The traditional data processors are unable to process this data of this size. But, the unique feature of Big-Data analytic tools is that they can “mine” such huge data. This helps to address the problems of the business in ways other than normal. This was not possible with traditional data processing software.
History of Big-Data
Some services like Hadoop and NoSQL became popular within the same period. They use it for storing and analysing big datasets which handled the volume of data. The Advent of IoT on the horizon add an another axis to the volume of data. IoT provided services for networking more devices and objects to the Internet. This called for new techniques to handle ever-rising data volumes.
Three V’s of Big-Data explained
Variety: This refers to different types of data, which we encounter in the digitised world. Earlier data was available in an arranged relational database format. But, this handles the new types of unstructured data types in a different format. Some of the data such as text, video and audio is unstructured data. These need more preprocessing. This is more to test, check and derive meaning.
Some Use cases of Big-Data
It helps a host of business activities from customer feedback to optimisation of several key factors. Some of the popular use cases are:
Product Mix Optimisation
Many companies use Data analytics for anticipation of customer demand. They use them to identify demand for their different products earlier than actual. This help them to optimise their product mix and thus their profit margins.
It is impossible to predict machine failure only by analysing structured data. Some of the key values are machine age and running hours. That is because, the causes of machine failure are also embedded in various factors. These range from sensor data, temperature to energy consumption. Using these factors , it is possible to take up preventive maintenance. And, thereby save on reduced down time and spares costs.
The customers nowadays engage with the businesses in many digital transactions. These include Social Media Networks. The resulting data generated can help pinpoint specific issues. One of the key examples is whether they enhance or degrade the customer experience. The knowledge thus gained helps to maximise the value offered to the customers.
The digital world has thrown new challenges on the front of fraud affecting business organisations. A business may be in high risk of fraud by even expert teams. Big Data analytics helps to detect patterns in the data indicating such frauds.