SlideShare a Scribd company logo
1 of 13
Download to read offline
Citi Global T4I Accelerator Data and Analytics Webinar:
An open innovation initiative seeking to source tech solutions that promote integrity around the world
Global	Government	 Industry
Any use of this material without specific permission of IBM or Citi is strictly prohibited.
By: Marquis Cabrera
Global Leader of Digital Government Transformation
IBM Global Government Center of Competency
Date: April 6, 2017
Twitter: @MarquisCabrera
2
• Global Problems: Problem-Solution
• Global Data and Analytics Problems
• Global IBM-Cognitive Use Cases
• Data and Analytics 101
• Consider: Logic Model / Theory of Change
• Potential Data Sources: Open Data, Open APIs
• Types of Analytics: Predictive, Prescriptive, Descriptive, and Diagnostic
• Useful Links
Agenda
3
• Global Problems: Problem-Solution
• Global Data and Analytics Problems
• Global IBM-Cognitive Use Cases
• Data and Analytics 101
• Consider: Logic Model / Theory of Change
• Potential Data Sources: Open Data, Open APIs
• Types of Analytics: Predictive, Prescriptive, Descriptive, and Diagnostic
• Useful Links
Agenda
4
Introduction
5
Global	Data	and	Analytics	Problems
Lack	of	Talent
Data	Privacy
Data	Security
Lack	of	Data
Data	Integrity
6
Global	IBM-Cognitive	Use	Cases
North	America	– Alameda	County	
Social	Services	Agency	gives	case	
workers	tools	to	help	more	clients	
while	eliminating	$11	million	in	
fraud.	
North	Carolina	identifies	millions	in	
suspicious	 Medicaid	claims	using	
advanced	analytics.
South	America	– Rio	de	Janeiro	
improved	emergency	response	
time	by	30%	with	centralized	
command	center.
Europe	–Baden-Württemberg	Ministry	of	Integration	
improves	immigrant	integration	policies	through	
analytics.	
City	Region	of	Eindhoven	uses	existing	data	from	road	
and	car	sensors	to	keep	traffic	moving.
Swedish	Armed	Forces	improves	data	quality,	while	
migrating	millions	 of	legacy	data	records.
Africa	– Using	deep	analytics	and	specialized	algorithms	to	
translate	visual	data	received	from	CCTV	cameras	positioned	
around	Nairobi,	citizens	can	use	their	mobile	phones	to	
receive	updates	on	road	conditions	 and	suggestions	for	
alternative	routes.	With	only	36	cameras	currently	installed	
around	Nairobi,	IBM	researchers	have	augmented	data	
using	mathematical	network	analytics	allowing	the	system	
to	predict	traffic	in	parts	of	town	where	no	data	feeds	are	
available.
China	– Zhenjiang	builds	more	intelligent	transportation	
system		to	help	analyze	traffic	patterns,	obtain	and	
disseminate	real-time	traffic	updates,	and	anticipate	and	
minimize	traffic	issues	 for	this	city	of	three	million	people.
Middle-East	– IBM	is	collaborating	with	Dubai	Customs,	 Dubai	Trade to	explore	blockchain
solutions.	Using	Hyperledger	Fabric and	IBM	Cloud,	the	blockchain solution	 transmits	
shipment	data	allowing	key	stakeholders	to	receive	real-time	information	about	the	state	
of	goods	and	the	status	of	the	shipment.	Taking	the	example	of	a	shipment	of	fruit,	
stakeholders	involved	in	the	process	will	receive	timely	updates	as	the	fruit	is	exported	
from	India	to	Dubai	by	sea,	and	then	manufactured	into	juice	in	Dubai,	and	then	exported	
as	juice	from	Dubai	to	Spain	by	air.
7
• Global Problems: Problem-Solution
• Global Data and Analytics Problems
• Global IBM-Cognitive Use Cases
• Data and Analytics 101
• Consider: Logic Model / Theory of Change
• Potential Data Sources: Open Data, Open APIs
• Types of Analytics: Predictive, Prescriptive, Descriptive, and Diagnostic
• Useful Links
Agenda
Logic	Model	/	Theory	of	Change
Resources /
Inputs
Activities Outputs Outcomes Impact
54321
Certain
resources
(data, inputs,
talent,
compute tools)
are needed to
operate your
program.
If you have
access to
them, then
you can use
them to
accomplish
your planned
activities (i.e.
build app to
prevent fraud).
Your Planned Work
If you
accomplish your
planned
activities, then
you will
hopefully deliver
the amount of
product and/ or
service that you
intended.
If you
accomplish
your planned
activities, to
the extent you
intended, then
your
participants
will benefit in
certain ways.
If these benefits to
participants
achieved, then
certain changes in
organizations,
communities, or
systems might be
expected to occur
(i.e. cost savings,
lives saved)
Your Intended Results
Data	Resources:	Global	Open	Data,	Open	APIs
Open	
APIS
Open	
Data
Types	of	Analytics		
There are four types of analytics:
1. Prescriptive – This type of analysis reveals what actions should be taken. This is the
most valuable kind of analysis and usually results in rules and recommendations for
next steps.
2. Predictive – An analysis of likely scenarios of what might happen. The deliverables are
usually a predictive forecast.
3. Diagnostic – A look at past performance to determine what happened and why. The
result of the analysis is often an analytic dashboard.
4. Descriptive – What is happening now based on incoming data. To mine the analytics,
you typically use a real-time dashboard and/or email reports.
11
• Global Problems: Problem-Solution
• Global Data and Analytics Problems
• Global IBM-Cognitive Use Cases
• Data and Analytics 101
• Consider: Logic Model / Theory of Change
• Potential Data Sources: Open Data, Open APIs
• Types of Analytics: Predictive, Prescriptive, Descriptive, and Diagnostic
• Useful Links
Agenda
Useful	Links
• Project Open Data
• https://project-open-data.cio.gov/
• Watson Analytics IoT Lite (Free)
• https://www.ibm.com/blogs/bluemix/2016/12/watson-iot-platform-renamed-lite-plan/
• How We Built an IoT Application in 10 Days Using Watson IoT and IBM Blockchain
• http://bit.ly/2msml8J via
• Cognitive Government: Enabling the Data-Driven Ecosystem
• http://www-01.ibm.com/common/ssi/cgi-
bin/ssialias?subtype=ST&infotype=SA&htmlfid=GVJ03029USEN&attachment=GVJ03029USEN.PDF
• Lean Analytics
• https://blog.kissmetrics.com/lean-analytics/
• How To Learn an Industry From Scratch
• https://www.forbes.com/sites/spdr/2017/03/20/get-smarter-about-gender-
intelligence/#b58faeb2d08f
Identify User
Needs
Be Agile and
Innovative
Leverage
Global Platform
Government
As a Platform
MEET USER NEEDS
Use Open
Data & APIs

