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The data quality challenge

This document discusses data quality and its importance for businesses. It provides a case study of how British Airways improved data quality which increased efficiency and decision making. An insurance case study shows how improving data quality led to better customer understanding and risk assessment. Finally, the document outlines key drivers of data quality including regulatory compliance, business intelligence, and customer-centric models.

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Elena Fahrenholz Akvile Gvildyte Valeriia Khliustina Lenia 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
5 How does data quality (or lack of) impact today's business?
How does data quality (or lack of) impact today's business?
7 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 impacts related 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
16 Customer data management in practice: A 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
18 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
24 Risk Regulatory compliances Data quality drivers Type of industry Increased numbers and different types of data sources Corporate governance MDM Duplicat ed effort Internal conditions Business drivers
25 Data quality drivers Business drivers Corporate Management/ Business Intelligence Poor data quality causes “blurry” management decisions No single point of truth Manual 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 processes Necessity 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
26 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
27 Data quality drivers
Thank you for your attention!
References http://prodataquality.com/DataQualityBasics.html http://www.sei.cmu.edu/measurement/research/upload/Loshin.pdf http://mitiq.mit.edu/IQIS/Documents/CDOIQS_200777/Papers/01_59_4E.pdf http://blog.masterdata.co.za/2011/10/24/what-are-your-business-drivers-for-data-governance/