Big Data Techniques are a group of methods used to analyze large, diverse data sets. The data may be as large as zettabytes and incorporate advanced analytical technology. It could contain structured, semi-structured and unstructured information. It can be generated by various applications and can be derived from a range of sources.
Customers generate lots of information every day when they send emails, use apps, interact with social media, and respond to services or products. They also generate data when they enter an establishment, speak with an agent from customer service or make a purchase online. Businesses collect this information in the course of their daily operations and use it for improving customer loyalty as well as expanding into new geographical areas, or creating new products.
Data is usually delivered in a different format than it was in the past. Data is no longer stored in databases or spreadsheets and instead comes from wearables, social media, and other technology platforms. Text, images, and videos are generally unstructured and have no rigid structure. This variety has helped put the “big” into big data.
The second characteristic of big data is speed. This is the speed at which data is generated and moved around. Every single one of these actions, such as sending an SMS, responding to a Facebook, Instagram or credit card purchase or making a purchase, produce data that must be processed quickly. Big data is difficult to handle due to this speed.