'Big Data' Is the New 'Cloud'

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  1. Extracting data from external sources
  2. Transforming data to the proper format and cleaning up bad data
  3. Loading the data to a database for analysis

Data integration companies include Birst, IBM, Informatica, MicroStrategy, NetApp, Oracle, Pervasive, Pentaho, SAP and SAS.

The challenges of complex unstructured data are Messy Big Data. Although databases provide a valuable structure for analyzing data, most data doesn't neatly fit into a database. As companies seek to analyze video, voice, pictures and other unstructured data, they require tools that fall outside traditional data analysis. Each of these specialized forms of data typically requires its own form of analysis. For video, tools such as HStreaming and SenSen Networks are used to analyze the actual video content. For voice, there are a number of speech analytics companies that have traditionally focused on the contact center space, such as Avaya's Aurix, CallMiner, HP Autonomy, Nexidia, NICE Systems and UTOPY.

The ease of use for big data and integration of Big Data with standard business analytics make up Easy Big Data. One of the most challenging aspects of Big Data that it is complex and difficult to analyze. Hadoop analysis is not intuitive, but a number of visualization vendors have started to focus on the importance of being able to access and analyze Big Data. Companies focused on making Big Data easier to use and visualize include Microstrategy, Pervasive, Pentaho, Splunk, Qlikview, Tableau Software and TIBCO Spotfire.

Machine and sensor data for large operational deployments constitute Automated Big Data. This use case is most common in manufacturing organizations that use time-series analysis of transactional processes. Process historians created by companies such as GE and Rockwell have traditionally support these needs. Interestingly, as traditional enterprises increasingly use more sensors and machines to support IT, marketing, retail and other efforts, they will increasingly need to conduct similar analysis regarding the quality and deviation of their machine environments and may need this type of solution.

As you can see, there are many types of Big Data that exist in the general enterprise world and many more that could potentially be categorized as industry verticals such as financial services, government, health care and others are considered. All of these use cases, technologies and considerations are part of concept of Big Data.

As your clients start to think about Big Data, you should ask the following questions to better understand what they are truly looking for:

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