In today’s world, the growth of data has increased exponentially. One of the main reasons for this rapid data growth is the extensive use of social applications. Billions of users connect daily on social media platforms, share information, upload images, videos, and many more. And companies are using this as an opportunity for growth and means to outdo their competitors. In this article, we describe what big data is and what is big data analytics.
What is Big Data?
The term big data was first used in reference to the increase in data volumes during the mid-1990s. In the year 2001, Doug Laney, an analyst then at consultancy firm Meta Group Inc., expanded the definition of big data. In his explanation, this expansion of data described the increasing
- volume of data being stored and used by organisations,
- the variety of data being generated by organisations, and
- the velocity, or speed at which data was being created and processed at the time.
also popular as the 3Vs of big data.
Today, big data is the term that is applied to data sets that are beyond the ability of traditional databases to capture, manage and process with low latency. Big data has the following characteristics- high volume, high velocity, or high variety. Artificial Intelligence (AI), mobile, social, and the Internet of Things (IoT) are the driving factors for the complexity of data that originate from the various data sources today. For example, big data from sensors, devices, audio/video, networks, log files, transactional applications, web, and social media are generated in real-time and at a very large scale.
What is Big Data Analytics?
Big data analytics are the advanced analytics techniques used against very large, diverse data sets that include structured, semi-structured, and unstructured data from different sources and of different sizes. When big data is analysed, analysts, researchers, and business users can make better and faster decisions using data that were previously inaccessible or unusable. Businesses especially benefit from big data techniques like text analytics, machine learning(ML), predictive analytics, data mining, statistics, and natural language processing(NLP).
In India, Praxis is the pioneer in providing full-time data science and data engineering programs. We are motivated by our goal to develop tools that transform India into a data-driven, tech-driven digital environment. We are working to achieve this goal by providing academics and industry experts who would assist in this development.