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2006 Jillian Macmurchy
 

2006 Jillian Macmurchy

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    2006 Jillian Macmurchy 2006 Jillian Macmurchy Presentation Transcript

    • Jillian Macmurchy Data Integration Solution Manager Enterprise Business Intelligence Platform SAS Enterprise Information Management
    • Agenda
      • Overview of Information Management
        • The impact of poor data management
        • The need for data management to evolve
      • Data Integration
        • The integration challenge
        • Smarter data integration
      • The role of Data Quality
        • The data quality process
        • Where to start?
      • SAS Enterprise Data Integration
      • Case Studies
    • Agenda
      • Information Management
        • The impact of bad data management
        • The need for data management to evolve
      • Data Integration
        • The challenge
        • Drivers for smarter data integration
      • The role of Data Quality
        • The data quality process
        • Where to start?
      • SAS Enterprise Data Integration
      • Case Studies
      • Metric mishap caused loss of NASA orbiter - NASA lost a $125 million Mars orbiter because an engineering team used British Imperial units of measure while the agency's team used the more conventional metric system.
      • In May 1999, during the Bosnian War, the United States inadvertently bombed the Chinese Embassy . The bombing stemmed directly from a data error.
      Famous Data Blunders
    • Business Impact of Badly Managed Data
      • Data broker ChoicePoint, Inc., payed $15 million in penalties for violating data security procedures and federal laws
      • Barbra Streisand pulled her investment account from her bank because it misspelled her name as ‘Barbara’
      • Going out of business… Enron, Andersen, Worldcom
    • Importance of EIM Re-enforced by Analysts
      • “ At too many companies decision makers lack the information they need to make decisions that will drive sustainable growth, thanks to disparate and disconnected legacy systems coupled with ingrained business processes that owe their survival to inertia alone.”
      • IQ Matters - Deloitte 2006
      “ Organisation’s should establish enterprise information management as a strategic business discipline that recognises information as a vital asset to be managed and maintained with the same rigor as applied to other significant assets (e.g. finance, real estate and people).” Ted Friedman – Research V.P. Gartner Group – Nov 2005
    • The Need to Evolve?
      • Ongoing process
      • Survival of the fittest
      • Constant – evolve or die
      • Gradual change into a better form
      "It is not necessary to change. Survival is not mandatory." -- W. Edwards Deming
      • 5 Levels of Evolution
        • Levels build on one another
        • Levels cannot be skipped
        • Competition will force evolution
        • Proactive better than reactive
      • 4 Critical Dimensions
        • People
        • Process
        • Culture
        • Infrastructure
      Information Evolution Model
    • Required Infrastructure Capabilities Infrastructure Data Quality Data Integration Metadata
    • Agenda
      • Information Management
        • The impact of bad data management
        • The need for data management to evolve
      • Data Integration
        • The challenge
        • Drivers for smarter data integration
      • The role of Data Quality
        • The data quality process
        • Where to start?
