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RESEARCH & RESOURCES


Featured Webinars

  • From Migration to Modernization: Boosting Your Data Infrastructure for Success

    Join this TDWI webinar, with Fern Halper, TDWI’s VP of research; Arnab Sen, VP of data engineering at Tredence; and Sami Akbay, group product manager – data and analytics at Google, to learn how to transition from legacy systems to modern, cloud-based infrastructures, democratize data across the organization, boost operational efficiency, and enable advanced technologies for sustained growth. October 22, 2024

  • Driving Data Quality at Scale with High-Performance Observability

    In this webinar, TDWI senior research director James Kobielus will discuss the value of observability, lineage analysis, and other tools for driving data quality at scale in the cloud. October 24, 2024

  • Building Sophisticated AI Business Applications in the Cloud

    In this webinar, TDWI senior research director Fern Halper will provide an overview of best practices for building sophisticated, high-performance, and low-latency AI business applications in the cloud. October 29, 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

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


Crossing Healthcare Chasms with Information Integration Best Practices

Information integration is critical for becoming smarter with data. Across all industries, organizations want to use data analytics to discover how they can reduce costs without sacrificing quality and effectiveness. Firms both large and small want to pull diverse data streams together. Users can then uncover insights and innovate at a faster pace than their competition.

David Stodder


The Data Warehouse Modernization Tipping Point

Changes in the way that today’s business transpires have slowly been cutting away at the ability to meet fully the needs of data consumers. At some point, those who manage the data warehouse will hit a threshold or boiling point that will make modernization mandatory.

Philip Russom, Ph.D.


The Changing Fabric of BI Environments: Supporting Line-of-Business Personnel and Data Scientists

Today’s BI environments have split personalities. They must support the production of routine reports and analyses used every day for decision-making by line-of-business employees, and yet, also enable data scientists and data crunchers to “freewheel” through the data in an unplanned, experimental fashion. What magic is this? How can implementers create a sustainable BI environment with these two seemingly contradictory purposes? Does one replace the other? What are the technological requirements for this new world?

Claudia Imhoff, Ph.D.


Beautiful Data in the Eye of the Beholder: Data Visualization Best Practices

High-quality data visualization is critical to the success of business intelligence, analytics, and data presentation. Because graphical interaction with data is now the norm, users are excited—and also have increasingly high expectations. Technology is important, but you also need to execute best practices to avoid pitfalls and to create beautiful data visualizations that are clear, effective, and accurate.

David Stodder


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