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Data Processing and Big Data

            Big Data is a broad term that refers to work with data sets that are too large for traditional data processing applications to handle. As a result, distributed data bases are needed. Big Data can be broken down and defined using three V's; Volume, velocity and variety. Transaction-based data stored through the years, unstructured data streaming in from social media and Increasing amounts of sensor and machine-to-machine data being collected are all factors that contribute to the increase in data volume. In the past the issue was a matter of storing the large amounts of data, but with storage costs decreasing, the issue is seen in analyzing the copious amounts of data. The second V, velocity, refers to data streaming in at unprecedented speed so that it must be dealt with in a timely manner. The challenge is handling and responding to the data velocity in a timely manor. Lastly, big data can be defined by the variety that it often comes in. Structured, numeric data, as well as unstructured text documents such as email, video, audio, stock ticker data and financial transactions are some just to name a few.
             Big Data is important to the future because it is becoming a crucial way that some companies out perform their competitors. Many companies focus on data-driven strategies to innovate and capture maximum value. Big Data will help to create new growth opportunities and entirely new categories of companies, such as those that aggregate and analyze industry data. Another reason big data is important to the future is because as organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days and therefore expose variability and boost performance. The next generation of products and services can also be developed through the usage of big data.
             One of the biggest users of Big Data in the business world is UPS.

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