Hadoop tutorial: what is Hadoop-able?
Is every piece of information under the big data umbrella eligible for inclusion in Hadoop architecture? To answer that question, it's important to first look at the ways that many companies use Hadoop. In some cases, Hadoop can be one component of a larger analytics platform, but often such potpourri approaches result in less than optimal insights. Consolidating big data strategies under the Apache Hadoop umbrella can add increased direction to information-driven initiatives and ultimately increase the ROI of such of insights.
A recent report by the Aberdeen Group found that businesses that used 'best-in-class' data analysis methods made significant gains in business critical functions (to be considered 'best-in-class,' at least 76 percent of business users had to be actively engaged with business analytics). In three major categories: business process cycle time; customer response time; and knowledge sharing and collaboration, the study found that in each metric, at least 80 percent of 'best-in-class' users reported marked improvement. This positive response indicates the efficacy of centralized analytical strategies, and gets at the basis of Hadoop, highlighted by Forbes contributor Bruno Aziza.
"Business users don't care about where and how the data is processed," he wrote. "When they hear big data or Hadoop, they don't think processing, they think insights."
In short, Apache Hadoop offers organizations the opportunity to define their own Hadoop-able data. An insight-driven strategy determines whether data has meaning.
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