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Data Governance Bootcamp // TDWI Data Governance Principles and Practices: Managing Data as an Asset
Building a data governance program is a complex process that focuses people, processes, policies, rules, and regulations on achieving specific goals for a managed data resource. Successful and effective data governance depends on achieving the right balance between control-oriented and collaborative governance to address the often conflicting needs of enterprise data management, self-service, agile development, big data, and cloud deployments.
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Data Monetization: Creating New Data-Driven Value Streams
In this workshop, Mr. Laney guides attendees through his formal approach to ideation, feasibility assessment, economic analysis, and the design, development, and support of data products and services. Participants will work together to generate and assess real ideas for their own business.
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Data Science Bootcamp // Data Sourcing and Preparation for Data Science
This course provides an overview of the data sourcing and preparation activities in data science and predictive analytics projects, highlighting key principles and practices and providing business examples to reinforce each concept.
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Data Governance Processes: A Framework for Business Success
This course will outline a practical approach to implementing a data governance framework with a focus on the key data governance processes to put in place, and how to embed data governance processes into core business processes to ensure compliance.
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Master Data Management: The Intersection of Architecture, Governance, and Process
This course provides practical strategies for gaining value from your MDM initiative, while at the same time ensuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
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Active Metadata for Data Fabric: Delivering Data Quality, Observability, and Trust
Understanding the best ways to capture metadata automatically at scale can then be followed by activating metadata to increase data observability, improve data quality and governance awareness, and ultimately build a trusted data architecture that can accelerate and improve decision-making in the business.
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Data Governance Bootcamp // TDWI Data Quality Management: Techniques for Data Profiling, Assessment, and Improvement
Data quality is one of the most difficult challenges for nearly every business, data management program, and BI and analytics team. Data resources power enterprise reporting, BI dashboards, self-service analytics, data science efforts, AI and machine learning, and more.
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Becoming a Data-Driven Organization: How to Build a Growth-Driven Business Data Strategy
In this class, Julia Bardmesser will show you how to join the dots between data capabilities and business outcomes using real-world case studies. You will also learn techniques for non-technical storytelling, so you can convert technical subjects such as master data management into concepts that business stakeholders can easily grasp.
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DataOps: Best Practices for Agile Data Management
DataOps is a process-focused and automated methodology for delivering data for machine learning and AI that concentrates on reducing cycle time and improving the quality of advanced analytics deliverables. DataOps builds on the concepts of DevOps, continuous integration and delivery (CI/CD), and agile.
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Decision Trees in Machine Learning: Building Explainable Models
This half-day session will dedicate half of its time to translating the business problem into a form that the algorithms can support and preparing data for optimal performance during modeling. The second half of the course focuses on different decision tree algorithms for classification and regression. Participants may consider “Predictive Modeling with Ensembles” as a natural follow-on to this course.
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