Data Mining V/S Data Extraction

Many developers often use the terms Data Mining and Data Extraction interchangeably. However, there is a stark difference between them. Data mining is a far more complex activity compared to data extraction, also known as web scraping.

In this article, we will explore the meaning of the terms Data Mining and Data Extraction and understand how they differ from each other.

What is Data Mining?

Data Mining is the technique of identifying patterns and trends by analyzing large data sets of raw data using statistical and Machine Learning methods. It holds great significance in real-time operations like identifying customer behavior and market trends and also plays a vital role in optimizing operational strategies for businesses.

Applications Of Data Mining

Data mining provides businesses with a competitive advantage and positions them strongly to make data-driven, informed decisions based on insights collected through enormous amounts of raw data.

Here are some key applications of data mining:

Marketing and Sales

Purchasing patterns and trends in customer behavior can be easily understood using Data Mining to create targeted marketing campaigns based on preferences, frequent purchasing habits, and geographical location. Similarly, the sales team can use data mining to provide better customer service, aiming to retain them for as long as possible and thereby decreasing the company’s churn rate.

Fraud Detection

Fraud identification is also one of the major applications of data mining. Deflection from a Common Pattern and identifying anomalies in a financial transaction allows businesses to spot suspicious handles that can pose a risk to their operations.

Manufacturing

Data mining can be leveraged to improve efficiency and productivity in manufacturing processes. It can be used to detect areas of improvement to remove product defects, predict potential machine failures, identify market patterns to boost capacity, and suggest energy-saving practices to minimize the cost of the end product.

Supply Chain Management

Businesses can use data mining to point out any flaw in the supply chain, allowing them to optimize the management of their goods and services. One can also optimize the inventory levels for future demands of their products by analyzing past trends in sales and market data.

Understanding the concept behind the process of Data Mining

The data mining process can be summarized into collecting and selecting the required data after cleaning it to remove the unnecessary dirty data.

Let’s discuss them!

Data Gathering

Data Gathering involves collecting data from multiple sources containing the relevant information for accurate analysis. The sources can include publicly available data on websites, databases, and APIs.

Data Cleaning

Data collection will provide you with the necessary data but in an unreadable form. The data is then cleaned afterward to remove any duplicity and missing values to avoid any inconsistency in the data and convert it into a usable format.

Data Selecting

Generally, we do not combine all of the cleaned data in the dataset. Data Selection helps us in extracting only the required data for the analysis.

Data Engineering

Data Engineering involves transforming and converting selected data into a suitable format for data mining.

Data Mining

To extract patterns and relationships between the data, we select appropriate algorithms and techniques, including classification, regression, and data clustering, among others.

Pattern Modelling

Pattern modeling can be explained as a computational representation of patterns, including the identification of recurring structures, relationships, and trends within the data.

Deploying and Implementing Insights

Finally, we deploy the information derived from data mining into practical applications, including existing systems or processes.

What is Data Extraction?

Data extraction, also known as data scraping or web scraping is the process of extracting data from publicly available sources such as web pages, databases, APIs, and other relevant sources.

It is the most essential step in the fields of business intelligence, data science, and market research to get valuable insights for making informed decisions.

Applications of Data Extraction

Data Extraction has various applications across several fields:

Business Intelligence

Improving operations and strategies is essential for companies looking to increase their conversion rate. That’s why companies extract consolidated data from various sources like CRM systems, sales databases, and more to analyze trends and consumer behavior and make informed decisions.

Market Research

A thorough data analysis is required to understand consumer sentiment, price fluctuations, and market trends. Data extraction allows businesses to collect data from various sources, including social media, news articles, and market reports, to optimize their marketing campaigns.

Financial Analysis

Data Extraction can help financial experts track potential future trends, market sentiments, and financial condition of their competitors, including their quarterly released balance sheets and results, by analyzing fluctuations in stocks and commodities pricing, currency exchange rates, and market sentiment.

Machine Learning Models

Data Extraction is the fundamental building part of the Machine Learning Models. It provides all the critical datasets to prepare and train the model to improve its accuracy and effectiveness.

Understanding the process of Data Extraction

The process of data extraction is part of the first step of the ETL(extract, transform, and load), which also serves as the foundation stage for laying the groundwork for subsequent data analysis and processing.

This process can be summarized into three simple steps.

Identifying the source

Data sourcing involves identifying multiple external relevant sources such as websites, documents, APIs, and databases.

Accessing and Collecting Data

After gaining access to the correct data source, you need to extract it using your dedicated scraper or any data extraction tool.

Storing Data

Finally, we store the extracted data in our respective database.

Differences between Data Mining vs. Data Extraction

Though data extraction can be considered a part of data mining, they both have major differences. Let’s discuss them!

Data mining can be defined as the process of identifying and exploring unknown and meaningful patterns from large datasets, while data extraction can be defined as the process of extracting data from relevant sources.
Data mining's main purpose is to uncover hidden patterns and generate valuable insights. Data extraction’s primary aim is to pull data from the respective source for further analysis.
The process of data mining is performed on structured data, while data extraction gathers data mostly from unstructured data sources.
Data Mining can be a complex task and can require a considerable investment. Data Extraction is easy to perform if the right tools and programming languages are selected.

Conclusion

In a nutshell, the comparison above presents significant differences; however, both processes play interconnected roles and are vital for accurately analyzing the data.

In this article, we learned the importance of the differences between data mata extraction and data mining.

If you think we can complete your web scraping tasks and help you collect data, feel free to contact us. Please do not hesitate to message me if I missed something. Follow me on Twitter. Thanks for reading!

Additional Resources

I have prepared a complete list of blogs to learn web scraping that can give you an idea and help you in your web scraping journey.

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