Data Management Strategies for the CIO
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David Loshin is the president of Knowledge Integrity, Inc, a consulting company focusing on customized information management solutions including information quality consulting and training, business intelligence, metadata and data standards management. David is an industry thought-leader and one of Knowledge Integrity's most recognized experts in information management. He writes for many industry publications, creates and teaches courses for The Data Warehousing Institute and other venues, and regularly presents at the annual DAMA/Meta Data conference. David is the author of Enterprise Knowledge Management - The Data Quality Approach, which describes a revolutionary strategy for defining, managing, and implementing business rules affecting data quality management. His other book Business Intelligence: The Savvy Manager's Guide has been hailed as a leading BI resource. He can be reached via his website, knowledge-integrity.com.
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Contributions from David Loshin, President, Knowledge Integrity, Inc.
- How to get senior execs to buy into data governance
- How often should you update your data quality strategy?
- Implementing data quality tools but ignoring governance
- Open source tools to help with name matching
- Data governance trends: Scorecards & governance tools
- Who should own the data governance process and framework?
- Estimating the cost of poor data quality and customer data cleansing
- Can we enforce data integrity in a company to ensure accurate data?
- Are there data governance plans, templates or standard procedures?
- Should we buy data quality management tools or focus on policies?
- Resolving data ownership issues with external funders, organizations
- Where to find new academic resources on data quality best practices
- How to estimate customer data cleansing costs
- Data quality management for data warehouses
- What is high quality information?
- Data quality management tools: Where to get unbiased information
- Roles and salaries of data quality and governance analysts
- The role of data governance in unstructured data
- How to maintain data quality and provide high quality information management and analysis
- Data quality management pitfalls: Three common mistakes to avoid