Top Five Tools of Big Data Analytics

It's a little unfortunate that Big Data became the rallying name on a new generation of analytic capabilities. Unfortunate because the volume, velocity, and variety of data, the attributes of Big Data, all help define the complexity of growing databases and not the benefits.

The benefits of Big Data are when the organization, its customers, and its partners can derive intelligence, insight and value from storing, processing, and analyzing larger volumes, velocity, and variety of data. So I would propose that rather than attributes of the data, we focus on the capabilities that will help organizations derive more value from it. Here are some suggested tools of Big Data Analytics

  • Analytic Visualizations - Well designed visualizations are the baseline tools for both experienced data scientists and more novice analysts to make sense of data. Visualizations tell a story, and help the analyst to share what they've learned so that the data "speaks" for itself. A well designed visualization is far more powerful than a set of charts laid out in a presentation or pdf. The visualization should help the audience see "answers" while giving them views and access to the underlying detailed data. 
  • Data Mining Algorithms - If visualizations make humans smarter about data, data mining algorithms make machines more capable to automate the analysis.Clustering, segmentation, outlier analysis, and other algorithms help data scientists find the needles in the haystack or offer mechanisms to drill down into data intelligently. Data mining algorithms needs to be designed to handle both the volume of Big Data, but also the velocity.
  • Predictive Analytic Capabilities - Whereas data mining helps an analyst understand the volume and velocity of data, predictive analytical capabilities are a combination of algorithms and tools for the data scientists to complete forward looking analyses and statements. I'm calling these "capabilities" because the ability to predict requires both visualization (to help the scientists see the results), data mining (because you can't predict without using data mining techniques to understand the historical data) and potentially tools to annotate data. In some business settings, a predictive analytical tool is needed. In others, predictive capabilities are needed in visualization or data mining tools.
  • Semantic Engines - Technologists understand that the "variety" of unstructured data offers other challenges and requires a different set of tools to parse, extract, and analyze. Semantic engines have to be designed and positioned to bring new tools to the non-IT organization to mine and extract intelligence from "documents" - a business friends way of talking about unstructured data.
  • Data Quality and Master Data Management - Data quality and MDM are a mix of governance practices, organizational processes and technology tools to insure that there is a defined quality and management process around the underlying data.Organizations looking to derive value from Big Data have to take steps to insure that the quality level is understood and that management processes are in place to maintain and improve.
I don't want to undermine the technical challenges in Big Data - there are plenty starting with Big Data Infrastructure (shown as a foundational element in the diagram accompanying this post) but with requirements that permeate into the analytical tools. But if Big Data is really going to be the next big thing in Technology, we better focus our efforts on the benefits and value and not just the challenges.

7 comments:

  1. I covered the Microsoft tools for "Big Data" here: http://blogs.msdn.com/b/buckwoody/archive/2012/02/20/big-data-a-microsoft-tools-approach.aspx

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  2. Thanks for including the link. Some good tools listed there.

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  3. Great overview Isaac. Also great to see the industry finally adopting the "3V"s of big data over 11 years after Gartner first published them. For future reference, and a copy of the original article I wrote in 2001, see: http://blogs.gartner.com/doug-laney/deja-vvvue-others-claiming-gartners-volume-velocity-variety-construct-for-big-data/. --Doug Laney, VP Research, Gartner, @doug_laney

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    1. Doug - Thanks for the link. It's quite unfortunate that Dumbill's article on O'Reilly's site didn't reference the Gartner post. It certainly the one (or one of a few) that's popularized the 3 V's as a way to define BigData.

      But as I say in this post, as an industry, we better market and educate on the analytics value derived from BigData rather than the technical attributes.

      Thanks again for the comment.

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  4. Hi Isaac,

    Nice blog! Is there an email address I can contact you in private?

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