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/