Campbell Robertson's presentation, on Analytics in Government from Opportunity for Analytics event, Ottawa

1. © 2015 IBM Corporation
Analytics in Government
Results-based outcomes with
IBM Predictive Analysis for Cost Avoidance and
Beyond
Campbell Robertson
Government Analytics Lead
IBM Analytics Group
cir@ca.ibm.com

2. © 2015 IBM Corporation2
Agenda
 Analytics in Government
 Contextual Computing
 IBM and Spark
 Next steps and how IBM can help

3. © 2015 IBM Corporation3

4. © 2015 IBM Corporation4
Understanding the Bigger Picture of answering and predicting
New / Enhanced
Applications
All Data
Claims
Tax & Income
Threat & Crime
Case Worker
Social Media
Sensor
Images &
Video
Outcome-based
Program Mgt.
Real-time
Fraud Detection
Real-time Threat
& Crime Detection
Audit & Tax
Compliance
Patrol Deployment
Budget & Finance
Optimization
Big Data & Analytics Platform
Big Data & Analytics Strategy, Integration & Managed Services
Big Data & Analytics Infrastructure
What is
happening?
Discovery and
exploration
Why did it
happen?
Reporting and
analysis
What could
happen?
Predictive
analytics and
modeling
What did
I learn,
what’s
best?
Cognitive
What action
should I
take?
Decision
management
Information Integration & Governance
Landing,
Exploration
and Archive
data zone
EDW and
data mart
zone
Operational
data zone
Real-time Data Processing & Analytics
Deep
Analytics
data zone
Risk Determination
Case Management

5. © 2015 IBM Corporation5
IBM has a broad set of analytics capabilities for government
Information Layer
How is data managed
and used?
Descriptive Layer
What is happening or
what has happened?
Predictive Layer
What could happen?
Prescriptive Layer
How can we achieve the
best outcomes?
Cognitive Layer
Tell me the best course of
action
Information
Governance
Data
Management
Big Data
Business
Intelligence
Predictive
Analytics
Content
Management
Watson
Analytics
Watson
Analytics
Financial
Performance
Analytics
Solutions
IBM Analytics
Platform
IBM Watson
IBM Analytics
Solutions
Content
Management
Analytics
Solutions

6. © 2015 IBM Corporation6
Enabling the Predictive Analytics Process
Collaboration and Deployment Services
Data Collection Modeler
Decision
ManagementStatistics
Transactions
Demographics
Interactions
Opinions
Predictive Modeling
Data Mining
Text Analytics
Social Network Analysis
Statistical Analysis
Prediction
Rules
Optimization
Process
Detect & Capture Analyze & Predict Engage & Act

7. © 2015 IBM Corporation7
Analytics in Program Compliance
How can I minimize
revenue drain from
non-filers?
How can I automate
processes to lower
labor costs?
How can I predict
who is likely to pay
their taxes?
Focus efforts on
high risk individualsAutomate ProcessesReduce Improper Payments
• Content Analytics is used to
extract data from documents,
including sales acts, codes,
addresses, amount paid,
seller and more to feed
Content Manager hub and
other systems
• Automation helps reduce
labor costs by 60% and
increases the amount and
types of data that can be
extracted
• Uses predictive modeling
that gathers information
from tax assessments, train
ticketing systems, TV
licenses, police records and
more to predict whether a
person will reliably pay
taxes
• 18% reduction in collections
workload
• 70% risk prediction
accuracy
• Uses predictive modeling to
improve collection and filing
compliance functions by
providing an integrated client-
level single taxpayer view of
compliance issues and events,
enabling comprehensive risk
identification and
• One pilot alone resulted in over
$120M dollars in incremental
tax assessments from non-
filers.
Dutch Tax &
Customs
Administration
Canadian Tax and Finance
Department
European Tax
Collection Agency

8. © 2015 IBM Corporation8
Alameda County
Social Services Agency
Analytics are transforming program agencies in many ways
• Identified fraud among
millions of claims, by finding
obscure connections among
doctors, pharmacists, lab and
medical supply companies
• Identified more than US
$200m in questionable claims
resulting in 22 criminal
convictions and US $49m in
recovered funds
Reduce
caseload work
• Gives managers and
caseworkers a deep, real-
time understanding of case
and program status,
enabling them to find the
best assistance programs
for each situation
• Reduced caseload work
by 90% or more
• Used a sophisticated
analytics solution to gain
insight into hidden predictors
to determine whether a
young person is more likely
to stay in or drop out of
school, occupational training
or employment.
• 250% improvement in
accuracy of identification of
at-risk youths
How can I provide
caseworkers with timely,
relevant information?
How can I identify clients
at-risk and improve
program success rates?
How can I identify and
reduce fraudulent claims?
U.S. State
Health Agency
UK youth services
organization
Increase program
success rates
Reduce
fraudulent claims

