Data Mining: Why Is It Important? Know Its History, Techniques, Applications
Data mining is very useful because it helps firms increase revenues, improve customer relationships, cut costs, reduce risks and improve decision-making.
Data mining is the process of uncovering patterns, anomalies, correlations within large data sets and other valuable information to predict outcomes. Over the years, data warehousing technology and big data have evolved, accelerating the adoption of data mining techniques by businesses and organisations to transform their raw data into useful knowledge. A data warehouse refers to the secure electronic storage of information by a business or organisation and provides useful insight into the organisation's operations. Big data refers to large amounts of data which are hard to manage, and include structured, unstructured, and semi-structured data.
Data mining is very useful because it helps firms increase revenues, improve customer relationships, cut costs, reduce risks, and improve decision-making.
Data Mining: Purpose
According to International Business Machines (IBM), data mining techniques can be divided into two main purposes. One of the purposes of these techniques is to describe the target dataset, while the other is to predict outcomes through the use of machine learning algorithms.
Using these methods, one can organise and filter data. This helps in fraud detection and reveals security breaches.
Data mining can be combined with data analytics and visualisation tools like Apache Spark to make the extraction of relevant insights easier and faster than ever. Apache Spark is a multi-language engine that helps in data processing on a large scale.
Data Mining: History
Excavation of sites to discover hidden secrets has been performed for millennia. The process of digging through data to discover hidden connections and predict future trends is sometimes called "knowledge discovery in databases". The term "data mining" was coined in the 1990s. Data mining was founded by intertwining three scientific disciplines: statistics, artificial intelligence and machine learning.
Statistics is the numeric study of data relationships, artificial intelligence refers to the human-like intelligence displayed by software and machines, and machine learning describes algorithms that can learn from data to make predictions.
Data mining leverages the limitless potential of big data and affordable computing power.
Over the last decade, processing power and speed have advanced to a great extent, enabling the world to perform quick, easy and automated data analysis. According to the official website of SAS, a statistical, software suite that is used for data management, data analysis and visualisation, retailers, banks, manufacturers and telecommunications providers use data mining to discover relationships between price optimisation and demographics to how the economy, competition, risk and social media are affecting their business models, revenues, operations and customer relationships.
Data Mining: Importance
The volume of data produced is doubling every two years, making data mining extremely important to uncover valuable information from the data. Moreover, unstructured data alone constitutes about 90 per cent of the digital universe.
Data mining allows one to sift through all the chaotic and repetitive noise in data sets, accelerate the pace of making informed decisions, and understand what is relevant and then make good use of that information to assess likely outcomes.
Data Mining: Applications
Data mining is used in different fields, including education, fraud detection, sales and marketing, and operational optimisation.
Education: In recent years, educational institutions have started collecting data to understand their student populations and which environments will increase the chances of success. In the era of online courses, data mining can help observe and evaluate performance using dimensions and metrics such as student profiles, keystroke classes, time spent, student profiles and universities.
Fraud detection: Observing data anomalies is beneficial because it helps companies detect fraud. Banks, financial institutions and SaaS-based companies use data mining to eliminate fake user accounts from their data sets.
Sales and marketing: Companies can use data mining to observe consumer demographics and online user behaviour in order to optimise their marketing campaigns, and improve segmentation and customer loyalty programmes. Firms can set expectations with their stakeholders using predictive analysis techniques.
Operational optimisation: Organisations can reduce costs across operational functions through process mining, which leverages data mining techniques. This will enable organisations to run more efficiently and improve decision-making.
Research: Data mining is extremely beneficial in research because it helps scientists search for information relevant to their studies.
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