzzy M Rapp
Address:5353 Gamble DrSaint Louis Park, MN55416-1509USA
Phone: (952) 542-0257
Email: suzzyrapp@gmail.com
Ethnicity: Caucasian
Marital Status: M
Financial: Single Family Home
Suzzy M Rapp
5353 Gamble DrSaint Louis Park, MN55416 -1509USAPhone 952-542-0257 Email: suzzyrapp@gmail.com
EDW 12377SFDC 722834ACXIOM054597455
A Data Management Example
Thanks to Informatica for their permission to use this slide© 2017 Informatica
INTEGRATED DATAMaster/Reference Data
(meta)Enterprise DW
Operational data stores
BUSINESS BRAINS & ANALYTICS
DATA VIRTUALIZATIONQuery Engine
Semantic ModelingSecurity
APIs
ARCHIVAL
DATA LAKEStructured &
Unstructured Data
DA
TA V
ISU
LATI
ON
ENG
AG
EMEN
T LA
YER
RESEARCH
APPLICATIONS
Goal: Enable data-value supply chain through best-in-class data analytics and next-gen application development ecosystem
Goal: Enable data-value supply chain through best-in-class data analytics and next-gen application development ecosystem
ENGAGEMENT LAYER
RESEARCH
APPLICATIONS
PEOPLEPROCESS &
TOOLSTECHNOLOGY
REG
ION
AL
CA
PAC
ITY
B
UIL
DIN
G
Public-Private-Education Partnerships
GovernmentsColumbus PartnershipThe Ohio State UniversityAnchor InstitutionsIT Developers
DataCorps
Regional Data EcosystemRegional Cost/Benefit
Data ManagementData QualityData Security & PrivacyData Availability (Marts)Data Planning (Roadmaps)Semantics (APIs)Tools (Visulatization), Analytics)
Thank YouIf you want to change outcomes, you need to realize that outcomes are the result of systems. Not the computer systems, but the way people work and interact. And these systems are the product of how people think and behave. So, if you want to change outcomes, you have to change your systems, and to do that, you have to change your thinking.
John Morgan
City of ColumbusDepartment of Technology1111 East Broad StreetColumbus, Ohio 43205
Sam Orthhsorth@columbus.gov(614) 645-2550