9. © 2015 IBM Corporation9
Predictive Analytics for Crime Prevention
Focus Attention on “Sick Areas”
• Hot spot analysis identifies areas of the
city with high crime
• Playback controls allow crime patterns
to be tracked over time
Find Root Causes
• Drill down on hot spots shows crime
detail
• Anomaly detection shows what is not
normal
Predictive Modeling
• Identify where crime is likely to happen
to improve patrol deployment
Evaluate Treatment
• Patrol coverage shows where patrols
are light

10. © 2015 IBM Corporation10

11. © 2015 IBM Corporation11
McKinley Children’s Center models and identifies variables that affect
permanency, helping improve child outcomes and program success
Solution Components
Business Challenge: McKinley Children’s Center sought to better understand the
many variables, such as age, ethnicity and types of serious incidents, that can
affect a child’s permanency. However, caseworkers used paper and pen to record
data, plus manually collected data from external databases, resulting in a highly
fragmented view of each child’s needs. The problem was not lack of information but
rather an inability to access and analyze it in an efficient and constructive manner.
The Smarter Solution: The center deployed a big data and analytics system that
collects and aggregates near-real-time data from disparate databases, giving
caseworkers a comprehensive view of each child’s profile plus an unprecedented
understanding of how different social and home variables affect that child’s success.
Using modeling and predictive analytics, caseworkers can uncover hidden patterns
and relationships and use the insight to determine the right combination of services
for each child, identify risk factors, match children with adoptive families, and speed
progress toward an optimal outcome.
Most children receive services from multiple agencies. The solution enables data
sharing with other organizations, giving kids the best opportunity for success.
—Executive, McKinley Children’s Center
99% reduction
in data collection time, from two
months to minutes, helping staff
spot trends in serious incidents
Pinpoints variables
that affect positive outcomes,
helping ensure a better foster
home experience
Identifies at-risk kids
helping caseworkers reduce
frequency of serious incidents
• IBM® SPSS® Modeler
• IBM SPSS Statistics
• IBM Cognos® Business Intelligence
• IBM Business Partner Data41

12. © 2015 IBM Corporation12
• IBM® DB2® V10.5
• IBM WebSphere® Application Server V8.5
• IBM Intelligent Pervasive Platform
• IBM Rapidly Adaptive Visualization Engine
• IBM Social Media Analytics
• IBM Content Analytics
• IBM Cognos® Express Reporter
• IBM Business Partner Ricca Srl
The Comune di Siracusa in Italy analyzes a mobile app and social
media content to develop an actionable plan to increase tourism
25% increase
in the number of comments about
the city posted to Twitter each day
Solution components
Business challenge: Tourism is a critical part of Siracusa’s economy, but the city
had only rudimentary knowledge of what tourists enjoyed and no way of knowing if
its ancient landmarks were in good condition and met visitors’ expectations. With
local industry on the decline, it was more important than ever for Siracusa to
maintain its history and its reputation as a beautiful and fascinating destination.
The smarter solution: The city took a two-pronged approach to gathering public
sentiment: the LoveCityIndex interactive mobile application, which gathers visitors’
feedback directly, and a social media analytics solution that helps uncover the
candid opinions of past and present visitors. The resulting insight helps the city
prioritize landmark maintenance and ensure that local treasures are accessible,
well-maintained and informative.
“This project makes us one of the most technologically advanced municipalities in
Italy and positions us to be at the leading edge of development in the future.”
—Roberto Visentin, mayor
Increases
tourist satisfaction by ensuring
that landmarks are in good
condition and easily accessible
Preserves
history by providing real-time
status of 80 priceless landmarks

13. © 2015 IBM Corporation13
Solution components
• IBM® Power® 740 Express®
• IBM InfoSphere® DataStage®
• IBM Cognos® Business Intelligence V10
• IBM Software Services
• IBM Business Partner Suzhou Wireless
Application Centre Ltd.
Optimizes allocation
of resources by more accurately
forecasting pattern shifts across
different parts of the city
Improves efficiency
of agency operations by
automating data acquisition
and reporting
95% reduction
in the time required to generate
reports on demographic trends
and business KPIs
A large city government in China uses predictive analytics to keep
services and resources in line with shifting demographic patterns
Business challenge: Fueled by explosive economic growth, China’s cities are
experiencing rapid and often unpredictable demographic evolutions. One such
city saw that the only way it could keep services, policies and resources aligned
with these shifts was to measure and predict them. But that meant first putting
the picture together from data spread across dozens of city agencies.
The smarter solution: With a common data model linking all of the city’s
municipal agencies, policymakers are uncovering demographic shifts even as
they’re unfolding. Agencies are taking proactive steps to put resources in place—
such as more healthcare facilities and transportation—to meet the changing
public service needs resulting from the city’s changing demographic profile.
Deeper, more accurate and more timely insights into the city’s changing
social and business profiles are positioning its agencies to better serve
citizens’ needs.

