1. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
LEVERAGING YOUR ANALYTIC CAPACITY TO
DRIVE VALUE FROM YOUR DATA ASSETS
MARC SMITH, SAS PRINCIPAL, INFORMATION MANAGEMENT

2. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
Passport Canada
Read the Full Story
have to be careful how we spend
us to have these tools and develop a
discipline to use that information. For us
having the right information, at the right
about improving the service we provide
Hubert Laferriere
Director, Strategic Management
Business Issue
Passport Canada needed to better forecast its revenues
and demand to appropriately allocate budget and
resources, while improving service delivery and
customer satisfaction.
Solution
SAS® Forecast Server
SAS® Data Integration Studio
SAS® Activity Based-Management
Results/Benefits
Passport Canada has improved its forecast accuracy to
within 5%. Analysts have reduced time spent capturing
and cleaning data by 10%. Passports turnarounds are
now completed in 10 business days.

3. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
Manitoba Centre for Health Policy
Read the Full Story
and letting SAS Enterprise Miner
come up with decision trees to find
out what's important in the
identification of chronic diseases
such as diabetes, asthma and
heart disease."
Charles Burchill
Manager of Program and Analysis System
BusinessIssue
Maintains a comprehensive population-based data repository for
use by research community, which supports the development of
health policies, programs and services for Manitobans.
To meet new provincial requirements around auditing and access
control, while its data was growing at an unprecedented rate.
Solution
SAS® Scalable Performance Data Server
SAS® Enterprise Miner
Results/Benefits
Researchers are now able to build queries in hours instead of
days, helping to provide insights into disease trends and service
use.

4. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
LA COUNTY
Business Issue
Understand cross-agency service utilization.
Measure the cost of serving the indigent population.
Reduce duplication of services without compromising privacy.
Solution
SAS® Analytics
SAS® Data Integration
DataFlux de-identification tool
Results/Benefits
Discover and correct Service duplication to reduce costs.
Identification of general relief recipients who are eligible for
applied.
Statistical evidence that placing homeless individuals into
apartments is cost-effective.
Predict costs for new programs. Read the Full Story
and difficult budgetary issues.
evidence-based research to
help elected officials
understand the costs and
Manuel Moreno
Director of Research, Chief Executive
Office

5. 5Copyright © 2012 SAS Institute Inc. All rights reserved. 5
Company Confidential - For Internal Use Only
Copyright © 2012, SAS Institute Inc. All rights reserved.
Finding treasures in unstructured data
like social media or survey tools
that could uncover insights
about citizen sentiment
Mine transaction databases
for data of migration patterns
that represent a shift in
composition..
Leveraging historical data
to drive better insight into
trends for the future
Analyze massive
amounts of data in
order to accurately
identify areas likely to
produce the most
sustainable outcome
FORECASTING
DATA MINING
TEXT ANALYTICS
OPTIMIZATION
STATISTICS
ADVANCED ANALYTICS FOR BIG DATA
INFORMATION
MANAGEMENT

6. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
STRATEGIC
IMPERATIVE
DRIVING THE NEED FOR ANALYTICS
Driving better outcomes through evidence-based
decisions based on sound research and analysis
Improving quality of service and sustainable funding
Efficiency and fact based performance management -
collect data and use it to evaluate whether objectives
are being met and how efficiently
Gaining public trust and providing transparency
through governance, risk and compliance
development and
the public service in
general should be more
evidence-based. This
requires setting clear
objectives based on
sound research and
evidence
Public Services, 2012

7. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
EXTERNAL
VIEWPOINT
CHALLENGES IN
ANALYTICS ADOPTION
Source:The CurrentState of Business Analytics:Where Do We Go From Here?
Prepared byBloombergBusinessweek Research Services,2011

8. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
THE SHIFT ANALYTICAL CULTURE
Facts, evidence, analysis as the primary
way of deciding
facts
Data
Enterprise
Leadership
Targets
Analysts

9. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.

10. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
DATA THE RAW MATERIAL
The prerequisite for everything analytical
Clean, consistent, accurate, common, integrated,
accessible
Needs to be centralized, linked and governed
- measuring something
new and important

11. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
DATA QUALITY MULTIPLE VERSIONS OF SAME DX
Site A Site B Site C
HYPERTENSION ESSENTIAL HYPERTENSION* (401) ESSENTIAL, BENIGN HYPERTENSION
ESSENTIAL HYPERTENSION* (401) ESSENTIAL HYPERTENSION* (401.) HYPERTENSION (ESSENTIAL)
ESSENTIAL HYPERTENSION (401) HYPERTENSION NOS (401.9) HYPERTENSION UNCOMPLICATED
HYPERTENSION (401) HYPERTENSION (401)
ESSENTIAL HYPERTENSION (401)
HYPERTENSION
HYPERTENSION (401.9)

12. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
DATA QUALITY MULTIPLE VERSIONS OF SAME MED
Site A Site B Site C
APO-HYDRO 25MG TABLET METFORMIN HCL 500MG ORAL TABLET INFLUENZA
HYDROCHLOROTHIAZIDE 25MG ORAL
TABLET METFORMIN HCL 500MG TA FLU VACCINE
HYDROCHLOROTHIAZIDE TAB 25MG APO-METFORMIN 500MG TABLET FLUVIRAL
APO-HYDRO 25 MG TABLET APO-METFORMIN - TAB 500MG FLU SHOT
APO HYDRO TAB 25MG VAXIGRIP

13. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
DATA QUALITY POOR CAPTUREOF RISK FACTORS
Site A Site B Site C
NON-SMOKER TOBACCO NON-SMOKER NON SMOKER
T TOBACCO NEVER SMOKER
EX-SMOKER TOBACCO EX SMOKER QUIT > 1 YEAR
SMOKER: QUITTING TOBACCO NON-SMOKER QUIT < 1 YEAR
SMOKER: NO PLAN TO QUIT TOBACCO SMOKER
SMOKER: ACTIVELY QUITING NEVER SMOKED
TOBACCO USE (305.1) TOBACCO NON SMOKER
SMOKER: ACTIVELY QUITTING
SMOKING
NON SMOKER
NICOTINE ADDICTION
NONSMOKER
EX SMOKER

14. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
VOLUME
VARIETY
VELOCITY
VALUE
TODAY THE FUTURE
DATASIZE
THE CHALLENGE

15. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
SAS DATAGOVERNANCE FRAMEWORK
Corporate
Drivers
Business
Framework
Process
& Policy
Data
Management
Data
Governance
Execution
Process
P
R
O
G
R
A
M
O
V
E
R
S
I
G
H
T
Data
Governance
Charter
Guiding
Principles
Decision-­‐
making
Bodies
Decision
Rights
Strategic Priorities: Public Trust, Quality
of Service, Policy Outcomes, Open
Government
Business Drivers: Data Quality
Improvement; Operational Efficiencies,
Program Integrity
Data Stewardship Roles & Tasks
Mechanisms: Stewardship Dashboards,
Workflow Automation, Data Profiling Tools
People: Council, Stakeholders, Meeting Agendas
Process: Metrics Definition, Workflow, Council By-­‐Laws
Data
Requirement
Data
Architecture
Data
Administration
Metadata
Management
Data
Quality
Security &
Access
Rights

16. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.

17. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
Domain Expert
Makes Decisions
Evaluates Processes and ROI
BUSINESS
MANAGER
Model Validation
Model Deployment
Model Monitoring
Data Preparation
IT SYSTEMS /
MANAGEMENT
Data Exploration
Data Visualization
Report Creation
BUSINESS
ANALYST
Exploratory Analysis
Descriptive Segmentation
Predictive Modeling
DATAMINER /
STATISTICIAN
IDENTIFY /
FORMULATE
PROBLEM
DATA
PREPARATION
DATA
EXPLORATION
TRANSFORM
& SELECT
BUILD
MODEL
VALIDATE
MODEL
DEPLOY
MODEL
EVALUATE /
MONITOR
RESULTS
ANALYTICS LIFECYCLE

18. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
ANALYTICAL CENTER OF
EXCELLENCE (ACE) CHARTER
To promote the use of analytics and to
support the end-to-end analytical
requirements of the enterprise.

19. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
** Used with
permission from
Alberta Health
Services

20. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
Research
Coordination with external organizations / academic centers to
drive research: Establish single point of coordination related to data and
resources that supports the research agenda with academic institutions and other
external organizations.
Senior
Health
Primary
Care/CDM
Public
Health
Clinical
Support
Services
Research
Cardiology
Critical CareCancer
Mental
Health &
Addiction
Bone and
Joint
Respiratory
Emergency
Care
Surgery
Core
Consolidated core functions to drive strategic
analytics: the goal is to establish a single source of
truth, scale and the development of best practices to
answer the key strategic questions for top executives.
Major Clinical Program Areas
Distributed clinical resources: Rebalance
resources to have a net increase of embedded analytics
within the major clinical program areas and strategic
programs.
Embedded Analytics
Coordinated
Strategic Analytics
DIMR
Population
Health
observatory
Zones
Activity
Based
Funding
HR
Case
Costing
Strategic Hub and Spoke Model Hybrid
** Used with
permission from
Alberta Health
Services

21. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
SAS® HIGH
PERFORMANCE
ANALYTICS
-la?
Server NServer 2Server 1
SAS In-Memory
Analytics
SAS High Performance
DeploymentMPIMPI
proc hplogistic
data=MPPLib.MyTabl e;;
class A B C D ;;
model y = a b c
b*d x1-­x100;;
output
out=MPPlib.logout
pred=p;;
run;;
Multiple
Threads
Multiple
Threads
Multiple
Threads
HDFS StorageHDFS StorageHDFS Storage

22. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
REQUIRES THE RIGHT ARCHITECTURE
High
Performance
Analytics
ANALYTICAL
REPORTING
OPERATIONAL
SYSTEMS
BUILT FOR PURPOSE
ANALYTICAL
DATA STORES
FOUNDATIONAL ENTERPRISE
& ANALYTICAL DATA WAREHOUSE
DATASERVICES
ANALYTICSSERVICES
EDW
ADW

23. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
SAS® HIGH-­
PERFORMANCE
ANALYTICS
KEY COMPONENTS

24. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
INFORMATION MANAGEMENT
HOW DO WE DO IT?

25. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
ANALYTICS
HOW DO WE DO IT?

26. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
BUSINESS INTELLIGENCE
HOW DO WE DO IT?

27. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
HIGH-PERFORMANCE ANALYTICS
HOW DO WE DO IT?
BUSINESS SOLUTIONS INFORMATION MANAGEMENT ANALYTICS BUSINESS INTELLIGENCE

28. Co p yri g h t © 2 0 1 2 , SA S In stitute In c. A ll ri gh ts re se rve d.
ALL IN A SINGLE, SEAMLESS FRAMEWORK
HOW DO WE DO IT?