      • SAS Enterprise Data Integration
      • Case Studies
    • Pressing Business Issues
      • Market Liberalisation & increasing competition
        • Speed to market
        • Decision making
      • Compliance regulations
        • In-country (Government agencies, N.Z. Companies Office)
          • Public accountability
        • Worldwide (Basel II, SOX)
      • Industry Consolidation
        • Mergers & Acquisitions
        • Department Mergers
      • No global view of business, operations, citizens, customers, products
      • Maintenance of multiple SAP/ERP/Legacy systems is costly
    • Despite Awareness, Use of Information is Still Not Fully Efficient and Effective Sources: Delloite IQ Matters & Industry Studies 40% of IT budgets projected spend on integration. 30% of people’s time is spent searching for relevant information. 80% of CFO’s think there is room for improvement in timeliness, accuracy and availability of data for decision making. Only 1/3 of CFOs believe that the information is easy to use, tailored, cost effective or integrated. Data quality degrades at 2% or more per month 60% + of CEOs need to do a better job capturing and understanding information rapidly in order to make swift business decisions. 80% + of decision makers believe there are “multiple versions of the truth” Data volumes are doubling annually
    • Typical Scenario
      • New project initiated
      • Does some data analysis
      • Uses an ETL tool or hand-coding
      • Has several data quality issues
      • Implements successfully but late
      PeopleSoft Data Mart Transactional Operational Analytical Sources
      • Project 1
    • Enterprise Data Warehouse
      • Data warehouse project undertaken
      • Has shared sources with project 1
      • Re-analyses data
      • Uncovers some new data quality issues
      • Uses an ETL tool or hand-coding
      PeopleSoft Legacy Data Data Mart Transactional Operational Analytical Data Mart Sources
      • Project 2
    • Projects 3, 4, 5, 6….. Enterprise Data Warehouse
      • Fragmented tools and different approaches
      • Redundant business rules and multiple versions of the truth
      • Incomplete information flows
      • Inaccurate, incomplete, inconsistent data
      • Inability to keep pace with growing data volumes and velocity
      • Fragile, complex, hard-coded infrastructure
      Oracle Other sources SAP PeopleSoft Trading Partners Siebel Legacy Data Electronic Marketplaces Data Mart Transactional Operational Analytical Data Mart Operational Data Store Constant Changes to Business Requirements and Systems Landscape Consumer Portals
    • A Centralised Approach to Data Integration
      • The right data, to the right place, at the right time
      • Lower costs
      • Improve efficiency & repeatability
      • Assist with regulatory compliance
      • Faster time to market
      • Enhanced customer service
      • Increased productivity
      • Leverage existing assets
      End-to-end Metadata Management System Consolidations & Migrations Supply Chain Optimisation Business Risk and Compliance Business Integration and Business Performance Management Customer Intelligence and CRM Analyse Extract Cleanse Enrich Transform Load
    • Data Integration Maturity
      • In order to mature an organisation must:
        • Treat their data as a strategic corporate asset
          • The organisational structure should reflect this
        • Have a clear understanding of the data infrastructure across the enterprise
        • Implement an information governance process
        • Apply a cross-enterprise data management framework
    • Agenda
      • Information Management
        • The impact of bad data management
        • The need for data management to evolve
      • Data Integration
        • The challenge
        • Drivers for smarter data integration
      • The role of Data Quality
        • The data quality process
        • Where to start?
      • SAS Enterprise Data Integration
      • Case Studies
    • If your data was water would you drink it?
    • Reasons for Poor Data Quality
      • Multiple data standards (or none!)
      • Data buried in free form fields
      • Redundant data
      • Multiple data sources
      • Duplicates
      • Default values
      • Invalid range of values
      • Null values
      “ 60% of respondents claimed bad data and duplicate data as the primary reason for data integration problems”
    • The Real Reason for Poor Data Quality
      • Lack of investment in process
        • Strategy, Skills, Operations
        • Business Function
        • No ownership of data
      • Lack of Investment in people
        • Internal Staff, Internal Support
        • External Support, Training, Education, Skills
        • Staff turnover
      • Lack of investment in infrastructure
        • Storage, telecommunications, ETL Tools
        • Hand-coding
        • Data Cleansing, BI Applications
      Lack of ownership of information quality by the business 1
    • Data Quality is Now Achievable
      • More organisations are now beginning to focus on data quality realising the cost in both time and money
      • Major initiative’s such as compliance, CRM and Master Data Management require good quality data to be effective
      • Analysts believe that organisations that focus on data quality programs will gain competitive advantage
      “ Almost 50% of CRM projects are failing or have failed due to problems managing data quality or reconciling customer data”…Wayne Eckerson, The Data Warehousing Institute
    • The SAS Data Quality Process Profile Understand the quality of source data Quality Reconcile and correct inconsistent data Integrate Consolidate and link data across disparate systems Enrich Enhance the value of data Monitor Automatically identify invalid information Process
    • Where to Start?
