IRMAC SAS INFORMATION MANAGEMENT, TRANSFORMING AN ANALYTICS CULTURE. Copyright 2012, SAS Institute Inc. All rights reserved.

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1 IRMAC SAS INFORMATION MANAGEMENT, TRANSFORMING AN ANALYTICS CULTURE

2 ABOUT THE PRESENTER Marc has been with SAS for 10 years and leads the information management practice for canada. Marc s area of specialty is in building enterprise architecture and information management strategies for large businesses. Marc has been in the information management and business analytics space for 20 years and he has experience developing and applying his knowledge in several industries in several provinces across Canada including banking, insurance, communication, retail, federal and provincial government, healthcare, pharmaceutical, utilities, oil and gas, and

3 3

4 THE CONTEXT BUILDING AN ANALYTICAL CULTURE Facts, evidence, analysis as the primary way of deciding Pervasive test and learn emphasis where there aren t facts Free pass for push backs Where s your data? Still room for intuition based on experience A focus on action after analysis Monitor and adjust - never resting on your analytical laurels Thomas Davenport, Analytics at Work

5 ANALYTICS AT WORK THE BIG PICTURE Analytical Capability Organizational Context Desired Result Enterprise Leadership Targets Analysts Analytical Culture And Business Processes Better Decisions! Systematic Review Thomas Davenport, Analytics at Work

6 DATA It s not what you report with, it s what you report on The prerequisite for everything analytical Clean, consistent, accurate, common, integrated, accessible Needs to be centralized and governed Analytical environment ties to it, but extendable - measuring something new and important Thomas Davenport, Analytics at Work

7 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

8 KEY OBJECTIVES TOP OF MIND FOR INFORMATION MANAGEMENT Elevating data to a core business asset Manage data as a strategic information asset for business value Optimize decision making to gain competitive advantage Enables competitive differentiation Analytics as a key Information Management processes Fast time to intelligence with optimal resources Apply analytics pervasively to a broader range of decisions

9 DATA GOVERNANCE MATURITY MODEL IS YOUR DATA TREATED AS AN ASSET? HIGH Governed LOW Proactive Business requirements drive all IT efforts Undisciplined Technology projects driven by IT only Duplicate, inconsistent data Inability to adapt to business changes LOW People, Policies, Technology Adoption Reactive Line of business influences IT projects Redundant information foils cross-functional efforts High cost to maintain multiple applications IT and business groups collaborate Enterprise views of master data and business processes remain separate, slowing innovation Repeatable, automated business processes Personalized customer relationships and an optimized supply chain HIGH

10 WHAT DATA GOVERNANCE DOES Establishes business stakeholders as information owners. Aligns data quality with business measures and acceptance. Removes IT from business decision making. Ensures data is managed separately from applications. Clearly defined decision rights for appropriate definition and appropriate use. Positions enterprise data issues as crossfunctional. Invites new rigor around monitoring and measurement.

11 COMMON CORPORATE DRIVERS What s the highestprofile business need that can benefit from data governance? What additional strategies or initiatives can leverage data governance? CRM SOX FINTRAC PCI Regulatory Compliance OSFI Security Policy Could individual projects or organizations benefit from data governance? MDM BI/DW ERP Enterprise Initiatives Monitoring Dashboards Terms Glossary Governance Quality Stewards Risk Management Policy hub Privacy Policy Fraud Detection Valuations

12 DATA GOVERNANCE PROGRAM ROLES 12

13 DATA GOVERNANCE FRAMEWORK P R O G R A M O V E R V S I G H T Corporate Drivers Business Framework Process & Policy Management Governance Execution Process Strategic Priorities: Voice of the Customer; Compliance Mandates, Mergers & Acquisitions Governance Charter Requirement Guiding Principles Business Drivers: At-Risk Projects: Quality Improvement; Operational Efficiencies Decisionmaking Bodies Stewardship Roles & Tasks Architecture Metadata Management Quality Administration People: Council, Stakeholders, Meeting Agendas Process: Metrics Definition, Workflow, Council By-Laws Mechanisms: Stewardship Dashboards, Workflow Automation, Profiling Tools Decision Rights Security & Access Rights

14 INSTITUTING DATA GOVERNANCE DO YOU HAVE A TOP-DOWN PROBLEM? There is a large or pending corporate initiative that will hinge on decisions about the use of information across organizations. Managers and staff across lines of business consistently complain about the same data issues. An executive or decision maker with authority has begun using the term data as an asset. There is consensus across organizations about the need to assign data ownership. Tiebreaking among organizations is needed for: definitions Business rules usage policies Security and privacy rules

