RESEARCH & RESOURCES


Upcoming Webinars

International Broadcasts

TDWI On-Demand Webinars on Data Management, Analytics, & AI

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

Modernize Data Warehousing with Hadoop, Data Virtualization, and In-Memory Techniques

A growing number of user organizations are under pressure to capture and manage big data, as well as get business value from big data by analyzing it. To achieve these goals, many organizations are extending and revamping their data warehouse (DW) environments. According to TDWI surveys, the new technologies being adopted most by users who are modernizing their DWs include: Hadoop, Data virtualization, and In-memory techniques.

Philip Russom, Ph.D.


How Data Science Is Changing the Way Companies Do Business

Data scientists are a new type of analyst—part data engineer, part statistician, and part business analyst. And they're in high demand. Companies are combing through résumés and job websites, interviewing recent university grads, and poaching from their competitors in an effort to bring these new talents into their organizations. Of course, we’ve had statistical analysts in our organizations for years. Unfortunately, although these people are great at analyzing data, they are not always the best at explaining their findings to executives and business workers in understandable terms.

Colin White


Social Media Analytics: Getting Beyond Tracking the Buzz

There are more than 100 vendors offering social media analytics tools, but the reality is that many of them simply track “buzz”—meaning the volume of tweets, blogs, news items, and other places a brand name or topic might appear in social media during a certain time period.

Fern Halper, Ph.D.


Business-Driven BI and Analytics

Self-reliant and less dependent on IT, business executives and departmental/LOB managers and users are deploying the latest tools, services, and applications for business intelligence, analytics, and data discovery. Although IT is hardly disappearing from the picture, IT needs to adjust how it manages access to data sources and governs BI and analytics. Enterprise BI/DW systems also need to accommodate how users customize their BI and analytics as they see fit based on their roles. As BI and analytics tools become easier to use and more flexible, the trend toward business users directing their own BI and analytics experiences will accelerate.

David Stodder


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.


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