14. © 2015 IBM Corporation14
Strategy and Planning required…
New / Enhanced
Applications
All Data
Claims
Tax & Income
Threat & Crime
Case Worker
Social Media
Sensor
Images &
Video
Outcome-based
Program Mgt.
Real-time
Fraud Detection
Real-time Threat
& Crime Detection
Audit & Tax
Compliance
Patrol Deployment
Budget & Finance
Optimization
Big Data & Analytics Platform
Big Data & Analytics Strategy, Integration & Managed Services
Big Data & Analytics Infrastructure
What is
happening?
Discovery and
exploration
Why did it
happen?
Reporting and
analysis
What could
happen?
Predictive
analytics and
modeling
What did
I learn,
what’s
best?
Cognitive
What action
should I
take?
Decision
management
Information Integration & Governance
Landing,
Exploration
and Archive
data zone
EDW and
data mart
zone
Operational
data zone
Real-time Data Processing & Analytics
Deep
Analytics
data zone
Risk Determination
Case Management

15. © 2015 IBM Corporation15
IBM Institute of Business Value Study: Contextual Computing
The next level in Analysis and Decision Support

16. © 2015 IBM Corporation16
Without context, the potential value of an enterprise’s data is not
being fully realized…
16
“In common use almost every word has many shades of meaning, and therefore
needs to be interpreted by the context.”
Alfred Marshal, Economist

17. © 2015 IBM Corporation17
… And conclusions drawn from data (big or not) may be flawed
 Organizations leverage enterprise data to gain insights and learn about ‘entities’
 Context provides insights to better understand how entities relate to one another
 Cumulative context is the memory and knowledge of how entities relate and interact over time
 Context accumulators detect like and related entities from historical and current data in large,
complex enterprise data environments to put data into context
 Discoveries can be made with each new data element or ‘observation’ introduced to a data
environment or ‘observation space’ (including real time-time data streams) and provide
information to consumers based on relevance
 With context, assertions can be made about each new observation which has the potential to
impact critical decisions and/or fundamentally alter prior assumptions or assertions
17
Consumer
Data ‘Observation
Space’
Context
accumulation
Data “finds” data
Information ‘in context’
Relevance

18. © 2015 IBM Corporation18
Contextual computing accelerates the detection of complex patterns
in both data and processes through four key activities
18
Collect all relevant data
from a variety of sources,
keep everything you can
as long as you can
Extracting features and
creating metadata from
diverse data sources to
continually build and update
context.
Analyze data in context to
uncover hidden information
and find new relationships
Composing
recommendations and
using context to deliver
insights to the point of
action (human or system)
Gather Connect Reason Adapt
SQL
NoSQL
Information Knowledge Intelligence
ESB
R
E
A
S
O
N
C
O
N
N
E
C
T
A
D
A
P
T
G
A
T
H
E
R
Data Context Decisions
& Actions
Feedback
& Learning
1 2 3 4
Feedback
& Learning
Feedback
& Learning

19. © 2015 IBM Corporation19
Traditional Approach
Structured, analytical, logical
Systems of Record
New Approach
Creative, holistic thought, intuition
Systems Of
Engagement
Systems Of Record
and
Systems Of
Engagement
The “Contextual Enterprise”: a future vision for contextual computing
19

20. © 2015 IBM Corporation20
Contextual computing provides national defense organization
with capabilities to better protect strategic maritime trade routes
 Securing strategic shipping
lanes and waterways are
critical to national security;
however, monitoring the
activity in a marine region is
extremely challenging and
resource intensive.
 Organization was in need of
capabilities to better enable
them to protect globally
significant waterways in an
increasingly resource
constrained environment.
In response to resource constraints and the emergence of advanced warfighting technologies, a national
defense organization has focused on developing and implementing leading-edge capabilities to meet
new security challenges effectively.
Challenges:
20
 Partnered with industry (ST Electronics and IBM) to develop and deploy a first-of-
a-kind solution (Comprehensive Maritime Awareness Solution [CMAS]) that
analyses huge amounts of data gathered from various coastal and satellite
sensors, databases and open source intelligence.
 The CMAS context accumulating engine generates higher quality predictions as to
which vessels are most important to focus.
 This capability conducts context accumulation over structured, social and
geospatial data and provides a ranked list of potential entities of interest and
indicates to the analysts why a particular vessel should be focused on it.
 This capability is a real-time, sub-second, sense and respond service—providing
information to decision makers fast enough to do something about it while events
are still happening.
Solution:
Improved decision making, incident response times and resource efficiency
through improved situational awareness.
Results:

