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RESEARCH & RESOURCES


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TDWI Webinars on Big Data, Business Intelligence, Data Warehousing & Analytics

TDWI Webinars deliver unbiased information on pertinent issues in the big data, business intelligence, data warehousing, and analytics industry. Each live Webinar is roughly one hour in length and includes an interactive question-and-answer session following the presentation.


On Demand

Big Data Analytics: Getting Business Value from Big Data via Advanced Analytics

The term “big data” has arisen in recent years to describe multi-terabyte data sets. Big data certainly has its challenges relative to scalability and data management. But it’s also useful for business intelligence purposes. In particular, the massive data sets of big data provide substantial data samples for various forms of analytics, especially advanced forms that are discovery oriented.

Philip Russom, Ph.D.


DIY BI and Analytics: Reaping Rewards and Avoiding Chaos with Self-Service BI

As the velocity of business increases, business users are less willing to wait for the IT department to create custom reports and analytics. Many users now expect to be able to interact with information and create their own views of data to address pressing business issues. At the same time, BI teams would like to off-load report and analytics creation duties to users and focus on more value-added activities.

Claudia Imhoff, Ph.D.


The Next-Generation Data Integration Hub: A Business-Friendly Publish-and-Subscribe Paradigm for Enterprise Data

Hub-and-spoke patterns have long been the preferred architecture for data and application integration technologies. That’s because hubs provide an easily understood design, reduce the number of interfaces required, foster reuse, and control data centrally for the purposes of optimization, data standards, and governance. However, there’s a new generation of data integration hub coming that even nontechnical users can employ to publish data, so that other users can easily subscribe to that data and bring it into their applications with minimal involvement from IT.

Philip Russom, Ph.D.


Practical Predictive Analytics for the Line-of-Business Analyst

Can business analysts effectively use predictive analytics? Adoption of predictive analytics and other advanced analytics has increased for a number of reasons, including a better understanding of the value of the technology and the availability of computing power. Economic factors are also a driving force in utilizing predictive analytics for business as companies strive to remain competitive. Companies want to better understand customer behavior. They want to better predict failures in their infrastructure. The uses for predictive analytics are extensive and growing.

Fern Halper, Ph.D.


Delivering on the Promise of Real-Time Business Analytics

Most business intelligence (BI) implementations query data that is historical, representing business processes that have already completed. Even so called real-time BI reports are generated using historical data. While this view of data has some value, it does not represent the complete picture. Fully informed business decisions require real-time insight into high-volume streaming data sources, current events, and ongoing business processes.

Philip Russom, Ph.D.


Faster, Must Go Faster: BI is Not Just for Tactical or Strategic Decisions – It’s Mandatory in Operations!

Business intelligence (BI) applications are playing an ever-increasing and important role in driving and optimizing daily business operations. This trend is leading to major changes in both the functionality and usability of BI-related technologies and products. Developing an operational BI strategy in this dynamic and constantly changing environment is not a simple task.

Claudia Imhoff, Ph.D.


Harnessing the Power of Big Data for Healthcare Organizations

Big data has arrived in healthcare, whether organizations are ready for it or not. Increasingly, healthcare provider and payer organizations have access to growing volumes of both structured and unstructured data. New sources, such as electronic health and medical records, doctors’ notations, claims data, clinical data, and external Internet/social media data, are mushrooming in size and importance for access and analysis. Big data holds the potential to give users a more complete and timely view. At all levels, users in healthcare organizations must leverage this data to discover insights for improving patient care, cost management, and operational process improvement.

David Stodder


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