Introduction 

What is the meaning data mining? The process by which companies turn raw data into information that is useful is Data Mining. In order to develop effective strategies in marketing, thereby decrease cost and increase sales, businesses use software to look for patterns in the large batches of data. Data mining largely is dependent on warehousing, computer processing, and effective data collection. These processes are useful for building machine learning models that power applications like search engine technology and programs that recommend websites. 

  1. What is Data Mining?
  2. Definition of Data Mining 
  3. Data Mining Uses
  4. Advantages of Data Mining  
  5. Why Data Mining is Important?
  6. What is the History of Data Mining? 
  7. How Data Mining Works

1. What is Data Mining?

Data mining is a process where hidden data are analyzed according to multiple perspectives and turning those data into useful information and can be put into action. Data Mining means collecting and assembling data from common areas, like data mining algorithms and data warehouses, look for patterns that can be used by businesses to improve customer service thereby increase their revenue. It is otherwise known as knowledge discovery or data discovery. It is very important in business intelligence to establish decisions that are driven by data.  

2. Definition of Data Mining 

Data mining is the process of analyzing large volumes of data so as to discover business intelligence which helps companies to solve problems, seize new opportunities, and mitigate risks. Some data mining tools used in the industry are Rapid Miner, oracle data mining, IBM SPSS Modeler, KNIME, Python Orange, Kaggle, Rattle, Weka, and Teradata. Data mining examples: include Groupon- Data mining allows Groupon alignment of marketing activities closely to customer preferences which analyze just 1 terabyte of real-time customer data that helps to identify the emerging trends. Data mining technique is used by Air France.

Trip searches, bookings, social media, flight operations, call centers, and interactions in the airport lounge are analyzed and a 360-degree customer view is created. Grocery stores use data mining by giving loyalty cards to customers that make it easy for the cardholders to avail of special prices that are not made available to non-cardholders. The above are a few examples of data mining helping companies to increase efficiency, streamline operations, cost reduction, and improve profits.  

3. Data Mining Uses

data mining is used in various fields like research, business, marketing, sales, product development, education, and healthcare. When used appropriately, data mining provides an extreme advantage over competitive establishments by providing more information about customers and helps to develop better and effective strategies in marketing which will raise the revenue and lower the cost. In order to achieve excellent results from data mining, a number of tools and techniques are required.  

Some of the most common Data mining concepts are  

  • Data cleansing and preparation- in this step transformation of data into a suitable form required for further processing and analysis such as identification and error removal and missing data. 
  • Artificial intelligence (AI)- analytical activities that are associated with human intelligence like reasoning, planning, learning, and problem-solving are performed by these systems. 
  • Association rule learning- also known as market basket analysis, these tools look in the dataset, for the relationship between variables such as concluding which products are purchased by the customers together. 
  • Clustering- is a process in which the dataset is partitioned into sets of relevant divisions called clusters, that would help the users to understand the structure or natural groups in the data.  
  • Classification- with the goal of predicting the target class for each and every case in the data, items are assigned by this technique in the dataset.  
  • Data analytics- data analytics is the process of evaluating digital information and converting it into information useful for business.  
  • Data warehousing- is the component of the foundational importance of most huge-scale data mining efforts with a large collection of data, that is used for decision making in organizations.  
  • Machine learning-  is a computer programmed technique, that makes use of statistical probabilities that gives the computer the capacity to ‘learn’ even without being clearly programmed.  
  • Regression- is a technique that is made use of to predict a variety of numeric values, including sales, price of a stock, temperatures, that are based on a precise dataset.  

4. Advantages of Data Mining  

The advantages of data mining are to enhances businesses to improve the time ahead by understanding the present and past and make precise predictions about the next level, increase revenue, understand the preferences and segments of customers, acquire new customers, improve cross-sales and up-sales, increase loyalty and retain customers, increase ROI from marketing campaign, fraud detection, credit risk identification and monitor operational performance. 

5. Why Data Mining is Important?

Data mining is important in marketing for exploring the large increase in the database and improving segmentation in the market. The valuable insight could be provided by extracting and combining multiple data from various sources. It is thus designed to explore data.

6. What is the History of Data Mining

The concept of data mining has been used by businesses for more than a century. It is not a new digital age invention. It became a great public focus in the 1930s. It first came into the Limelight when Alan Turing in 1936 introduced the universal machine concept that was able to perform computations that are similar to present-day computers. Hence it is not a new invention of the digital age.

So many improvements have taken place since then. In order to improve several aspects that include sales processes, interpret financials for the purpose of investment, businesses have started machine learning, and are making use of data mining. As a result of this data scientists all over the world have become very vital for business establishments and enable them to achieve greater business goals with data science. 

7. How Data Mining Works

Exploring and analyzing large quantities of information to derive relevant patterns and trends is involved in data mining. Data mining has many uses such as credit risk management, database marketing, spam email filtering, fraud detection, and also to fathom the opinion and sentiment of users. The data mining process is further divided into five steps. First data is collected and loaded into the data warehouse.

Then the data is managed and stored either in the cloud or in the in-house servers. The data is assessed by management teams, business analysts, and information technology professionals and they determine how to organize the data. Then based on the results of the users, it is sorted by the application software. Finally, the data is presented by the end-user in a format like a graph or a table that is easy to share. 

Conclusion 

When used appropriately data mining provides a great advantage over competitors and allows a business to determine the trend of customers to develop marketing strategies that are effective in bringing down costs and boost up revenue.  

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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