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I wanted to share this link with members on Analytic Bridge - it represents a series of articles, insights and conclusions drawn about Big Data Technology, Trends and Success:

http://www.scoop.it/t/big-data-technology-applications-and-analytics

Here's a comment I posted about the Article entitled "The Role of the Data Scientist in Big Data" from Tech Republic. There are others on the page.    "As I read this article a few additional points came to mind.   As Data Scientists form a data plan, they typically take an inventory of available data including what may be classified as "dark data", unstructured data that the organization is not using today.   The data inventory will likely include disparate sources of data. The analytical advantages of integrating this data could improve the chances of the exceeding business goals.  During the inventory process, Data Scientists will need to assess how complete each field of data is.  How dirty is it?  Do you have what you think you'll need to achieve your objectives or do you need to collect new information?   The data inventory is directly tied to analysis of the data which is, in turn, tied to your goals.  One way to think about this is as follows.  For each goal the business has, form a set of questions that need to be resolved through big data analysis.  Resolution to these questions will prove or disprove the hypothesis you have formulated.   The data visualization you perform, the predictive models that you build, the dashboards you derive should all be in support of resolving the questions and therefore the hypothesis you are testing.    A data inventory is essential.   Listing a set of questions you want to resolve in support of organizational goals is essential.  Using a variety of analytical approaches to answer these questions will help you create and manage a complete big data program."

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Tags: analysis, analytics, applications, big, data, databses, enterpirse, hadoop, marklogic, nosql, More…platforms, unstructured, xml

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