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Why Data Mining is Still Important in 2019

The influence of technology on the world is constantly growing. In this new age where everything is being shifted to the online sphere, data has risen as the new currency. It has become the most important asset of every organization. Hence the uses of data mining in today’s world cannot be undermined or understated.

Data mining seems like magic to most people. Extreme accuracy and precision with the data loads are what make this highly powerful and as much intriguing. Data mining can be said to be an offshoot of the entire set of database technologies. It is a multi-pronged process that employs many elements from statistics like classification, regression, clustering and association. Additionally, many other non-statistical tasks are also an integral part of it. Earlier, going into the technical lanes that lead to data mining, let us take a short halt and try to arrive at its definition.

What is Data Mining?

Data mining, in simple words, refers to the extraction of information from enormous sets of data. It is the process by which knowledge is mined out from data. The information so got has many applications like market analysis, fraud detection, science projects, production control, etc. With the advent of the digital age, it has found great application in market analysis.

Definitely, the next obvious question to strike your mind is the use of data mining. Here we have listed the most prominent uses of the same.

Uses of Data Mining

Data mining has proved to be a boon for the new-age marketers who have to face competition not only in the physical world but also in an increasing level of the same in the online world.

  • Identification of Patterns in Customer Data: The Internet generates a huge amount of customer data on a daily basis. This calls for an increased level of precision in making value for such huge sets of data. Data mining helps in deducing patterns in raw data that is generated by the internet browsing patterns of the customers. Mining discovers this information and puts it in the right perspective for the marketers. This helps them to decide the future marketing strategy.
  • Website Optimization: As the real essence of data mining lies in deriving useful information from raw data so this also enables the developers to make suitable changes to the websites in order to secure better ranking and visitors. Data mining provides the most vital information that can be used for making these changes.
  • Marketing Campaigns: Data mining is highly useful in truly evaluating customer response and interests over certain products and services. Even vague customer responses can be evaluated for determining patterns. Such information comes in handy for designing marketing campaigns.
  • Customer Satisfaction: It is by the streamlined information about the customers that marketers are able to make the marketing campaigns more personalized. This not only enhances customer satisfaction but also helps in building customer relationships.

Features of Data Mining Tools:

As seen above the primary use of data mining is to look for hidden patterns in big data sets.  It is a technique that is helpful in enumerating unexpected relationships in the data for making business gains. Many data mining tools have been in use in 2019. The most popular ones are:

  • SAS Data Mining: SAS stands for Statistical Analysis System and was developed for analytics and data management. The primary features include:
  1. Helps in the analysis of Big Data
  2. Ideal tool for text mining, data mining and also optimization
  3. It gives a distributed memory processing architecture
  • Teradata: It is a huge and parallel open processing system that is used in the development of large-scale data warehousing applications. It is run on Unix/Linux/ Windows server platform. Some of the notable features include:
  1. It can handle a max of 64 joins in a query.
  2. It comes with an extremely low cost of ownership and is highly easy to set up, maintain and administer.
  3. It is helpful in supporting SQL  for interaction with data that is stored in tables.
  • R-Programming: It is a language that is useful for statistical computation and graphics in addition to big data analysis. Some of the best features are:
  1. Highly effective in data handling and storage.
  2. It furnishes a whole list of mathematical operators for performing difficult calculations
  3. It also provides graphical facilities for data analysts.
  • BOARD: It is a Management Intelligence Toolkit that brings together the features of business intelligence and other types of corporate performance management. Most notable features are:
  1. Allows for Analysis, Simulation, Planning, and Prediction by making use of a single platform.
  2. Allows for building various customized and analytical applications.
  3. It reports by accessing multiple data sources.

Applications and Examples of Data Mining

Data mining has found its application in three major domains:

  1. Market Analysis: Various fields like customer profiling, cross-market analysis, target marketing, determining the customer purchase pattern, etc.
  2. Corporate Analysis: Herein it is useful for various fields like financial planning and evaluation of assets, resource planning, and even competition are important.
  3. Fraud Detection: It is primarily used in the field of credit card services, and even telecoms for the detection of frauds.

The idea was not to present a complete data mining tutorial but bring out the major aspects of data mining the relevance of which has grown over time and is expected to remain so for a long time in the future.

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