More Related Content

What's hot

Pikas using bibliometrics to make sense of research proposals
Pikas using bibliometrics to make sense of research proposalsPikas using bibliometrics to make sense of research proposals
Pikas using bibliometrics to make sense of research proposalsChristina Pikas
 
Differential Privacy Case Studies (CMU-MSR Mindswap on Privacy 2007)
Differential Privacy Case Studies (CMU-MSR Mindswap on Privacy 2007)Differential Privacy Case Studies (CMU-MSR Mindswap on Privacy 2007)
Differential Privacy Case Studies (CMU-MSR Mindswap on Privacy 2007)Denny Lee
 
Big data
Big dataBig data
Big data26Nia
 
CIRPA 2016: Individual Level Predictive Analytics for Improving Student Enrol...
CIRPA 2016: Individual Level Predictive Analytics for Improving Student Enrol...CIRPA 2016: Individual Level Predictive Analytics for Improving Student Enrol...
CIRPA 2016: Individual Level Predictive Analytics for Improving Student Enrol...Stephen Childs
 
Advancing Foundation and Practice of Software Analytics
Advancing Foundation and Practice of Software AnalyticsAdvancing Foundation and Practice of Software Analytics
Advancing Foundation and Practice of Software AnalyticsTao Xie
 
Data quality architecture
Data quality architectureData quality architecture
Data quality architectureanicewick
 
Using Bibliometrics to Keep Up with the Joneses
Using Bibliometrics to Keep Up with the JonesesUsing Bibliometrics to Keep Up with the Joneses
Using Bibliometrics to Keep Up with the JonesesChristina Pikas
 
