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LEVERAGING YOUR ANALYTIC CAPACITY TO DRIVE VALUE FROM YOUR DATA ASSETS

MARC SMITH, SAS PRINCIPAL, INFORMATION MANAGEMENT

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Passport Canada

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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

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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?