TDWI | Training & Research | Business Intelligence, Analytics, Big Data, Data Warehousing

Onsite Education

• TDWI Data Governance Principles and Practices: Managing Data as an Asset

  Data is a critical resource for every organization. We depend on data every day to keep records, produce reports, deliver information, monitor performance, make decisions, and much more. The data resource is on par with financial and human resources as a core component of doing business, yet data management practices are often quite casual and unstructured. more

• TDWI Data Virtualization: Solving Complex Data Integration Challenges

  The data integration landscape has changed radically the past few years. What was once a relatively manageable problem of blending and unifying data from enterprise transaction systems has grown to encompass external data, Web data, clickstream data, end-user data, big data, cloud data, and more. more

• TDWI Data Quality Management: Techniques for Data Profiling, Assessment, and Improvement

  Data quality is one of the most difficult challenges for nearly every business, IT organization, and BI program. The most common approach to data quality problems is reactive—a process of fixing problems when they are discovered and reported. more

• TDWI Data Integration Principles and Practices: Creating Information Unity from Data Disparity

  Today’s business managers depend heavily on data analysis and decision-speed information, raising the stakes for data integration. At the same time, the work of integrating data has become increasingly complex. more

• TDWI Data Integration Techniques: ETL and Alternatives for Data Consolidation

  Data integration is becoming increasingly complex as new expectations and technologies change the face of data warehousing and business intelligence. The design of data integration systems was comparatively straightforward when extract, transform, and load (ETL) was the only option. more

• Data Preparation for Predictive Analytics

  This one-day session will expose analytics practitioners, data scientists, and those looking to get started in predictive analytics to the critical importance of selecting, transforming, and properly preparing data ahead of model building. more