Ensuring data quality
Ensuring data qualityEnsuring data quality
Ensuring data qualityIUPUI
 
Paradigm4 Research Report: Leaving Data on the table
Paradigm4 Research Report: Leaving Data on the tableParadigm4 Research Report: Leaving Data on the table
Paradigm4 Research Report: Leaving Data on the tableParadigm4
 
Ict journal layout
Ict journal layoutIct journal layout
Ict journal layoutFifeCollege
 
Journey from Data Quality to Applied Machine Learning
Journey from Data Quality to Applied Machine LearningJourney from Data Quality to Applied Machine Learning
Journey from Data Quality to Applied Machine LearningVictor Gunawan
 
Analytics 101 - Getting Started
Analytics 101 - Getting Started Analytics 101 - Getting Started
Analytics 101 - Getting Started Gautam Munshi
 
Data quality - The True Big Data Challenge
Data quality - The True Big Data ChallengeData quality - The True Big Data Challenge
Data quality - The True Big Data ChallengeStefan Kühn
 
Data Quality Rules introduction
Data Quality Rules introductionData Quality Rules introduction
Data Quality Rules introductiondatatovalue
 
Data Quality Integration (ETL) Open Source
Data Quality Integration (ETL) Open SourceData Quality Integration (ETL) Open Source
Data Quality Integration (ETL) Open SourceStratebi
 
Data Analytics.03. Data processing
Data Analytics.03. Data processingData Analytics.03. Data processing
Data Analytics.03. Data processingAlex Rayón Jerez
 
DGIQ 2015 The Fundamentals of Data Quality
DGIQ 2015 The Fundamentals of Data QualityDGIQ 2015 The Fundamentals of Data Quality
DGIQ 2015 The Fundamentals of Data QualityCaserta
 
Data science lecture1_doaa_mohey
Data science lecture1_doaa_moheyData science lecture1_doaa_mohey
Data science lecture1_doaa_moheyDoaa Mohey Eldin
 

What's hot (20)

Pikas using bibliometrics to make sense of research proposals
Pikas using bibliometrics to make sense of research proposalsPikas using bibliometrics to make sense of research proposals
Pikas using bibliometrics to make sense of research proposals
 
Differential Privacy Case Studies (CMU-MSR Mindswap on Privacy 2007)
Differential Privacy Case Studies (CMU-MSR Mindswap on Privacy 2007)Differential Privacy Case Studies (CMU-MSR Mindswap on Privacy 2007)
Differential Privacy Case Studies (CMU-MSR Mindswap on Privacy 2007)
 
Big data
Big dataBig data
Big data
 
CIRPA 2016: Individual Level Predictive Analytics for Improving Student Enrol...
CIRPA 2016: Individual Level Predictive Analytics for Improving Student Enrol...CIRPA 2016: Individual Level Predictive Analytics for Improving Student Enrol...
CIRPA 2016: Individual Level Predictive Analytics for Improving Student Enrol...
 
Advancing Foundation and Practice of Software Analytics
Advancing Foundation and Practice of Software AnalyticsAdvancing Foundation and Practice of Software Analytics
Advancing Foundation and Practice of Software Analytics
 
Data quality architecture
Data quality architectureData quality architecture
Data quality architecture
 
Using Bibliometrics to Keep Up with the Joneses
Using Bibliometrics to Keep Up with the JonesesUsing Bibliometrics to Keep Up with the Joneses
Using Bibliometrics to Keep Up with the Joneses
 
Ensuring data quality
Ensuring data qualityEnsuring data quality
Ensuring data quality
 
Paradigm4 Research Report: Leaving Data on the table
Paradigm4 Research Report: Leaving Data on the tableParadigm4 Research Report: Leaving Data on the table
Paradigm4 Research Report: Leaving Data on the table
 
Ict journal layout
Ict journal layoutIct journal layout
Ict journal layout
 
Journey from Data Quality to Applied Machine Learning
Journey from Data Quality to Applied Machine LearningJourney from Data Quality to Applied Machine Learning
Journey from Data Quality to Applied Machine Learning
 
Environmental Data Management and Analytics
Environmental Data Management and AnalyticsEnvironmental Data Management and Analytics
Environmental Data Management and Analytics
 
