HI Team,

please help

Thanks for posting this, Kate.  Was just talking with someone last week about what Big Data is, and these additional criteria as posted by Bill Schmarzo are useful for understanding what makes it different from standard database analysis.

Bill Schmarzo contributes regularly to the InFocus blog, specializing in applications of Big Data. He wrote this helpful piece called simply "What Is Big Data?" that you might find helpful. Some of the answers covered include:

“Big Data” is more than just about data volumes, as it needs to encompass data velocity, variety, and complexity.  But big data is more than just about data.  Below are some of the drivers of change that need to be considered in any “big data” discussion or envisioning exercise.

• Detailed Structured Transactional Data:  POS transactions, call detail records, credit card transactions, shipping status updates, purchase orders, payments, shipments, account transactions
• Unstructured Data:  Web logs/clickstream, newsfeeds, social media, geo-location, mobile, consumer comments, claims write ups, doctor’s notes, clinical studies, image analysis, video analysis, audio analysis (Shazam)
• Machine or Device-generated Data:  RFID sensors, smart meters, smart grids, GPS spatial (Progressive Snapshot), micro-payments
• Data Exchanges/Data Aggregators:  Financial, credit, market, geographical, weather, automotive (Polk), legal (LexisNexis), Government data (Agriculture, Commerce, Defense, Labor, Health Services)
• Technology Drivers:  MPP architectures, columnar databases, in-database analytics, in-memory computing, NoSQL, parallel & distributed processing, advanced data visualization, mobile bi, collaboration, data mashups, search, cloud
• Advanced Analytic Tools:  SAS, R, statistical analytics, predictive analytics, data mining, machine learning, Hadoop (HDFS, Hive, HBase)
• Advanced Architectural Design:  Agile data warehousing, data virtualization, data fabric, high-performance data analysis (e.g., algorithmic trading), iPhone/iPad apps user experience, analytic sandboxes, experimentation, Data-as-a-Service, BI-as-a-Service