LEVERAGING YOUR ANALYTIC CAPACITY TO DRIVE VALUE FROM YOUR DATA ASSETS - Marc Smith
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Transcript of LEVERAGING YOUR ANALYTIC CAPACITY TO DRIVE VALUE FROM YOUR DATA ASSETS - Marc Smith
Co p yri g h t © 2 0 1 2 , SA S I n st it ut e I n c. A ll r i gh t s re se rve d.
LEVERAGING YOUR ANALYTIC CAPACITY TO DRIVE VALUE FROM YOUR DATA ASSETS
MARC SMITH, SAS PRINCIPAL, INFORMATION MANAGEMENT
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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.
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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
Business Issue 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.
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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 Copyright © 2012 SAS Institute Inc. All rights reserv ed. 5 Company Confidential - For Internal Use Only
Copyright © 2012, SAS Institute Inc. All rights reserv ed.
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
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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
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EXTERNAL VIEWPOINT
CHALLENGES IN ANALYTICS ADOPTION
Source: The Current State of Business Analytics: Where Do We Go From Here? Prepared by Bloomberg Businessweek Research Services, 2011
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THE SHIFT ANALYTICAL CULTURE
Facts, evidence, analysis as the primary way of deciding
facts
Data Enterprise Leadership Targets Analysts
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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
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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)
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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
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DATA QUALITY POOR CAPTURE OF 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
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VOLUME VARIETY
VELOCITY VALUE
TODAY THE FUTURE
DA
TA S
IZE
THE CHALLENGE
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SAS DATA GOVERNANCE 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
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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
DATA MINER / STATISTICIAN
IDENTIFY / FORMULATE
PROBLEM
DATA PREPARATION
DATA EXPLORATION
TRANSFORM & SELECT
BUILD MODEL
VALIDATE MODEL
DEPLOY MODEL
EVALUATE / MONITOR RESULTS
ANALYTICS LIFECYCLE
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ANALYTICAL CENTER OF EXCELLENCE (ACE) CHARTER
To promote the use of analytics and to support the end-to-end analytical requirements of the enterprise.
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** Used with permission from
Alberta Health Services
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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 Care Cancer
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
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SAS® HIGH PERFORMANCE
ANALYTICS -la?
Server N Server 2 Server 1
SAS In-Memory Analytics
SAS High Performance Deployment MPI MPI
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 Storage HDFS Storage HDFS Storage
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REQUIRES THE RIGHT ARCHITECTURE High
Performance Analytics
ANALYTICAL REPORTING
OPERATIONAL SYSTEMS
BUILT FOR PURPOSE ANALYTICAL
DATA STORES FOUNDATIONAL ENTERPRISE
& ANALYTICAL DATA WAREHOUSE
DA
TA S
ER
VIC
ES
AN
ALY
TIC
S S
ER
VIC
ES
EDW
ADW
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SAS® HIGH-PERFORMANCE
ANALYTICS KEY COMPONENTS
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INFORMATION MANAGEMENT
HOW DO WE DO IT?
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ANALYTICS
HOW DO WE DO IT?
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BUSINESS INTELLIGENCE
HOW DO WE DO IT?
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HIGH-PERFORMANCE ANALYTICS
HOW DO WE DO IT? BUSINESS SOLUTIONS INFORMATION MANAGEMENT ANALYTICS BUSINESS INTELLIGENCE
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ALL IN A SINGLE, SEAMLESS FRAMEWORK
HOW DO WE DO IT?