Data Vs Quality Data

1. Elena Fahrenholz
Akvile Gvildyte
Valeriia Khliustina
Lenia Miltiadous
The Data Quality
Challenge

2. 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

3. Data Quality aspects

4. Data Quality criteria

5. 5
How does data quality (or lack of) impact today's
business?

6. How does data quality (or lack of) impact today's
business?

7. 7
How does data quality (or lack of) impact today's
business?

8. How does data quality (or lack of) impact today's
business?

9. 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

10. The Case Study

11. 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

12. 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

13. 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

14. Solution
• Trillium Software System
• Focus data quality project on 3 years of
historical customer reservation data
• 3 Phases
DiscoveryDiscovery ImprovementImprovement MonitoringMonitoring

15. 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. 16
Customer data
management in practice: A
insurance case study

17. 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. 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

19. What could happen?
• The spurious results
• Impact to the cost
• Misleading scores of insurance analyze

20. Actions
• Data procession on the database software
• Forming a project team
• Generation of data-driven analytical pieces
• Data modeling and extraction

21. Results (I)
• Issues with software.
• Company cannot be sure about completeness,
accuracy, currency of data.

22. 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

23. 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. 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. 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. 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. 27
Data quality drivers

28. Thank you for your
attention!

29. 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/