TDWI Webinars

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.


Upcoming

Fern Halper

10 Considerations for Analytics in the Cloud

TDWI Research indicates that more companies are considering moving to public or hybrid cloud offerings for some or all of their analytics. Whether for customer, supply chain, or financial metrics, such organizations often collect large amounts of data—especially public cloud-generated data—and are interested in analyzing that information in the cloud.

Date: June 11, 2015

Time: 9:00AM PT

Fern Halper


Colin White

An Analytics Road Map for Modernizing the Data Warehouse

The field of business analytics is undergoing massive change as vendors introduce disruptive technologies such as analytic appliances, non-relational systems, cloud computing, and big data analytics. What vendors often forget in the rush to market these new technologies is that many organizations are struggling with performance demands, governance issues, and satisfying user requirements using their existing data warehouse and analytics environment, and may not have the resources to take advantage of new industry developments.

Date: June 16, 2015

Time: 9:00AM PT

Colin White


Your First Hire in Predictive Analytics (Hint: It’s Not a Data Scientist)

Many organizations are launching their first predictive analytics project and are not sure how or where to begin. With a great deal of hype around big data and data science, most companies understandably seek a data scientist with an extensive resume as their initial step.

Date: June 17, 2015

Time: 9:00AM PT

Keith McCormick


Colin White

Unifying the Traditional Enterprise Data Warehouse with Hadoop

The three-decade-old enterprise data warehouse is evolving into an enhanced data warehouse architecture where Hadoop acts as a supporting platform for traditional data warehouse activities. The challenge with this enhanced data warehouse approach is how to store and access data transparently regardless of its location and how it is managed. This presentation explores why organizations are adding Hadoop to the traditional data warehouse, presents use cases for such an environment, and takes a detailed look at why organizations need a common and transparent interface to both traditional relational and Hadoop data management systems.

Date: June 18, 2015

Time: 9:00 am

Colin White


Fern Halper

How the Right BI Can Fundamentally Change Your Organization

Self-service Business intelligence software is bringing analysts and business users together and driving the fundamental cultural shift making organizations truly data-driven. Broader access to reliable and curated data can improve business performance with top- and bottom-line impact. And more businesses are seeing this benefit as interest in self-serve BI tools grows, according to TDWI research.

Date: June 23, 2015

Time: 9:00AM PT

Fern Halper


Philip Russom

The Logical Data Warehouse: What it is and why you need it

A logical data warehouse is an architectural layer that sits atop the usual data warehouse (DW) store of persisted data. The logical layer provides (among other things) several mechanisms for viewing data in the warehouse store and elsewhere across an enterprise without relocating and transforming data ahead of view time. These views also serve as interfaces into disparate data and its sources. In other words, the logical data warehouse complements the traditional core warehouse (and its primary function of a priori data aggregation, transformation, and persistence) with functions that fetch and transform data, in real time (or near to it), thereby instantiating non-persisted data structures, as needed.

Date: June 24, 2015

Time: 9:00AM PT

Philip Russom


Philip Russom

Data Exploration and Analysis in the Age of Big Data: Finding Information and Gaining Results Faster than You Thought Possible

Organizations today are seeking to drive deep analysis, detect patterns, and find anomalies across terabytes or petabytes of raw big data. Whether you’re trying to discover the root cause of the latest customer churn or the hidden costs that are eroding the bottom line, you need analytic tools and techniques that work well with unstructured and multi-structured data in its original raw form.

Date: June 25, 2015

Time: 9:00AM PT

Philip Russom