Big Data refers to a large volume of data characterized by greater variety in greatly increasing volumes and ever-growing velocity. These three characters are known as three Vs of Big Data. The traditional data processors are unable to process this data because of its sheer size. However, the unique feature of Big Data analytic tools is that they can “mine” such huge data to address the problems of the business in ways that was not possible with traditional data processing software.
History of Big Data
The use of term Big Data is relatively new, but the problems of handling large data were encountered and handled as early as in 1970’s with the advent of relational database. By 2005, it was becoming clear that huge unstructured data generated through YouTube, Facebook, and similar online services. Hadoop and NoSQL became popular in the same period for storing and analyzing such big datasets. Advent of IoT on the horizon provided for networking more devices and objects to the Internet and called for new techniques to handle ever-rising data volumes.
Three V’s of Big Data explained
Volume: Big data is characterized by huge amount of data, which is also unstructured data. For example, this may be click streams on the business organization’s webpages, Twitter data feed or even data streamed from sensor enabled equipment. This could run into terabytes of data or even petabytes for large organizations.
Velocity: Velocity refers to the high rate of data received and acted upon. This characteristic also differentiates between traditional data processing programs as in this case the data accumulated is in relational and structured format. The processing of big data is thus required to operate in real time on high velocity input of data streams.
Variety: This refers to different types of data, which are encountered in current digitized world. Traditionally, data was available in a neatly arranged relational database but big data handles new types of unstructured data types. Partially structured and unstructured data such as text, video and audio need additional preprocessing in order to evaluate, support metadata and derive meaning.
Some Use cases of Big Data
Big data is helping a host of business activities from customer feedback to optimization of several key factors. Some of the popular use cases of big data are:
Product Mix Optimization
Many companies use Big Data analytic for anticipation of customer demand for their different products much earlier than their actual demand shows and thus help them to optimize their product mix and thus their profit margins.
it is impossible to predict machine failure only by analyzing structured data such as machine age and running hours, because causes of machine failure are also embedded in factors such as sensor data, temperature and energy consumption. If these factors are analyzed, it is possible to take up preventive maintenance and save on reduced down time and spares costs.
As the customers are connected with the businesses in many digital transactions (including on Social Media Networks), the data generated can be used to evaluate this huge data and pinpoint at the specific issues that either enhance the customer experience or degrade it. The knowledge thus gained and followed by actions, helps to maximize the value offered to the customers.
The digital world has thrown new challenges on the front of fraud affecting business organizations. A business may be in high risk of being defrauded by even expert teams. Big Data analytics are used to detect patterns in the data indicating such frauds.
Big data has a great impact on boosting operational efficiency. It is now possible to analyze deviations in any of intermediate processes, analyze factors causing outages and losses and optimize production based on future demand.