There are various terminologies in digital technology that can make you confused. Such are the terms like big data and data analytics, that sound similar but have different meanings. They are important if one wants to use the data for business development. You can study the behavior of the customer, solve employee-related issues and get more profit.
But before understanding the difference between big data and data analytics, let us check out what these terms mean.
Big data is the large volume of data which is in various forms like unstructured and raw, structured and semi-structured data. Big data is complex as it is collected from different kinds of sources. It is difficult to manage big data through traditional data management tools. For processing and storing the data, you need specialized and effective tools.
Big Data is used in various fields such as communication, banking, financial services, media, and so on. There are also different sources of big data like a data warehouse, social media sites, sensors, and stock exchanges. These are later used to make strategic decisions.
The best part about Big Data is it has great velocity and so it helps in improving the operational work. Apart from that Big Data is secure and can help in preventing fraud. One can take advantage of it if you are aware of the type of technologies it uses.
Big Data is based upon variety which means data is available in various forms like audio, text, image, video, XML, and so on.
Data Analytics is more about studying historical data, analyzing it, and further deriving conclusions from it to provide effective solutions to the problems which businesses face. It helps in improving the overall performance of the business and enhances productivity. There are four types of data analytics which include:
Descriptive
Diagnostic
Predictive
Prescriptive
Data Analytics is applied in multiple disciplines such as gaming, traveling, healthcare, risk management, and research.
Some of the major advantages of implementing data analytics are quality control, real-time forecasting, data-driven marketing, and effective decision-making.
