Data Governance Skills for the 21st Century
Duration: Three Day Workshop
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.
Your Team Will Learn
- The practices, roles, skills, and disciplines essential to data governance
- The qualities that make good data stewards and stewardship organizations
- The data governance challenges and opportunities that arise from cloud services
- Risks, challenges, and opportunities of big data governance
- How to overcome apparent conflicts between data governance and agile projects
- How to overcome apparent conflicts of data governance with self-service BI and analytics
- How to create and analyze data profiles
- Processes and techniques for data quality assessment and data quality improvement
- Subjective and objective methods to assess and measure data quality
- Data quality and data governance professionals
- Business and IT executives facing the realities of agile, big data, and cloud
- Mangers, architects, designers, and developers of BI, MDM, and analytics systems
- Data stewards, data architects, and data administrators
- Everyone with a role in data governance or data quality management
- Anyone needing to modernize data governance for agile BI, big data, or cloud