Analytics 101 - Getting Started
Analytics 101 - Getting Started Analytics 101 - Getting Started
Analytics 101 - Getting Started
 
Data quality - The True Big Data Challenge
Data quality - The True Big Data ChallengeData quality - The True Big Data Challenge
Data quality - The True Big Data Challenge
 
Data Quality Rules introduction
Data Quality Rules introductionData Quality Rules introduction
Data Quality Rules introduction
 
Data Quality Integration (ETL) Open Source
Data Quality Integration (ETL) Open SourceData Quality Integration (ETL) Open Source
Data Quality Integration (ETL) Open Source
 
Data Analytics.03. Data processing
Data Analytics.03. Data processingData Analytics.03. Data processing
Data Analytics.03. Data processing
 
Harper Analytics Beyond Usage Numbers
Harper Analytics Beyond Usage NumbersHarper Analytics Beyond Usage Numbers
Harper Analytics Beyond Usage Numbers
 
DGIQ 2015 The Fundamentals of Data Quality
DGIQ 2015 The Fundamentals of Data QualityDGIQ 2015 The Fundamentals of Data Quality
DGIQ 2015 The Fundamentals of Data Quality
 
Data science lecture1_doaa_mohey
Data science lecture1_doaa_moheyData science lecture1_doaa_mohey
Data science lecture1_doaa_mohey
 

Similar to Citi Global T4I Accelerator Data and Analytics Presentation

Introductions to Business Analytics
Introductions to Business Analytics Introductions to Business Analytics
Introductions to Business Analytics Venkat .P
 
Introduction to data science
Introduction to data scienceIntroduction to data science
Introduction to data scienceSpartan60
 
Introduction to Big Data Analytics
Introduction to Big Data AnalyticsIntroduction to Big Data Analytics
Introduction to Big Data AnalyticsUtkarsh Sharma
 
How Lyft Drives Data Discovery
How Lyft Drives Data DiscoveryHow Lyft Drives Data Discovery
How Lyft Drives Data DiscoveryNeo4j
 
Predictive Analytics: Context and Use Cases
Predictive Analytics: Context and Use CasesPredictive Analytics: Context and Use Cases
Predictive Analytics: Context and Use CasesKimberley Mitchell
 
Data Analytics: Better Decision, Better Business
Data Analytics: Better Decision, Better BusinessData Analytics: Better Decision, Better Business
Data Analytics: Better Decision, Better BusinessMcKonly & Asbury, LLP
 
Foundational Strategies for Trust in Big Data Part 2: Understanding Your Data
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataFoundational Strategies for Trust in Big Data Part 2: Understanding Your Data
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataPrecisely
 
Neo4j GraphTour Santa Monica 2019 - Amundsen Presentation
Neo4j GraphTour Santa Monica 2019 - Amundsen PresentationNeo4j GraphTour Santa Monica 2019 - Amundsen Presentation
Neo4j GraphTour Santa Monica 2019 - Amundsen PresentationTamikaTannis
 
How Lyft Drives Data Discovery
How Lyft Drives Data DiscoveryHow Lyft Drives Data Discovery
How Lyft Drives Data DiscoveryNeo4j
 
Data+Science+in+Python+-+Data+Prep+&+EDA.pdf
Data+Science+in+Python+-+Data+Prep+&+EDA.pdfData+Science+in+Python+-+Data+Prep+&+EDA.pdf
Data+Science+in+Python+-+Data+Prep+&+EDA.pdfneelakandan2001kpm
 
Use of secondary data in marketing analytics
Use of secondary data in marketing analyticsUse of secondary data in marketing analytics
Use of secondary data in marketing analyticsDebasisMohanty37
 
000 introduction to big data analytics 2021
000   introduction to big data analytics  2021000   introduction to big data analytics  2021
000 introduction to big data analytics 2021Dendej Sawarnkatat
 
The New Self-Service Analytics - Going Beyond the Tools
The New Self-Service Analytics - Going Beyond the ToolsThe New Self-Service Analytics - Going Beyond the Tools
The New Self-Service Analytics - Going Beyond the ToolsKatherine Gabriel
 

Similar to Citi Global T4I Accelerator Data and Analytics Presentation (20)

