Using Hadoop to move past Too Much Data
Big data analytics are frequently touted for their world-changing properties, but many users feel stymied by the complexity of turning the vast amounts of information they're currently gathering into actionable insights. Using Hadoop clusters and databases, processing the stores of unstructured data that hold the most promise can be simplified, allowing organizations to make sense of information.
According to the most recent CMO Survey, marketing executives report that just 30 percent of projects incorporate analytics, down from 37 percent a year ago, Forbes reported. At the same time, spending on big data is increasing at an average rate of 66 percent for the next three years. Contributor and Duke University professor Christine Moorman speculated that this "utilization gap" stems in part from the fact that companies are not gathering "deep, non-quantitative insights."
Additionally, organizations find it difficult to understand what customers are actually saying when analyzing textual data, she said. AdAge's Simon Dumenco agreed, noting that marketers are building "bigger haystacks" of data without being able to find the needle.
"Generally, it's pretty much Too Much Data and/or Useless Data and/or Inaccessible Data and/or Nobody Knows Quite What To Do With It Data and/or … you get the idea," he wrote.
In a recent GigaOM article speculating that just 1 percent of available data is currently being analyzed, contributor Gurjeet Singh agreed that making use of the unstructured data being gathered is at the heart of generating new insights. He suggested that too much money is being used to gather new data and not enough time is being devoted to solutions for handling it. However, tools such as Hadoop are lowering the cost of analysis while getting at the core problem of making this analysis effective. Using Hadoop big data tools to dive into NoSQL databases, organizations can turn unused, diverse sets of unstructured data such as text, video and voice into tangible insights.