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

Paving the Way Through Radical Collaboration

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

Delivery Zone Availability

Enhanced PermitParking

ParkingManagement

Truck Platooning

Oversize Vehicle Routing

Interstate Truck Parking Availability

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

USDOT program

Network Data Applications

Smart Columbus: From an IT Point of View

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

Example Technical Reference Architecture

Flexible

Replicable

Portable

Scalable

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