Making Sense of Big Data Using Analytics

Michael Vizard

For all the talk about Big Data, one of the fundamental challenges facing IT organizations is actually making that data accessible to end users. What’s basically required is a new generation of analytics applications that are specifically designed to visualize trends hidden in massive amounts of data.

At the Strata + Hadoop World conference this week, there was no shortage of Big Data offerings from both new and established players in the analytics space. Dell, for example, showcased Kitenga Analytics, which is based on technology that Quest Software acquired shortly before it was acquired by Dell.

According to Joanna Schloss, a product marketing manager at Quest Software, Kitenga Analytics has now been integrated with the Quest Toad for Business Intelligence Suite in a way that masks the complexity of the Hadoop programming model.

In fact, masking the complexity of Hadoop from end users was a recurring theme at the show. Splunk released Splunk Hadoop Connect, which provides bi-directional integration between Splunk Enterprise and Hadoop. Splunk Enterprise provides a set of analytics tools that IT organizations can use to more easily analyze large sets of data that might originally have been collected using Hadoop, says Sanja Mehta, vice president of product marketing for Splunk.

Elsewhere, Tableau Software announced that it is expanding the number of partners that can serve as data sources for its analytics application that is delivered as service to include Cirro, DataStax, Digital Reasoning, EMC Greenplum, Hadapt, Hortonworks, Karmasphere and Simba.

According to Dan Jewett, vice president of product management for Tableau, the major challenge organizations will face as they get more familiar with Big Data is simply finding a way to get all that information in the hands of regular business analysts who don’t have a lot of programming skills. As part of that effort, he says Tableau is working on developing a richer set of APIs that will make it easier to connect the software-as-a-service (SaaS) application to any data source.

In a similar vein, Datameer released version 2.1 of its namesake analytics application, which adds connectors to more data sources, including Google AdSense, Facebook, Twitter, LinkedIn, Salesforce, Zendesk, GitHub, Atlassian JIRA and Google Analytics. Datameer also opened an Analytic Applications Market that is intended to make it easier for customers to discover analytics applications developed by data scientists.

Clearly, it’s still early when it comes to Big Data in the enterprise. But as more data becomes available, IT organizations are going to be asked to find tools that make sense of all that data as quickly as possible.



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