Technology trends such as big data, cloud, self-service, and agile challenge traditional data governance practices. At the same time increasing regulation of data and concerns about data privacy and security raise the stakes for governance. With new pressures, new technologies, and more data it is certainly time to learn and implement new governance techniques.
Data governance brings level of discipline to data management that is typical when managing financial and human resources. Data quality management is an integral part of data governance. Traditional data governance practices met the challenge of managing data as an asset until recently. Recent developments in the world of data are challenging the old model of policy- and enforcement-based governance. Agile projects often conflict with governance processes. Big data brings substantial changes to the scope and complexity of governance. Cloud deployment brings new issues that go well beyond the obvious concerns of security and privacy. Adoption of self-service BI and analytics radically changes governance culture and practices.
Data quality is one of the most vexing of governance issues. Most organizations have persistent and long-standing data quality problems that they correct reactively. A proactive data quality management program shifts the focus from correction to prevention and makes remarkable changes in the quality and value of your data.
The Data Governance Skills for the 21st Century workshop will cover essential techniques and best practices for data governance over three days of in-depth, interactive training.
1. Data Governance Fundamentals
1.1 Data Governance Concepts
1.2 Data Stewardship
1.3 Data Governance Processes
2. Data Governance Innovations
2.1 The Current State of Data Governance
2.2 Agile Data Governance
2.3 Big Data Governance
2.4 Cloud Data Governance
2.5 Contemporary Governance Techniques
3. Self-Service Data Governance
3.1 Big Shifts in Data Management
3.2 Governance and the Information Supply Chain
3.3 Technologies and Modern Data Governance
3.4 People, Processes, and Modern Data Governance
4. Data Quality Management
4.1 Data Quality Basics
4.2 Profiling Data
4.3 Assessing Data Quality
4.4 Fixing Data Quality Defects
4.5 Preventing Data Quality Defects