      • Baseline Assessment, Measurement & Improvement:
        • Establish a baseline for data quality by assessing your data
        • Identify objective metrics for measuring the quality of data
        • Use the results of the assessment to find and correct problems in the data supply chain
        • Develop a process for on-going monitoring and auditing to measure data quality against the KPI’s by domain experts
      • Integrate and automate parts of the data quality process as functions within the data integration process
        • Investigate how to integrate data enrichment processes within existing data validation routines
        • Investigate how to integrate data enrichment processes with real-time data acquisition processes
      • Business ownership & accountability
        • Recruit an executive sponsor
        • Convene a data quality working group
        • Have the business appoint a data quality steward for each business unit
    • Today’s agenda
      • Information Management
        • The impact of bad data management
        • The need for data management to evolve
      • Data Integration
        • The challenge
        • Drivers for smarter data integration
      • The role of Data Quality
        • The data quality process
        • Where to start?
      • SAS Enterprise Data Integration
      • Case Studies
    • Data Integration capabilities…
    • With supporting services….
    • Able to Interact with all Systems
    • Critical Business Initiatives Rely on Integrated Information
    • Universal Data Integration
    • Agenda
      • Information Management
        • The impact of bad data management
        • The need for data management to evolve
      • Data Integration
        • The challenge
        • Drivers for smarter data integration
      • The role of Data Quality
        • The data quality process
        • Where to start?
      • SAS Enterprise Data Integration
      • Case Studies
    • CHEP - Case Study Business Case
      • Data Quality Improvement
      • Supply Chain Profitability Analysis
      • Building Marketing Capabilities
    • DATA QUALITY IMPROVEMENT Enabling the DQ Management Strategy
      • Sustainability
      • Establish clear accountability from Executive down
      • Establish data quality KPIs for balanced scorecard
      • Incentivise
      • Empower / enable self-audit
      1
      • Data Cleanse
      • Profile data
      • Establish quality rules
      • Automate data cleansing tasks where possible
      • Measure & report quality of data
      2
      • Single View of the Customer
      • “ Force” a reconciliation of data stored in multiple systems
      • Provide visibility of the data entered to the account owner
      • Maximise access / distribution of information via browser interface
      3
    • DATA QUALITY IMPROVEMENT Data Integration, Improvement & Enrichment PCMS ORACLE Financials TRANSPORT Dbase CMS Siebel
      • Analyse
      • Extract
      • Cleanse
      • Enrich
      • Transform
      • Load
      Informing data quality improvement in core systems D & B DATA End-to-end metadata SAS Server Reporting
    • DATA QUALITY IMPROVEMENT Result 83% 96%
    • Case Study – Data Migration WHAT: HOW: RESULT: WHY: Migrate seamlessly from one data warehousing solution to another (24 million customer records and 7TB) AA sold from its parent company, Centrica. Needed to build a new data warehouse that would be populated with information that was housed on Centrica's system and had less than 1 year to do it. Used data integration technologies to extract the relevant data and build the new data warehouse New Data Warehouse in place within 6 months and significantly reduced cost of operation, ownership over old system
    •  
    • How can SAS Help?
      • Provide a comprehensive and complete data integration platform
      • Provide industry and domain expertise built up over many years and projects
      • Implement using a proven data integration & project methodology
      • Provide the foundation for actionable BI
      • Data Quality Assessment engagement to help define a baseline measure
    • Data Quality Assessment
      • Services lead fixed length engagement to review a snapshot of your organisations data quality awareness
      • Analyse sample data
      • Summarise results and recommendations of data analysis in a report
      • Help you to develop a Return on Investment (ROI) model based on available information.
      • Review assessment findings with your team and executive sponsor(s).
      • Provide knowledge transfer on the methodology and results of the assessment.
    • Summary of Key Points
      • An enterprise wide technology based approach to EIM is critical
      • Data integration, data quality and metadata must be implemented using a common technology platform
      • Data quality is not a nice to have!
      • Use a proven methodology
      • Implement an information governance process
      • Treat data as a strategic corporate asset
      Information is the lifeblood of business; not a by-product of it
    • Thank You!