New Directions in Enterprise IT Architecture: Achieving Business Value via New Data, Hadoop, and NoSQL
TDWI Speaker: Philip Russom, TDWI Research Director
Date: Tuesday, December 2, 2014
Time: 9:00 a.m. PT, 12:00 p.m. ET
Webinar Abstract
Organizations today need to manage, analyze, and visualize diverse data sources such as machines, sensors, social media, Web applications, and so on. Fully leveraging granular data from these sources drives insights into customer behavior, business operations, information security, risk management, and competition, which in turn increase growth, efficiency, and competitive success.
The catch is that this new data comes in many formats, models, and schemas—as well as schema-free forms—which is challenging IT environments consisting of data warehouse platforms and business intelligence software that were not designed for the new extremes of data diversity. Hence, many organizations are revising their IT enterprise architectures to include tools, platforms, and capabilities that enable them to manage, analyze, alert on, and visualize new sources of data.
As Hadoop matures and as users mature in their understanding of its practical applications, Hadoop is fitting into multiple spots in enterprise architecture. Hadoop works well for inexpensive batch storage. But trying to explore, analyze, and visualize data in Hadoop can mean setting up predefined schemas or moving data out of Hadoop to separate analytics data stores. New analytic platforms enable organizations to get up and going to explore, analyze, and visualize unstructured data in Hadoop or NoSQL stores in hours instead of weeks or months.
In this Webinar you will learn:
- Current evolutions in enterprise architecture and data architecture
- Practical applications of Hadoop and NoSQL data stores in enterprise architecture and IT infrastructure, as well as data warehouse environments and analytics
- How to enable business analysts, product managers, security practitioners, and other colleagues across departments and lines of business to analyze big data
- Why rapid time to value and lower risk of project failure are major criteria in evaluating a platform to search, explore, and visualize big data and new data
Philip Russom, Ph.D.