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Big Data and Analytics

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(US Navy Blue Angels, San Francisco Fleet Week - Jeff M. Wang)

 

Big data refers to extremely large datasets that are difficult to analyze with traditional tools. It is often boiled down to a few varieties of data generated by machines, people, and organizations. Big data is being generated by everything around us at all times. Every digital process and social media exchange produces it. Systems, sensors and mobile devices transmit it. Big data can be either structured, semi-structured, or unstructured. IDC estimates that 90 percent of big data is unstructured data. Many of the tools designed to analyze big data can handle unstructured data. The unstructured data usually refers to information that doesn't reside in a traditional row-column database. It is the opposite of structured data - the data stored in fields in a database.

Big data is arriving from multiple sources at an alarming velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics capabilities and skills. In most business use cases, any single source of data on its own is not useful. Real value often comes from combining these streams of big data sources with each other and analyzing them to generate new insights. The organization that can quickly extract insight from their data AND leverage the data achieves an advantage. 

Analyzing large data sets, so-called big data, will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.

Big data must pass through a series of steps before it generates value. Namely data access, storage, cleaning, and analysis. One approach to solve this problem is to run each stage as a different layer. And use tools available to fit the problem at hand, and scale analytical solutions to big data.

 


[More to come ...]

 

 



 

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