Data Science in Python.pptx
Data Science in Python.pptxData Science in Python.pptx
Data Science in Python.pptx
 
Introductions to Business Analytics
Introductions to Business Analytics Introductions to Business Analytics
Introductions to Business Analytics
 
Introduction to data science
Introduction to data scienceIntroduction to data science
Introduction to data science
 
Introduction to Big Data Analytics
Introduction to Big Data AnalyticsIntroduction to Big Data Analytics
Introduction to Big Data Analytics
 
lec1.pdf
lec1.pdflec1.pdf
lec1.pdf
 
How Lyft Drives Data Discovery
How Lyft Drives Data DiscoveryHow Lyft Drives Data Discovery
How Lyft Drives Data Discovery
 
Predictive Analytics: Context and Use Cases
Predictive Analytics: Context and Use CasesPredictive Analytics: Context and Use Cases
Predictive Analytics: Context and Use Cases
 
Data Analytics: Better Decision, Better Business
Data Analytics: Better Decision, Better BusinessData Analytics: Better Decision, Better Business
Data Analytics: Better Decision, Better Business
 
Data driven decision making
Data driven decision makingData driven decision making
Data driven decision making
 
Foundational Strategies for Trust in Big Data Part 2: Understanding Your Data
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataFoundational Strategies for Trust in Big Data Part 2: Understanding Your Data
Foundational Strategies for Trust in Big Data Part 2: Understanding Your Data
 
Neo4j GraphTour Santa Monica 2019 - Amundsen Presentation
Neo4j GraphTour Santa Monica 2019 - Amundsen PresentationNeo4j GraphTour Santa Monica 2019 - Amundsen Presentation
Neo4j GraphTour Santa Monica 2019 - Amundsen Presentation
 
Life Science Analytics
Life Science AnalyticsLife Science Analytics
Life Science Analytics
 
How Lyft Drives Data Discovery
How Lyft Drives Data DiscoveryHow Lyft Drives Data Discovery
How Lyft Drives Data Discovery
 
Lesson1.2.pptx.pdf
Lesson1.2.pptx.pdfLesson1.2.pptx.pdf
Lesson1.2.pptx.pdf
 
KIT601 Unit I.pptx
KIT601 Unit I.pptxKIT601 Unit I.pptx
KIT601 Unit I.pptx
 
BAS 250 Lecture 1
BAS 250 Lecture 1BAS 250 Lecture 1
BAS 250 Lecture 1
 
Data+Science+in+Python+-+Data+Prep+&+EDA.pdf
Data+Science+in+Python+-+Data+Prep+&+EDA.pdfData+Science+in+Python+-+Data+Prep+&+EDA.pdf
Data+Science+in+Python+-+Data+Prep+&+EDA.pdf
 
Use of secondary data in marketing analytics
Use of secondary data in marketing analyticsUse of secondary data in marketing analytics
Use of secondary data in marketing analytics
 
000 introduction to big data analytics 2021
000   introduction to big data analytics  2021000   introduction to big data analytics  2021
000 introduction to big data analytics 2021
 
The New Self-Service Analytics - Going Beyond the Tools
The New Self-Service Analytics - Going Beyond the ToolsThe New Self-Service Analytics - Going Beyond the Tools
The New Self-Service Analytics - Going Beyond the Tools
 

More from Marquis Cabrera

Columbia School of Social Work: Equipping Social Workers with Technology to I...
Columbia School of Social Work: Equipping Social Workers with Technology to I...Columbia School of Social Work: Equipping Social Workers with Technology to I...
Columbia School of Social Work: Equipping Social Workers with Technology to I...Marquis Cabrera
 
A two front battle: How governments can prevail in the fact of the global ski...
A two front battle: How governments can prevail in the fact of the global ski...A two front battle: How governments can prevail in the fact of the global ski...
A two front battle: How governments can prevail in the fact of the global ski...Marquis Cabrera
 
Rethinking enterprises, ecosystems and economies with blockchains
Rethinking enterprises, ecosystems and economies with blockchainsRethinking enterprises, ecosystems and economies with blockchains
Rethinking enterprises, ecosystems and economies with blockchainsMarquis Cabrera
 
