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Available On-Demand - This webinar has been recorded and is now available for download.

Strategies for Data Exploration and Analysis in the Age of Big Data Analytics

TDWI Speaker: Philip Russom, TDWI Research Director

Date: Tuesday, May 20, 2014

Time: 9:00 a.m. PT, 12:00 p.m. ET

Webinar Abstract

According to multiple TDWI surveys, the vast majority of IT users feel that big data is an opportunity, because of the new and more granular insights it provides about customers, operations, partners, and many other business entities and processes. Likewise, most users now see advanced forms of analytics as the primary path to reaping insights from big data, whether big data comes from traditional enterprise applications or new sources, such as Web applications, application logs, sensors, machines, and social media. For these reasons, TDWI sees many user organizations diving deeper into big data analytics.

The catch with wringing business value from big data via analytics is that both big data and analytics are quite diverse. For example, big data ranges from structured data (mostly relational) to semi-structured data (as in XML, JSON, CSV), unstructured data (mostly human language text), and streaming data (from sensors, machines, Web sites). Likewise, analytic applications can be based on many different technologies, including complex SQL, data mining, statistical analysis, predictive algorithms, clustering techniques, graph analytics, MapReduce, and multiple forms of natural language processing.

To become better equipped for handling all big data types and processing them via multiple analytic workloads, many user organizations are expanding their software portfolios to include a greater variety of data platforms and analytic tools. This Webinar will provide an overview of new technologies and user practices that lead to success with all big data and analytic applications, with a focus on multi-tool and multi-data-platform approaches.

You will learn:

  • Why big data analytics is becoming increasingly important for many businesses  
  • Leading applications for big data analytics, such as discovering the root cause of the latest customer churn, the hidden costs that are eroding the bottom line, new customer segments, or operational efficiencies 
  • Trends in designing data structures and integrating multiple tools and data platforms in a unified data architecture 
  • Emerging practices with advanced analytics, data exploration and discovery, and managing highly diverse big data 

Philip Russom, Ph.D.

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