TDWI | Training & Research | Business Intelligence, Analytics, Big Data, Data Warehousing

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

TDWI Speaker: Philip Russom, TDWI Research Director

Date: Thursday, December 5, 2013

Time: 9:00am PT, 12:00pm ET

Webinar Abstract

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.

Apache Hadoop is maturing as a loosely coupled stack for inexpensive batch storage, where you don't need to know data formats or schemas to store and process the data. Hadoop can store an abundance of data and has the potential to serve a variety of analytic and data science applications, from e-commerce customer segmentation and A/B testing to fraud detection, machine learning, and medical research. But trying to explore, analyze and visualize data in Hadoop has often meant significant work manually writing jobs or setting up predefined schemas, which takes time and keeps vital data out of the reach of business and IT. New tools and analytic platforms allow even nontechnical users to explore and understand massive data sets. They are enabling organizations to get up and going to explore, analyze, and visualize unstructured data in Hadoop in hours instead of weeks and months.

You will learn:

• Why analytics that tap into raw, unstructured machine data is becoming increasingly important and valuable  
• Why rapid time to value is a major criterion in evaluating solutions that search, explore, and visualize unstructured data 
• How to enable colleagues across departments and lines of business to analyze unstructured data without specialized skill sets 
• Ways to search and explore unstructured data on the fly without fixed schemas 

Philip Russom, Ph.D.

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