Today, Almost all leading companies such as Amazon, Google, and Facebook store and process big data to gain insight into several previously unknown facts and figures. Looking at the potential benefits of storing and processing big data, organisations which were not previously into big data have started big data processing for better decesion-making.
In this video, you will first look at the volume of data that companies such as Facebook and Google deal with. then , you will go through some of the sources of big data. finally, you will learn about the industries where big data is being used. our industry expert will also cover use cases in the retail industry including product affinity, which is the process of identifying which products can be sold together.
In this video, we learnt that size is not the only criterion to define big data. big data can also be characterised in terms of speed at which the data arrives and the variety of the data. You also learned various examples of big data sources. Broadly, the sources of big data can be classified into three major categories:
- People: Today, people are quite active on the Internet through social networking sites such as Facebook, Twitter, and Instagram. On these platforms, they share a lot of information through posts; it could be a valid opinion regarding a political issue or a post about their recent visit to a hill station. This data is termed as ‘shared data’ — data that is shared by people. Even the user ratings for a movie or product can be referred to as data generated by people.
- Machine: Data that is generated by a machine/computer in a periodic manner, or at the occurrence of some event, is termed as ‘machine-generated data’. Some common examples of data produced by machines are from cell phone towers, RFID tag scanners and car sensors.
- Organisation: This refers to the data that is generated by an organisation itself. This data mostly has a well-defined structure. Some examples of data produced by organisations are internal sales data or customers’ demographic information. This kind of data can be integrated with some external data to generate useful insights. Archived data is also helpful in performing a comparative analysis with the latest data and for historical analyses, which is useful for prediction of future trends.
In this video, you also got an idea about the various industry use cases of big data such as in —
- Retail: In retail, big data is used in Clickstream analysis and Sentiment analysis which help in understanding buying patterns, customer purchase intent and many other insights.
- Financial Services: Big data is used to detect and stop fraudulent transactions in financial service companies. It is also used to design and modify predictive models on various investment strategies which are used by institutions to make decisions like how much money to invest, which sectors to invest in when to invest etc.
- Healthcare: In healthcare, predictive models can be generated for resource planning and distribution. It is also used in research labs and therapy. Because of the ability to remotely monitor health, patients no longer have to go to physicians for primary consultations.
- Manufacturing: In manufacturing big data can be used to read and analyse data from sensors attached to various machine parts. This helps in maintenance of equipment and prevent the loss of machine hours.
- In the next video, you will learn about the types of data.