Physiocare.io -- About Us and Strategy Deck
Physiocare.io -- About Us and Strategy DeckPhysiocare.io -- About Us and Strategy Deck
Physiocare.io -- About Us and Strategy DeckMarquis Cabrera
 
Blockchain 101 for Government Officials
Blockchain 101 for Government OfficialsBlockchain 101 for Government Officials
Blockchain 101 for Government OfficialsMarquis Cabrera
 
Harvard Law: How to Build a Social Enterprise?
Harvard Law: How to Build a Social Enterprise? Harvard Law: How to Build a Social Enterprise?
Harvard Law: How to Build a Social Enterprise? Marquis Cabrera
 
Co-Design: Quantified User Experience
Co-Design: Quantified User Experience Co-Design: Quantified User Experience
Co-Design: Quantified User Experience Marquis Cabrera
 
McKinsey Agility Hackathon: How to rapid prototype with your customers?
McKinsey Agility Hackathon: How to rapid prototype with your customers? McKinsey Agility Hackathon: How to rapid prototype with your customers?
McKinsey Agility Hackathon: How to rapid prototype with your customers? Marquis Cabrera
 

More from Marquis Cabrera (8)

Columbia School of Social Work: Equipping Social Workers with Technology to I...
Columbia School of Social Work: Equipping Social Workers with Technology to I...Columbia School of Social Work: Equipping Social Workers with Technology to I...
Columbia School of Social Work: Equipping Social Workers with Technology to I...
 
A two front battle: How governments can prevail in the fact of the global ski...
A two front battle: How governments can prevail in the fact of the global ski...A two front battle: How governments can prevail in the fact of the global ski...
A two front battle: How governments can prevail in the fact of the global ski...
 
Rethinking enterprises, ecosystems and economies with blockchains
Rethinking enterprises, ecosystems and economies with blockchainsRethinking enterprises, ecosystems and economies with blockchains
Rethinking enterprises, ecosystems and economies with blockchains
 
Physiocare.io -- About Us and Strategy Deck
Physiocare.io -- About Us and Strategy DeckPhysiocare.io -- About Us and Strategy Deck
Physiocare.io -- About Us and Strategy Deck
 
Blockchain 101 for Government Officials
Blockchain 101 for Government OfficialsBlockchain 101 for Government Officials
Blockchain 101 for Government Officials
 
Harvard Law: How to Build a Social Enterprise?
Harvard Law: How to Build a Social Enterprise? Harvard Law: How to Build a Social Enterprise?
Harvard Law: How to Build a Social Enterprise?
 
Co-Design: Quantified User Experience
Co-Design: Quantified User Experience Co-Design: Quantified User Experience
Co-Design: Quantified User Experience
 
McKinsey Agility Hackathon: How to rapid prototype with your customers?
McKinsey Agility Hackathon: How to rapid prototype with your customers? McKinsey Agility Hackathon: How to rapid prototype with your customers?
McKinsey Agility Hackathon: How to rapid prototype with your customers?
 

Recently uploaded

Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
Schema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdfSchema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdfLars Albertsson
 
Log Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptxLog Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptxJohnnyPlasten
 
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...Suhani Kapoor
 
FESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdfFESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdfMarinCaroMartnezBerg
 
Smarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxSmarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxolyaivanovalion
 
Mature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptxMature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptxolyaivanovalion
 
CebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxCebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxolyaivanovalion
 
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...Suhani Kapoor
 
04242024_CCC TUG_Joins and Relationships
04242024_CCC TUG_Joins and Relationships04242024_CCC TUG_Joins and Relationships
04242024_CCC TUG_Joins and Relationshipsccctableauusergroup
 
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323  Cheap Rate Call Girls in Vashi with Real Photo 100% SecureCall me @ 9892124323  Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% SecurePooja Nehwal
 
Week-01-2.ppt BBB human Computer interaction
Week-01-2.ppt BBB human Computer interactionWeek-01-2.ppt BBB human Computer interaction
Week-01-2.ppt BBB human Computer interactionfulawalesam
 
Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data  2023Data-Analysis for Chicago Crime Data  2023
Data-Analysis for Chicago Crime Data 2023ymrp368
 
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130Suhani Kapoor
 
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Serviceranjana rawat
 
April 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's AnalysisApril 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's Analysismanisha194592
 
