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Copyright © 2006, SAS Institute Inc. All rights reserved.
Jillian MacmurchyData Integration Solution ManagerEnterprise Business Intelligence Platform
SAS Enterprise Information Management
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
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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
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
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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
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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
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Required Infrastructure Capabilities
Infrastructure
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
Sources: Delloite IQ Matters & Industry Studies
Despite Awareness, Use of Information is Still Not Fully Efficient and Effective
40%40% of IT budgets of IT budgets projected spend on projected spend on
integration.integration.
30%30% of people’s time is of people’s time is spent searching for spent searching for
relevant information.relevant information.
80% of CFO’s think there is room for 80% of CFO’s think there is room for improvement in timeliness, accuracy improvement in timeliness, accuracy and availability of data for decision and availability of data for decision
making.making.
Only Only 1/31/3 of CFOs believe that the of CFOs believe that the information is easy to use, tailored, cost information is easy to use, tailored, cost
effective or integrated.effective or integrated.
Data quality Data quality degrades at 2% or degrades at 2% or more per monthmore per month
60% +60% + of CEOs need to do a better job of CEOs need to do a better job capturing and understanding information rapidly capturing and understanding information rapidly
in order to make swift business decisions.in order to make swift business decisions.
80% +80% + of decision of decision makers believe there makers believe there
are “multiple are “multiple versions of the versions of the
truth”truth”
Data volumes Data volumes are doubling are doubling
annually annually
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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 DataMart
Transactional Operational Analytical
Sources
Project 1
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
DataMart
Transactional Operational Analytical
DataMart
Sources
Project 2
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Enterprise Data
Warehouse
Projects 3, 4, 5, 6…..
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 infrastructureOracle
Othersources
SAP
PeopleSoft
Trading Partners
Siebel
Legacy Data
ElectronicMarketplaces
DataMart
Transactional Operational Analytical
DataMart
Operational Data Store
Constant Changes to BusinessRequirements and Systems Landscape
ConsumerPortals
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A Centralised Approach to Data Integration
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
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
If your data was water would you drink it?
Copyright © 2006, SAS Institute Inc. All rights reserved.
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”
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
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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
Copyright © 2006, SAS Institute Inc. All rights reserved.
The SAS Data Quality Process
ProfileUnderstand the quality of source dataQualityReconcile and correct inconsistent data
IntegrateConsolidate and link data across disparate systemsEnrichEnhance the value of dataMonitorAutomatically identify invalid information
Process
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Where to Start?
2. 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
3. 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
1. 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
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
Data Integration capabilities…
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With supporting services….
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Able to Interact with all Systems
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Critical Business Initiatives Rely on Integrated Information
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Universal Data Integration
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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
Copyright © 2006, SAS Institute Inc. All rights reserved.
CHEP - Case StudyBusiness Case
Data Quality Improvement
Supply Chain Profitability Analysis
Building Marketing Capabilities
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Sustainability• Establish clear accountability from Executive down• Establish data quality KPIs for balanced scorecard• Incentivise• Empower / enable self-audit11Data Cleanse• Profile data• Establish quality rules• Automate data cleansing tasks where possible• Measure & report quality of data22Single 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 interface33
DATA QUALITY IMPROVEMENTEnabling the DQ Management Strategy
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PCMS
ORACLEFinancials
TRANSPORTDbase
CMSSiebel
• Analyse• Extract• Cleanse• Enrich• Transform• Load
Informing data quality improvement in core systems
D & BDATA
DATA QUALITY IMPROVEMENTData Integration, Improvement & Enrichment
End-to-end metadata
SAS Server
Reporting
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DATA QUALITY IMPROVEMENTResult
83%
96%
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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
Copyright © 2006, SAS Institute Inc. All rights reserved.
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
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.
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
Thank You!
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