15 TOP-DOWN APPROACH Initial Vision Assemble Core Team Create Guiding Principles Identify Decision- Making Bodies Determine DG Stake-holders Assign Decision Rights Apply Process

16 INSTITUTING DATA GOVERNANCE DO YOU HAVE A BOTTOM-UP PROBLEM? A specific project is hindered by the lack of data policies or decision making. A team on the business side needs help for a specific initiative and doesn t know where to start. There is a key project that could benefit from tactical data improvements, like metadata services or The poor quality of data is widely acknowledged for a particular: Source system Business application subject area (e.g., product) Business process (e.g., target marketing)

17 BOTTOM-UP APPROACH Exploration Additional Correction Profiling Capability Gaps Benefit Analysis Engage Sponsors & Stakeholders Choose Target Scope

18 SAS INFORMATION MANAGEMENT SUPPORT FOR ENTIRE INFORMATION MANAGEMENT CONTINUUM INFORMATION MANAGEMENT Strategy STRATEGY & IMPLEMENTATION SUPPORT Governance INFORMATION GOVERNANCE Capabilities DATA MANAGEMENT DECISION MANAGEMENT ANALYTICS MANAGEMENT SAS / Flux provides unified data management capabilities that include data governance, data integration, data quality and MDM SAS provides decision services that include business rules and workflow that facilitates integration of the information services into the business systems SAS provides complete analytics management that includes model management, deployment, monitoring and governance of the analytics information asset 18

19 THE ANALYTICS LIFECYCLE BUSINESS MANAGER Domain Expert Makes Decisions Evaluates Processes and ROI DEPLOY MODEL EVALUATE / MONITOR RESULTS IDENTIFY / FORMULATE PROBLEM How can you create a strategic advantage? DATA PREPARATION DATA EXPLORATION DATA SCIENTIST Exploration Visualization IT SYSTEMS / MANAGEMENT Model Validation Model Deployment Model Monitoring Preparation VALIDATE MODEL BUILD MODEL TRANSFORM & SELECT DATA MINER / STATISTICIAN Exploratory Analysis Descriptive Segmentation Predictive Modeling 19

20 SAS INFORMATION MANAGEMENT UNDERPINS THE BUSINESS ANALYTICS FRAMEWORK INFORMATION MANAGEMENT BUSINESS SOLUTIONS DECISION MANAGEMENT Driving the decision cycle: bringing analytics to the point of contact ANALYTICS MANAGEMENT Complete lifecycle management ANALYTICS INFORMATION MANAGEMENT BUSINESS INTELLIGENCE DATA MANAGEMENT Comprehensive quality, relevance, governance Trusted Information 20

21 SAS INFORMATION MANAGEMENT CAPABILITY VIEW INFORMATION MANAGEMENT DATA SERVICES INFORMATION SERVICES INFORMATION GOVERNANCE & COLLABORATION Management DATA INTEGRATION DATA QUALITY MASTER DATA MANAGEMENT DECISION MANAGEMENT Events, Workflow & Business Rules MODEL MANAGEMENT & MONITORING MODEL DEPLOYMENT & INTEGRATION Analytics Management INFRASTRUCTURE SUPPORT: Text & Unstructured Support, Security, Meta-data & Lineage, Monitoring & Deployment Infrastructure Support ENTERPRISE DATA ACCESS 21

22 THE CHALLENGE? DATA SIZE VOLUME VARIETY VELOCITY VALUE TODAY THE FUTURE 22

23 HOW DO WE MANAGE DATA IN THE PHYSICAL WORLD? 1. Acquire 2. Determine Relevance 3. Store Trash Cache Storage

24 HOW DO WE MANAGE INFORMATION IN THE IT WORLD? Users Systems Relevance is traditionally determined at query time... Acquire, Store, Analyze Queries A Big Analytics strategy requires a new approach... Stream it, Score it, Store it Acquisition Transformations Normalization DATA

25 INFORMATION MANAGEMENT STREAM IT, SCORE IT, STORE IT ENTERPRISE DECISIONS / ACTIONS / DATA LOW COST STORAGE RAW RELEVANT DATA 25

26 BIG DATA ANALYTICS REQUIRES ENTERPRISE ARCHITECTURE APPROACH MARKETING EDW SALES FINANCE SUPPLY CHAIN ADW RISK IN-MEMORY HR 26

27 Analytics at the Point of Decision Ultimately it s about making better decisions faster than your competitors Integrated information and analytic services directly in the operational applications Ability to process events & support workflow and case management Closed loop cycle that feeds the result of the information and analytic service back into the process 27

28 IF THE DATA IS NOT ACCURATE, RELIABLE AND COMPLETE

29 SAS INFORMATION MANAGEMENT

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