Ukraine War presentation: KNOW THE BASICS
Ukraine War presentation: KNOW THE BASICSUkraine War presentation: KNOW THE BASICS
Ukraine War presentation: KNOW THE BASICSAishani27
 

Recently uploaded (20)

Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Sampling (random) method and Non random.ppt
Sampling (random) method and Non random.pptSampling (random) method and Non random.ppt
Sampling (random) method and Non random.ppt
 
Schema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdfSchema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdf
 
Log Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptxLog Analysis using OSSEC sasoasasasas.pptx
Log Analysis using OSSEC sasoasasasas.pptx
 
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
 
FESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdfFESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdf
 
Smarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxSmarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptx
 
Mature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptxMature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptx
 
CebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxCebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptx
 
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
 
04242024_CCC TUG_Joins and Relationships
04242024_CCC TUG_Joins and Relationships04242024_CCC TUG_Joins and Relationships
04242024_CCC TUG_Joins and Relationships
 
Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh
Delhi 99530 vip 56974 Genuine Escort Service Call Girls in  KishangarhDelhi 99530 vip 56974 Genuine Escort Service Call Girls in  Kishangarh
Delhi 99530 vip 56974 Genuine Escort Service Call Girls in Kishangarh
 
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323  Cheap Rate Call Girls in Vashi with Real Photo 100% SecureCall me @ 9892124323  Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
 
Week-01-2.ppt BBB human Computer interaction
Week-01-2.ppt BBB human Computer interactionWeek-01-2.ppt BBB human Computer interaction
Week-01-2.ppt BBB human Computer interaction
 
Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data  2023Data-Analysis for Chicago Crime Data  2023
Data-Analysis for Chicago Crime Data 2023
 
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
VIP Call Girls Service Miyapur Hyderabad Call +91-8250192130
 
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
(PARI) Call Girls Wanowrie ( 7001035870 ) HI-Fi Pune Escorts Service
 
April 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's AnalysisApril 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's Analysis
 
꧁❤ Aerocity Call Girls Service Aerocity Delhi ❤꧂ 9999965857 ☎️ Hard And Sexy ...
꧁❤ Aerocity Call Girls Service Aerocity Delhi ❤꧂ 9999965857 ☎️ Hard And Sexy ...꧁❤ Aerocity Call Girls Service Aerocity Delhi ❤꧂ 9999965857 ☎️ Hard And Sexy ...
꧁❤ Aerocity Call Girls Service Aerocity Delhi ❤꧂ 9999965857 ☎️ Hard And Sexy ...
 
Ukraine War presentation: KNOW THE BASICS
Ukraine War presentation: KNOW THE BASICSUkraine War presentation: KNOW THE BASICS
Ukraine War presentation: KNOW THE BASICS
 

