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RESEARCH & RESOURCES


Featured Webinars

  • How Generative AI and Large Action Models Will Transform the Way We Work

    In this webinar, TDWI senior research director James Kobielus will discuss the business process automation market, how AI is being adopted in this arena, and how generative approaches such as LAMs offer a fresh new paradigm for automating complex processes faster, cheaper, and more scalably than has been possible with traditional approaches. October 9, 2024

  • The State of Data Governance

    In this webinar, TDWI senior research director James Kobielus will discuss key findings from the recently examined data on coherent strategies for data governance. October 14, 2024

  • Unlocking the Power of Generative AI: 5 Essential Steps to Make It Enterprise-Ready

    Join Fern Halper, Ph.D., TDWI VP of research, and Informatica’s GVP of ecosystems and technology, Rik Tamm-Daniels, as they discuss key requirements for transforming generative AI applications into enterprise-grade solutions. October 17, 2024

Upcoming Webinars

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

Predictive Analytics for the Business Analyst

Predictive analytics has finally hit the mainstream as organizations realize its value and how it can help them become more competitive. The technology has also become easier to use. In fact, a current trend in predictive analytics is improving ease of use so that analysts supporting functions such as sales, marketing, and finance can use more sophisticated software.

Fern Halper, Ph.D.


Integrating Data in a Heterogeneous and Real-Time IT Environment

Integrating and transforming data for business decision making has always been a complex and resource intensive task. The industry move toward the use of cloud, mobile and big data technologies makes this task even more difficult given the heterogeneous nature of the many systems involved. This heterogeneity coupled with the need for companies to make faster and often close to real-time decisions requires organizations to modernize their data integration frameworks to integrate data not only at the database and file system level, but also at the application and business process level.

Colin White


Preparing Your Data Warehouse Environment for Big Data

Significant changes are afoot in data warehouse environments (DWEs). This is because organizations are evolving their DWEs so they can leverage big data for business value, practice advanced forms of analytics for new insights, scale up to larger user communities, enable self-service data exploration, and operate the business more competitively based on real-time and near-real-time data.

Philip Russom, Ph.D.


In-Memory Data Fabric: A Modern Approach to Data Warehouse Architecture

Defining the term data warehouse is getting more difficult. Many of the recent technology innovations in software and hardware have enabled a new generation of data warehouse architectures. In-memory processing coupled with today’s faster hardware gives new data warehouse architectures greater speed and scale.

Philip Russom, Ph.D.


Are Advanced Analytics Possible without a Data Scientist?

Data scientists are in high demand and companies are frantically looking for ways of overcoming the costs and shortage of experienced data science talent. To help solve this problem, both established and new start-up vendors are introducing products that claim to enable business users to do advanced and predictive analytics without the assistance of a data scientist.

Colin White


The Three Pillars of Agile Data Integration: Self Service, Rapid Data Set Prototyping, and Data Stewardship

Agile development methods—as applied in data disciplines—have experienced strong adoption by users in recent years, and for good reason. As more organizations “run the business” based purely on data (and compete and innovate), data management professionals are under increasing pressure to deploy data solutions into business use sooner, produce multiple solutions, and align data solutions with quickly evolving business goals. Hence, delivery speed, development productivity, and business alignment are the leading priorities (and benefits) for agile data management.

Philip Russom, Ph.D.


Graph-Based Analysis: Using Alternative Data Analytics to Analyze Behavior

Although many different techniques and technologies for big data appliances can increase scalable performance, the ways that certain applications are mapped to a typical Hadoop-style stack might limit scalability due to memory access latency or network bandwidth. Yet the promise of big data must go beyond increased scalability for known problems.

David Loshin


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