Understanding the Need, Types, Workflow & Applications of Data Mining Simplified 101

on data mining, Data Mining, Data Transformation, Tutorials • May 6th, 2022 • Write for Hevo

Applications of Data Mining_FI

Data Mining, also known as Knowledge Discovery in Data (KDD), is the process of revealing patterns and other valuable information from large datasets. With the development of data warehousing technology and the growth of big data, the adoption of data mining technology has accelerated rapidly over the past few decades, helping companies transform raw data into useful knowledge. However, despite the fact that this technology continues to evolve to process large amounts of data, business leaders still face challenges related to scalability and automation.

In this article, you will gain information about Data Mining. You will also gain a holistic understanding of the importance of Data Mining, its benefits, its step-by-step procedure, and the different techniques involved. It will also provide information about the applications of Data Mining and a few of the top tools that help in the process.

Read along to find out in-depth information about the applications of Data Mining.

Table of Contents

What is Data Mining?

Applications of Data Mining:  Data Mining| Hevo Data
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Data Mining is the process of analyzing data in order to uncover patterns, correlations, and anomalies in large amounts of data. These datasets contain information from employee databases, vendor lists, financial information, network traffic, client databases, and customer accounts. Moreover, Statistics, Machine Learning (ML), and Artificial Intelligence can also be leveraged in the process to explore large datasets.

Data Mining helps businesses develop better business strategies, improve customer relationships, reduce costs, and increase revenues.

The Data Mining process starts with determining the business goal. Data is then collected from various sources and loaded into Data Warehouses, which act as a repository for analytical data. Data is also cleansed, which includes the addition of missing data and the removal of duplicate data. Sophisticated tools and mathematical models are used to find patterns in data.

The results are compared to the business objectives to see if they can be used in day-to-day operations. Based on the comparison, the data is deployed within the company. The information is then displayed in the form of simple graphs or tables.

Why Data Mining is Important?

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Data Mining can help you generate ideas, thoughts, and opinions that you’ve never thought of before. This is because many teams aren’t yet very comprehensive. It provides an external perspective on the world and helps you make informed decisions for your business. 

Data Mining can also help you develop intelligent market decisions, run accurate campaigns, make forecasts, and more. With the help of data mining, we can analyze customer behavior and their insights. This leads to great success and data-driven business.

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How does Data Mining work?

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There are a huge number of applications for Data Mining processes. But, while undergoing any of it, a common 6-step procedure has to be followed.

These steps are as follows:

1) Understand Business 

Data Mining Projects start by first identifying and analyzing the company’s current situation, the project’s objectives, and scope. The stakeholders will discuss the definition of success for the project.

2) Understand the Data

Once the problem statement has been defined, it is time to determine what type of data is required to solve the problem and collect it from the appropriate sources.

3) Prepare the Data

This phase entails resolving data quality issues such as duplicate, missing, or corrupted data. It is then necessary to prepare the final data set, which contains all of the relevant data required to resolve the problem statement. The format of the data should be easy to understand and recognize by the stakeholders.

4) Model the Data

In this phase, you will employ numerous algorithms and modeling techniques to ascertain data patterns. Clustering, predictive models, classification, estimation, or a combination of these techniques can be used. You must create, test, and evaluate the model.

5) Evaluate the Data

In this phase, you must determine how and in what ways the results of a particular model will aid in meeting the business goal or resolving the problem. You must put the model to the test and assess its success.

6) Deploy the Solution

Finally, once the model has proven to be accurate and reliable, it is time to put it to use in the real world. The deployment can occur within the organization, be shared with customers, or be used to generate a report for stakeholders to demonstrate its reliability.

Types of Data Mining Techniques

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Some of the Data Mining techniques are as follows:

1) Data Warehousing

Data Warehousing (DW) is the process of collecting and managing data from a variety of sources and integrating it into a centralized location to provide meaningful business insights. Data warehouses are typically used to connect and analyze business data from heterogeneous sources. Spreadsheet tools, servers, and dedicated dataset software are a few types of Data Warehouses. A concrete Data Mining process is built on Data Warehousing.

2) Association 

Association is the process of finding correlations and causal relationships between different types of data. For example, if a customer in a particular industry buys a particular product most of the time, linking the two will help you make a stronger pitch later.

3) Classification

Classification is a simple process of splitting data into buckets based on certain common properties and characteristics. The hardest part of classification is deciding which category to put the data in.

4) Regression 

Regression is a Data Mining technique used to predict numbers such as item prices based on specific factors, characteristics, or data points. For example, if you want to predict the price of a house, you can consider the neighborhood, lot size, and so on.

5) Clustering

Like Classification, Clustering is the process of roughly organizing data into buckets based on similarity. The difference between Classification and Clustering is that Classification requires you to create categories, whereas Clustering makes it important to find similarities regardless of category.

What All are the Benefits of Data Mining?

The benefits of Data Mining are as follows:

  • Data mining assists businesses in obtaining knowledge-based information.
  • It can be implemented in both new and existing systems.
  • Data Mining enables businesses to make profitable changes to their operations and production.
  • It facilitates trend and behavior prediction, as well as the automated discovery of hidden patterns.
  • When compared to other statistical data applications, Data Mining is a more cost-effective and efficient solution.
  • Data Mining is a quick process that allows users to analyze large amounts of data in less time.
  • It enables data scientists to quickly initiate automated predictions of behaviors and trends, as well as uncover hidden patterns.

