In today's competitive market, many organizations are unaware of the quantity of poor-quality data in their systems. Some organizations assume that their data is of adequate quality, although they have conducted no metrical or statistical analysis to support the assumption. Others know that their performance is hampered by poor-quality data, but they cannot measure the problem.

1. DATA QUALITY STRATEGIES– RISK MANAGEMENT
VIEW POINT
Suvradeep Rudra
Jan’2014

2. Agenda
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Top Risk Management Constraints
Health of Enterprise Data - Your first step
Data Governance
Building a Data Quality strategy

3. Executive Summary
In today's competitive market, many organizations are unaware of the
quantity of poor-quality data in their systems. Some organizations
assume that their data is of adequate quality, although they have
conducted no metrical or statistical analysis to support the assumption.
Others know that their performance is hampered by poor-quality data, but
they cannot measure the problem.
Enterprise are spending most of their time reconciling and validating
business data since underlying data originate from disparate systems.
Data quality is concerned not only with the structure of the dataset, but
with the usefulness and value of the information it contains, record by
record and field by field.

4. Top Risk Management Constraints
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Most organizations are struggling
to measure ROI on risk
management and communicate its
process ,values and effectiveness to
key stakeholders.
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Most organization struggles to
access enterprise wide risk
exposure
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Regulatory compliance posing
greatest obstacles against
developing enterprise risk
management solutions

5. Top Risk Management Constraints
• Top executive do not articulate risk
appetite properly
• Lack of human resource and
expertise
• The greatest setback posed by
various business units inside an
organization is to unable to make
Risk based decisions

6. Health of Enterprise Data
Rise of Data Governance

7. "Data Governance is the exercise of decisionmaking and authority for data-related matters." -Data Governance Institute

8. Data Governance steps
• Identify a data governor and the team
• Identify the Data governance territory
• Draw up data governance roadmap ,both short
term and long term plan
• Develop data governance strategy
• Implement data security strategy around it
• Calculate the value (ROI) good data
• Monitor and control data governance rules

9. Data Quality Strategies
• Breakdown the data problem in smaller
manageable component and resolve them
individually with specific solution
• Define and prepare data for analysis
• Build a data profiling platform to analyze the
data for 3 major category checks
• Structure Discovery
• Data Discovery
• Relationship Discovery

10. Data Quality Strategies
Example of Edit Checks
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Allowable Values
Field Length
Value Data Type
Contextual Cross-Field Checks
Validation, Enrichment and Deduplication
Data Aggregator Validation Rules
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Allowable Values
Field Length
Value Data Type
Contextual Cross-Field Checks
Prior Reporting Month Checks
Acceptance Thresholds

11. Data Quality Strategies
• Investigate and Identify data issues – Ask following Qs
• Do we have the data necessary to complete the project on time and
on budget?
• Does the data definition support our business requirements?
• Will the project be able to cost-effectively produce and maintain the
information required by the business?
• Does the data consistently and accurately represent the business
needs?
• Will the relationship between the data elements support the business
requirements?
• Will we be able to integrate, consolidate, aggregate, cross-reference
and pivot the data for usable reports?
• What data needs to be cleansed?
• What data needs to be transformed?
• Will the data be correct, consistent and stable?

12. Data Quality Strategies
• Build business rules and data standards
• Monitoring ,Metering Data using Scorecards
• Accuracy
• Atomic
• Complete
• Consistent
• Redundancy
• Timely ….. Etc.
• Raise and Report data issues

13. Data Quality Strategies
• Build business rules and standards
• Understand and integrate Metadata from all
integrating applications
• Utilize Reference data to build more robust rules
• Notification and Alerting process triggers based
on business rules
• Remediate and close all data issues
• Update relevant documents based on the data
issues found and remedies provided
• …….. follow the above strategy

14. Suvradeep Rudra is a Sr. Data Architect and has more than 10
years of experience in Data Management. He held a number
of roles at Caritor Inc. (now NTT DATA), Oracle, Deloitte
Consulting. Experienced in building overall data
strategy, tapping value from data assets and capabilities and
driving value to the business. He has worked in various
projects, establishing and building data management solutions
for customers in the industries such as High Tech, Health
Insurance, Oil and Gas, Payments services and Banking. His
experience ranges from Data strategy, Product
Strategy, MDM, Business Intelligence and Analytics, Data
Architecture (Data Warehouse), Data Governance.
He holds Masters in Computer Applications from University
of Madras, Chennai, India.
He can be reached via LinkedIn profile