TueAM2: Data Quality Strategy and Architecture

Your Data Quality Strategy and should be aimed at deploying best practices for proactive assessment, measurement, inspection, monitoring, notification, tracking, and resolution of data quality issues. Identifying and implementing the associated componentry coupled with best practices in data quality management will help retool existing workflow processes with automated systems that can help reduce the need for manual data inspection while rapidly alerting data stewards to identified issues.

This tutorial looks at leveraging an organization’s existing data management infrastructure, available development tools, and practical data quality best practices to develop a formal framework for data quality management that organizes practices for:

Data quality rules management: Soliciting and managing data quality requirements and validation rules for data verification.
Data quality measurement and reporting: Enabling and invoking services to validate data against data rules and report anomalies and data flaws, both through notifications and through scorecards.
Standardized data validation: Validate existing processes while integrating services for data verification within newly-developed processes.
Source data quality assessment: Source data assessment and evaluation of data issues to identify potential data quality rules.
Incident management: Standardized approaches to data quality incident management (reporting, analysis/evaluation, prioritization, remediation, tracking).

David Loshin is the President of Knowledge Integrity, Inc, (www.knowledge-integrity.com), a consulting company focusing on customized information management solutions including information quality consulting and training, business intelligence, metadata, and data standards management. David is among Knowledge Integrity’s recognized experts in information management, contributing to Intelligent Enterprise, DM Review, and The Data Administration Newsletter (www.tdan.com) and is a channel expert for the Business Intelligence network (www.b-eye-network.com).

David's most recent book is "The Practitioner's Guide to Data Quality Improvement," and he has authored "Master Data Management" published by The MK/OMG Press in September 2008. His book, "Business Intelligence: The Savvy Manager’s Guide" (June 2003), has been hailed as a resource allowing readers to "gain an understanding of business intelligence, business management disciplines, data warehousing, and how all of the pieces work together." David has created courses for The Data Warehousing Institute, presented at DAMA/Meta Data conferences, taught tutorials on data quality at a number of venues, and as a representative of Knowledge Integrity is often called upon to provide insights and thought leadership to the Information Management community.