Real-World Applications of Data Mining

Some of the use cases and applications of Data Ming are as follows:

1) Applications of Data Mining: Market-Based Analysis

Market-Based Analysis is a concept that identifies a customer’s purchasing pattern on a regular basis. This analysis can help companies promote deals, offers, and sales. This is where data mining techniques are useful.

For example:

  • Data mining concepts are used in sales and marketing to improve customer service, cross-sell opportunities, and direct mail response rates.
  • Data mining can help with customer retention by identifying patterns and predicting likely defections.

2) Applications of Data Mining: Education

Data mining leverages the Educational Data Mining (EDM) method to analyze the education sector. This method generates patterns that can be used by both students and teachers. EDM  is to categorize and predict student performance, achievement levels, dropouts, and teacher performance. It can assist educators in tracking academic progress in order to improve the teaching process and help students in course selection. Further, it can also help the Educational management in developing the curriculum in a batter and efficient way.

3) Applications of Data Mining: Banking

Banks leverage Data mining techniques to better understand & comprehend market risks. It is commonly used to analyze transactions, card transactions, purchasing patterns, and customer financial data in credit ratings and intelligent anti-fraud systems. 

Banks can also use Data Mining to learn more about customers’ online preferences or habits in order to maximize the return on their marketing campaigns, examine the performance of sales & marketing channels, and manage regulatory compliance standards.

4) Applications of Data Mining: Healthcare & Medicine

Data Mining allows for precise & accurate diagnostics. With access to all of the patient’s information, such as medical records, physical examinations, and treatment patterns, better & effective treatments can be prescribed. 

Data Mining techniques also allow for more efficient, and cost-optimized usage of health resources by identifying risks, predicting illnesses in specific segments of the population, and forecasting the length of hospitalization.  Detecting irregularities and strengthening ties with patients through a better understanding of their needs are other benefits of using Data Mining in medicine.

5) Applications of Data Mining: Transportation

A diversified transportation company with a large direct sales force can apply data mining to identify the best prospects for its services. A large consumer merchandise organization can apply information mining to improve its business cycle to retailers. It also helps determine the distribution schedules among outlets and also analyzes loading patterns.

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Top 5 Tools for Data Mining

Some of the top tools used for Data Mining are as follows:

1) Rapid Miner

Rapid Miner is a Data Science software platform that provides an integrated environment for various stages of data modeling, including Data Preparation, Data Cleansing, Exploratory Data Analysis, and Visualization. Software-backed techniques are Machine Learning, Deep Learning, Text Mining, and Predictive Analytics. It is a user-friendly GUI tool that guides you through the modeling process. Written entirely in Java, this tool is an open-source framework and is very popular in the data mining world.

2) Oracle Data Mining

Oracle combines database technology expertise and analytical tools to deliver the Oracle Advanced Analytics Database as part of Oracle Enterprise Edition. There are multiple data mining algorithms for classification, regression, prediction, anomaly detection, and more. This is proprietary software backed by Oracle’s technical staff to help organizations build robust enterprise-grade data mining infrastructure. 

The algorithm integrates directly with the Oracle database core and natively processes the data stored in its own database, eliminating the need to extract the data to a stand-alone analytics server. Oracle Data Miner provides GUI tools that guide users through the process of creating, testing, and deploying data models.

3) IBM SPSS Modeler

IBM SPSS Modeller is a visual data science and machine learning solution. It helps in reducing the “time-to-value” by speeding up operational tasks for data scientists. Moreover, this tool has a wide range of use cases starting from will also take care of everything from data exploration to machine learning.

Leading enterprises use the software for data preparation, discovery, predictive analytics, text analysis, model management, and deployment. It enables organizations to access their data assets and applications easily. Its user interface has made it easier to work with data mining algorithms.

4) KNIME

KNIME stands for Konstanz Information Miner. It is an open-source data integration & analysis platform. Its distinguishing feature is its ability to build, deploy, and scale rapidly. This tool can be used for Predictive Intelligence and Analytics by people without much technical expertise. The product positions itself as an end-to-end Data Science solution, assisting in the creation and production of data science through a single simple and intuitive environment.

KNIME is built on the modular data pipeline concept and operates in accordance with it. It contains a number of data mining and machine learning components that are interconnected.

5) Python

Python is a free open-source language known for its fast learning curve. Combined with its functionality as a general-purpose language and a large library of packages that help build a data modeling system from scratch, Python is a great tool for organizations that want to tailor their software to specifications.

With  Python, you can’t get the unique features that proprietary software offers, but the functionality is there to help everyone choose and create their own environment with a graphical interface to suit their tastes. Python also supports a large online community of package developers that ensure that the packages provided are robust and secure. One of the features that Python is known for in this area is the powerful on-the-fly visualization that Python provides.

Conclusion

In this article, you have learned about Data Mining. This article also provided information on Data Mining, its benefits, its step-by-step procedure, and the different techniques involved. You also came to know about the applications of Data Mining and a few of the top tools that help in the process.

Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations with a few clicks.

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Hevo Data, with its strong integration with 100+ Data Sources (including 40+ Free Sources) allows you to not only export data from your desired data sources & load it to the destination of your choice but also transform & enrich your data to make it analysis-ready. Hevo also allows the integration data from non-native sources using Hevo’s in-built REST API & Webhooks Connector. You can then focus on your key business needs and perform insightful analysis using BI tools. 

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