DATA QUALITY STRATEGIES– RISK MANAGEMENT

VIEW POINT

Suvradeep RudraJan’2014

Agenda

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

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.

• Most organizations are struggling to measure ROI on risk management and communicate its process ,values and effectiveness to key stakeholders.

• Most organization struggles to access enterprise wide risk exposure

• Regulatory compliance posing greatest obstacles against developing enterprise risk management solutions

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

Top Risk Management Constraints

Health of Enterprise Data

Rise of Data Governance

"Data Governance is the exercise of decision-making and authority for data-related matters." --

Data Governance Institute

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

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

Data Quality StrategiesExample of Edit Checks

• Allowable Values• Field Length• Value Data Type• Contextual Cross-Field Checks• Validation, Enrichment and Deduplication

Data Aggregator Validation Rules

• Allowable Values• Field Length• Value Data Type• Contextual Cross-Field Checks• Prior Reporting Month Checks• Acceptance Thresholds

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?

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

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

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