The data quality challenge
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Transcript of The data quality challenge
Elena FahrenholzAkvile GvildyteValeriia KhliustinaLenia Miltiadous
The Data Quality Challenge
Agenda• Data quality’s impact on todays business
• British Airways case study
• Customer data management in practice: An insurance case study
• Main drivers of success
Data Quality aspects
Data Quality criteria
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How does data quality (or lack of) impact today's business?
How does data quality (or lack of) impact today's business?
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How does data quality (or lack of) impact today's business?
How does data quality (or lack of) impact today's business?
Example areas of business impactsrelated to data quality
Impact Category Examples of issues for review
Financial • Lost opportunity cost • Identification of high net worth customers • Increased value from matching against master customer database • Time and costs of cleansing data or processing corrections • Inaccurate performance measurements for employees
Productivity • Decreased ability for straight-through processing via automated services
Risk • Inability to access full credit history leads to incorrect risk assessment • Missing data leads to inaccurate credit risk • Regulatory compliance violations • Privacy violations
Trust / Confidence • Improved ease-of-use for staff (sales, call center, etc.) • Improved ease of interaction for customers • Inability to provide unified billing to customers • Impaired decision-making for setting prices
The Case Study
Data Quality Importance
•Check-in, ticketing and seat allocation processes
•Business intelligence
Commercial planning
Decision making
•Marketing and CRM
•Customer service
•New business software application delivery
Data Governance Review
•Data governance manager
•Staff members from each of
the key commercial functions
•Staff member of each business
area trained to take a ‘data
defining’ role
Issues• Legacy data
– Stored in many different formats
– Held to different standards
– Varying levels of cleanliness
• Live data feeds lower data quality than
expected
• ‘Point solutions’ implemented locally, rather
than holistically
• Little means of judging the quality of the data
Solution• Trillium Software System• Focus data quality project on 3 years
of historical customer reservation data• 3 Phases
DiscoveryDiscovery ImprovementImprovement MonitoringMonitoring
Benefits• Clean customer data• Increased recognition of the
importance of commercial data• ROI–More accurate and quicker analyses,
supporting faster and better strategic and operational decisions
– Data governance and data quality strategies working well
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Customer data management in practice: An insurance case study
Situation
• Understanding consumer’s behavior is critical in the insurance industry
• Lack of knowledge and comprehension
•Market pressure and competition
• Necessity to capture consumers’ data
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Role of data
• A key to successful financial processes
• Data is needed while making potential contracts
• To manage customers, the top quality data is required
• It helps to distinguish the needs of customer
• Possible ways of insurance
What could happen?
• The spurious results• Impact to the cost• Misleading scores of insurance analyze
Actions
•Data procession on the database software• Forming a project team•Generation of data-driven analytical pieces•Data modeling and extraction
Results (I)
• Issues with software.
•Company cannot be sure about completeness, accuracy, currency of data.
Results (II)
• Immediate informational reporting• Data mining techniques• Scoring, modeling and implementing
a consumers cross-sell pilot
• Better understanding of data• Time and cost saving• Reducing risk
Why?
• Non-accurate collection of data
• Complete trust in the system
• Careful revision of data• Facts before speculations• Appropriate “data on
demand” tools and methods
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Risk
Regulatorycompliances
Data quality drivers
Type of industry
Increased numbers
and different types of
data sources Corporate
governanceMDM
Duplicated effort
Internal conditions
Businessdrivers
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Data quality driversBusiness drivers
Corporate Management/ Business Intelligence
Poor data quality causes “blurry” management decisionsNo single point of truthManual effort necessary during report creation
Compliance Legal and regulatory risks through bad or incomplete corporate data Contractual breaches and liability cases likely
Process Integration along the Value Chain
Common material and partner data as a mandatory pre-requisite for efficient order-to-cash and procure-to-pay processesNecessity to establish unique data integration methodologies
Customer-centric Business Models
One-face-to-the-customer requires consistent and sustainable customer and contract data managementData integration necessary on business unit and regional level
Electronic Product Information
Customers and business partners demand high-quality electronic product informationNecessity to establish unique data integration methodologiesData integration necessary on business unit and regional levelInformation lifecycle management from F&E to Sales & Distribution
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Data quality drivers
• Basel II/III
• Sarbanes Oxley (SOX)
• Anti-Money Laundering (AML)
Regulatory compliances
Internal Drivers
• Data Warehouse / BI
• Data Migrations - Mergers and Acquisitions
Application Consolidation
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Data quality drivers
Thank you for your attention!
References http://prodataquality.com/DataQualityBasics.htmlhttp://www.sei.cmu.edu/measurement/research/upload/Loshin.pdfhttp://mitiq.mit.edu/IQIS/Documents/CDOIQS_200777/Papers/01_59_4E.pdfhttp://blog.masterdata.co.za/2011/10/24/what-are-your-business-drivers-for-data-governance/