Big data is a term used to describe data sets that are too large or complex for typical relational databases to acquire, maintain, and handle in a timely manner. Big data has one or more of the following characteristics: a large volume, a fast rate, or a wide diversity. Data complexity is being driven by new forms of artificial intelligence (AI), mobile, social, and the Internet of Things (IoT).
Big data analysis enables analysts, academics, and business users to make better and faster decisions based on previously inaccessible or unsuitable data. Text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing are examples of advanced analytics approaches that can be used individually or in conjunction with existing enterprise data to get new insights from previously untapped data sources.
According to Gartner, Big Data means- “Big data” is high-volume, velocity, and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”
The definition above clearly says that big data is a high-volume variety of data that helps to create detailed insights for an organization.
Use Cases of Big Data are following –
Customer Integration: Aggregate structured, semi-structured, and unstructured data from your customers interactions with the organization to get a 360-degree view of their behavior and motivations for better-targeted marketing.
Fraud Detection: Real-time transaction monitoring identifies anomalous patterns and behaviors that indicate fraudulent activity. Companies can identify and minimize fraud by combining big data with predictive/prescriptive analytics and comparing historical and transactional data.
Improved Customer Experience: The battle for clients is heating up. More than ever before, a clearer picture of the consumer experience is possible. To optimize the contact experience and maximize the value offered, big data allows you to collect data from social media, online visits, call records, and other sources. Begin sending targeted offers, reduce customer attrition, and address issues before they become a problem.
History of big data
The concept of big data originated back in the 1960 and 70s when data got started collecting in big data centers and relational database development took place.
Around 2005, data-driven websites like Facebook, YouTube got formed which use more and more data. To handle this large data, Hadoop (the open-source framework) was formed which makes data handling easier and cheaper to store.
With the growth of the Internet of Things (IoT), more and more devices and objects got connected to the internet, which results in the high volume of data being stored at someplace. Hadoop plays a major role in solving these problems.