21. © 2015 IBM Corporation21
Four key capabilities are critical to successfully implementing a
contextual computing solution
21

22. © 2015 IBM Corporation22
Conclusions
 Context is a ‘value multiplier’ and can enable organizations to realize greater value in their enterprise’s data
 Context provides the basis for the next generation of business intelligence and the foundation for cognitive
computing
 While immature, contextual computing capabilities are advancing and pioneering organizations are already
realizing business benefits
 Significant opportunities exist in government and many organizations expect to implement a contextual
computing solution in the next 3 years
 Four key capabilities are critical to successfully implement a contextual computing solution: data, skills, policy
and technology
 Challenges related to policy and skills will be the greatest hurdles for government organizations and many
will require external partners and vendors to help implement
 Much can be learned from those that have pioneered implementations already – and government
organization can take steps now to begin bringing context to their organizations
22
“It's the future. We need to align with this to move forward.”
Paul Haugan, CIO, Johnson County (Kansas, USA)

23. IBM | SPARK
Power of data. Simplicity of design. Speed of innovation.

24. IBM Spark
Why Spark matters to a business?
2. Spark lets you develop line-of-business
applications faster
3. Spark learns from data and delivers in real time
With Hadoop, you ask a question and get back a batch of data. With
Spark, you may say, “continue to give me answers to this
question”…and when new data comes, the user is smarter.
1. Spark makes it easier to access and work with
all data
- Enables new data-based use cases
- All data: Internal/External, Structured/Unstructured
- Real-time insights, from all data sources
- Automates analytics with Machine Learning
- Clients that lead in data, lead their industry
Design Development
Data Science

25. IBM Spark
Spark processes and analyzes data from ANY data
source
Hadoop Database Mainframe
Data-
warehouse
Business Applications and Business
Intelligence

26. © 2015 IBM Corporation26
How do you begin this journey?
Start with
questions, not with
data
Focus on the
highest value
initiatives
Embed insights to
drive actions and
deliver value
Keep existing
capabilities while
adding new ones
Develop an
analytics plan for
the future
Align information
from all relevant
sources

27. © 2015 IBM Corporation27
NEXT STEPS AND HOW IBM
CAN HELP

28. © 2015 IBM Corporation
Leading the Charge for Analytics Solution Success!!
IBM Analytics Workshop

29. © 2015 IBM Corporation29
Features
 Specific Industry use cases
 Knowledge transfer from IBM Analytic experts
 Deliver on your Platform Measures of Success
 Collaborative and agile execution as core
philosophy
– Enable your skills
– Expanded understanding of big data
technologies
– Joint teaming in ‘Hack’ room environment
– We encourage your team to actively
participate
 Establish Analytics Infrastructure/capability
– Software at no charge for non-production use
– Flexible to support either on-premise, IBM
Hardware/Appliance, Private or Public Cloud
 We will help create alignment.
Deliverables
 Customized reference architecture and
roadmap
 Findings and recommendations report
 Analytic reports designed around your
data and business requirements
 IP that you can leverage to continue to
grow skills
IBM Analytics Workshop - Offering Overview
Benefits
 Expedites your enablement and
roadmap definition
 Provides insight into your business
leveraging multiple data sources
 Grows skills within your organization to
support your Analytics Solution journey

30. © 2015 IBM Corporation30
IBM helps identify a quick-hit analytics opportunity using our specialized
selection methodology
IBM ports a real, actionable data set – even messy data - unto a unique
toolset and platform enabled by the IBM Cloud
IBM data scientists use special techniques to analyze the data that
doesn’t require traditional data models or schema
The project is finished in a matter of weeks (not months or years)
Actionable findings and outcomes are ready for business consumption
Business and economic value are realized as the first real bite of
analytics outcomes are pursued and won
Identify a quick hit
opportunity
Real data
IBM Data Scientists
Fast turnaround
Actionable insights
ROI
Introducing the IBM Rapid Analytics Results (RAR) program
The RAR is quick, multi-week analytics project that provides real insight on a real data set
providing actionable value with no infrastructure or skills investment.

31. © 2015 IBM Corporation31
Rapid Analytics Results program components
• Combing proven organization and project management methodologies in new
ways and inventing a new project selection methodology for data science
• Utilizing NoSQL oriented data management techniques specifically designed for
rapid results exploratory analytics
• Utilizing an optimized analytics cloud software platform complete with analytics
tools
RAR Program Components
“Focuses on boiling bathtubs, not oceans”
Methodologies
Data management techniques Platform, tools,
and cloud

32. © 2015 IBM Corporation

33. © 2015 IBM Corporation33

34. © 2015 IBM Corporation34
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