Data Mining
Facts that are collected through observation mostly numerical are known as Data. Data are a set of values of qualitative or quantitative variables about one or more persons or objects. They are collected together for reference or analysis. Data is information that has been translated into a form that is useful for the organization.
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What is data mining?
Data mining is the process of analyzing large databases in order to get new information. It is a subfield of computer science and statics whose main aim is to extract information from all available sources and is used worldwide. It includes statistics, machine learning, and database systems. This branch of data science compares millions of discrete data and businesses use this to track and forecast customer behavior. It can be considered as a process of converting raw data into useful information. This method of analyzing vast volumes of data to discover market insight can help companies overcome problems, minimize risks, and identify new prospects. It depends on data collection, warehousing, and computer systems.
How does it work?
In this process, large chunks of databases are analyzed to find recent patterns and trends. Data mining is used in diverse sectors such as market research, business strategies, CRM, fraud detection, business analysis, and risk management, among many others. The process is as follows:
- Business understanding: Extensive projects in data mining are initiated by first understanding the business situation, project goals, and objectives.
- Data understanding: The next step is to collect the data and get an understanding of the data set once the business problem is understood.
- Data preparation: It consists of the preparation of the final data set comprising all the necessary data to address the business query.
- Modeling: Appropriate modeling techniques are selected for the data in this phase.
- Evaluation: Assessing whether the results obtained from the model were effective and how would help the businesses to accomplish their objective.
- Deployment: Finally, when it is reliable and accurate, the model can be used in real-world applications.
Examples:
All the superstores, fashion brands, airlines, e-commerce applications, science and engineering, marketing, financial institutions, healthcare, etc. use data mining to find out the latest trends and patterns. For example:
- Airline companies offer loyalty cards and memberships to their travelers which gives the traveler extra benefits over the non-member. This enables the airline to track the frequent flying routes, booking period, and the price of the ticket of the traveler. This data is then stored and different coupons, schemes, and discounts are sometimes offered to the traveler depending on their bookings.
- An insurance company uses data mining in forecasting the customers who buy insurance policies, analyzes their claims, detects fraudulent activities, and determine risky customers. This will help the company to eradicate unnecessary losses due to frauds, etc.
- A vaccine producing company uses data mining to identify the chronic diseases and related symptoms, track high-risk regions, and population prone to the disease, and design programs to reduce the spread. With this information, the company will focus on producing a vaccine that helps in curing the symptoms.
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Future of Data Mining:
Businesses are now using data mining to catch up with others by making critical business decisions. It is going to be as common and ubiquitous as other technologies in the coming years. The five major future trends of data mining are:
- Multimedia Data mining: In this method, data is collected from various multimedia sources such as texts, images, audio, video, hypertext, etc. which are converted into different numerical formats.
- Ubiquitous Data mining: It involves gathering data about individuals from mobile devices which helps industries to study human-computer interactions.
- Distributed Data mining: In this method highly sophisticated algorithms are used to mine a huge amount of data from different company locations or at organizations so as to provide proper insights.
- Spatial & Geographic Data mining: It involves extracting information from environmental, astronomical, and geographical data like space images which is majorly used in geographic information systems and navigation apps.
- Time Series & Sequence Data mining: It involves studying cyclical and seasonal trends which helps in analyzing random events occurring apart from normal events. It mainly helps in determining customers buying patterns and their behavior.
Author: Urvi Surti
About the Author: Urvi is a commerce graduate and has a keen interest in Finance. She has completed her Chartered Wealth Management (CWM) from the American Academy of Financial Management and is currently pursuing a career in Financial Risk Management (FRM).
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