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RESEARCH & RESOURCES


Featured Webinars

Upcoming Webinars

  • Expert Panel: Real-Time Analytics Use Cases and Architectures

    In this expert panel, TDWI senior research director James Kobielus will discuss the chief enterprise use cases for real-time analytics and the principal architectural considerations for data, analytics, and IT professionals seeking to optimize their infrastructures for these applications. December 9, 2024 Register

International Broadcasts

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

Bridging the Business and IT Divide: Ensuring Success for Your Self-Service BI Project

You have attended the conferences, read the books and articles, gotten advice from vendors and implementation companies alike, and maybe even created your first BI prototype. You think you are ready to take on the big project. Compare your progress to the best practices checklist offered in this presentation.

Claudia Imhoff, Ph.D.


Text, Text Everywhere: Considerations for Gaining Insight From Text

Text analytics is rapidly gaining market momentum among organizations that want to gain insight into their unstructured text and use it for competitive advantage. Factors fueling market growth include a better understanding of the technology’s value, maturing of the technology, and the computing power to help analyze large amounts of data. Organizations are using the technology to address a variety of business problems.

Fern Halper, Ph.D.


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.


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