posted at 22:50
Author: Jordan Novet
Wed, 26 Nov 2014 06:30:35 +0000
5 big data implementation mistakes to avoid
In recent years, few terms have been as overused and misunderstood as "Big data." From making predictions about massive flu outbreaks with a Google flu trends solution, to tracking shopping trends and directing savings to customers, to making real-time trading decisions that impact companies' and individuals' bottom line positions - data has become the key to staying competitive in today's global economy. To understand the industry meaning of big data, and why big data has gotten so much attention, we need to break down the aspects of the database industry that have led to some of the challenges we face when managing and analyzing data today. Perhaps the most obvious reason for any organization to take on the challenge of big data is the ability to remain competitive by using available data to drive business intelligence that supports decision-making. Most database technologies require some sort of data definition or a schema that can slow projects down if some requirements aren't known at the start with respect to data needs. My data shows a growing trend toward the logical data warehouse: a warehouse that is really built on two or more physical databases integrated into a single access view. A LDW uniquely consolidates the indexes and data from almost any data source and makes it possible to build a customized view enabling any client to perform transactions or analytical queries. Whether one is dealing with financial data, health care-specific information, shopping analytics, published work, or government intelligence, the only consistencies in data are its ever-changing complexity and variety, as well as its increasing volume and demand. To deal with the massive and continued influx of data in a way that drives business value, organizations need to understand the reasons so many big data projects fail, so those failings can be avoided.

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