Citi Global T4I Accelerator Data and Analytics Presentation

  • 1. Citi Global T4I Accelerator Data and Analytics Webinar: An open innovation initiative seeking to source tech solutions that promote integrity around the world Global Government Industry Any use of this material without specific permission of IBM or Citi is strictly prohibited. By: Marquis Cabrera Global Leader of Digital Government Transformation IBM Global Government Center of Competency Date: April 6, 2017 Twitter: @MarquisCabrera
  • 2. 2 • Global Problems: Problem-Solution • Global Data and Analytics Problems • Global IBM-Cognitive Use Cases • Data and Analytics 101 • Consider: Logic Model / Theory of Change • Potential Data Sources: Open Data, Open APIs • Types of Analytics: Predictive, Prescriptive, Descriptive, and Diagnostic • Useful Links Agenda
  • 3. 3 • Global Problems: Problem-Solution • Global Data and Analytics Problems • Global IBM-Cognitive Use Cases • Data and Analytics 101 • Consider: Logic Model / Theory of Change • Potential Data Sources: Open Data, Open APIs • Types of Analytics: Predictive, Prescriptive, Descriptive, and Diagnostic • Useful Links Agenda
  • 6. 6 Global IBM-Cognitive Use Cases North America – Alameda County Social Services Agency gives case workers tools to help more clients while eliminating $11 million in fraud. North Carolina identifies millions in suspicious Medicaid claims using advanced analytics. South America – Rio de Janeiro improved emergency response time by 30% with centralized command center. Europe –Baden-Württemberg Ministry of Integration improves immigrant integration policies through analytics. City Region of Eindhoven uses existing data from road and car sensors to keep traffic moving. Swedish Armed Forces improves data quality, while migrating millions of legacy data records. Africa – Using deep analytics and specialized algorithms to translate visual data received from CCTV cameras positioned around Nairobi, citizens can use their mobile phones to receive updates on road conditions and suggestions for alternative routes. With only 36 cameras currently installed around Nairobi, IBM researchers have augmented data using mathematical network analytics allowing the system to predict traffic in parts of town where no data feeds are available. China – Zhenjiang builds more intelligent transportation system to help analyze traffic patterns, obtain and disseminate real-time traffic updates, and anticipate and minimize traffic issues for this city of three million people. Middle-East – IBM is collaborating with Dubai Customs, Dubai Trade to explore blockchain solutions. Using Hyperledger Fabric and IBM Cloud, the blockchain solution transmits shipment data allowing key stakeholders to receive real-time information about the state of goods and the status of the shipment. Taking the example of a shipment of fruit, stakeholders involved in the process will receive timely updates as the fruit is exported from India to Dubai by sea, and then manufactured into juice in Dubai, and then exported as juice from Dubai to Spain by air.
  • 7. 7 • Global Problems: Problem-Solution • Global Data and Analytics Problems • Global IBM-Cognitive Use Cases • Data and Analytics 101 • Consider: Logic Model / Theory of Change • Potential Data Sources: Open Data, Open APIs • Types of Analytics: Predictive, Prescriptive, Descriptive, and Diagnostic • Useful Links Agenda
  • 8. Logic Model / Theory of Change Resources / Inputs Activities Outputs Outcomes Impact 54321 Certain resources (data, inputs, talent, compute tools) are needed to operate your program. If you have access to them, then you can use them to accomplish your planned activities (i.e. build app to prevent fraud). Your Planned Work If you accomplish your planned activities, then you will hopefully deliver the amount of product and/ or service that you intended. If you accomplish your planned activities, to the extent you intended, then your participants will benefit in certain ways. If these benefits to participants achieved, then certain changes in organizations, communities, or systems might be expected to occur (i.e. cost savings, lives saved) Your Intended Results
  • 10. Types of Analytics There are four types of analytics: 1. Prescriptive – This type of analysis reveals what actions should be taken. This is the most valuable kind of analysis and usually results in rules and recommendations for next steps. 2. Predictive – An analysis of likely scenarios of what might happen. The deliverables are usually a predictive forecast. 3. Diagnostic – A look at past performance to determine what happened and why. The result of the analysis is often an analytic dashboard. 4. Descriptive – What is happening now based on incoming data. To mine the analytics, you typically use a real-time dashboard and/or email reports.
  • 11. 11 • Global Problems: Problem-Solution • Global Data and Analytics Problems • Global IBM-Cognitive Use Cases • Data and Analytics 101 • Consider: Logic Model / Theory of Change • Potential Data Sources: Open Data, Open APIs • Types of Analytics: Predictive, Prescriptive, Descriptive, and Diagnostic • Useful Links Agenda
  • 12. Useful Links • Project Open Data • https://project-open-data.cio.gov/ • Watson Analytics IoT Lite (Free) • https://www.ibm.com/blogs/bluemix/2016/12/watson-iot-platform-renamed-lite-plan/ • How We Built an IoT Application in 10 Days Using Watson IoT and IBM Blockchain • http://bit.ly/2msml8J via • Cognitive Government: Enabling the Data-Driven Ecosystem • http://www-01.ibm.com/common/ssi/cgi- bin/ssialias?subtype=ST&infotype=SA&htmlfid=GVJ03029USEN&attachment=GVJ03029USEN.PDF • Lean Analytics • https://blog.kissmetrics.com/lean-analytics/ • How To Learn an Industry From Scratch • https://www.forbes.com/sites/spdr/2017/03/20/get-smarter-about-gender- intelligence/#b58faeb2d08f
  • 13. Identify User Needs Be Agile and Innovative Leverage Global Platform Government As a Platform MEET USER NEEDS Use Open Data & APIs