Data Harvesting vs Data Mining

Data harvesting and data mining are two significant measures that could help pre-plan, organize, and manage clients' data to help teams excel in client assistance exceptionally well.

What is Data Harvesting?

Data harvesting means getting the data and information from an online resource. It is usually interchangeable with web scraping, web crawling, and data extraction. Collecting is an agricultural term that means gathering ripe crops from the fields, which involves the act of collection and relocation. Data harvesting is extracting valuable data from target websites and putting them into your database in a structured format.

To conduct data harvesting, you need to have an automated crawler to parse the target websites, capture valuable information, extract the data and finally export it into a structured format for further analysis. Therefore, data harvesting doesn't involve algorithms, machine learning, or statistics. Instead, it relies on computer programming like Python, R, and Java to function.

Many data extraction tools and service providers can conduct web harvesting for you. Octoparse stands out as the best web scraping tool. Whether you are a first-time self-starter or an experienced programmer, it is the best choice to harvest the data from the internet.

What is Data Mining?

Data mining is often misunderstood as a process of obtaining data. There are substantial differences between collecting and mining the data, even though both involve the act of extraction and obtaining. Data mining is the process of discovering fact-based patterns you generate from a large set of data. Data mining is interdisciplinary, integrating statistics, computer science, and machine learning rather than just getting the data and making sense of it.

Data mining has Four Key Applications. The first one is the classification. As the word implies, data mining puts things or people into different categories for further analysis. For example, the bank builds up a model of classification through applications. They gather millions of applications and each individual's bank statements, job titles, marital status, school diploma, etc., then use algorithms to calculate and decide which application is riskier. When you fill out the application form, they know what category you belong to and what loan applies to you.

Regression: Regression is used to predict the trend based on numerical values from the datasets. It is a statistical analysis of the relationship between variables. For example, based on historical records, you can predict how likely the crime is to occur in a specific area.
Clustering: A cluster is to group data points based on similar traits or values. For example, Amazon groups similar products based on each item's description, tags, and functions for customers to identify easier.
Anomaly detection: It is the process of detecting abnormal behaviors called outliers. Banks employ this method to detect unusual transactions that don't fit into your normal transaction activities.
Association learning: Association learning answers the question of "how does the value of one feature relate to that of another?" For example, people who buy soda in grocery stores are more likely to buy Pringles together. Market basket analysis is a popular application of association rules. It helps retailers identify the relationships between consuming products.

These four applications build the backbone of Data Mining. Data mining is the core of Big Data. The process of data mining is also conceived as Knowledge Discovery from Data (KDD). It illuminates data science, which helps study research and knowledge discovery. Data can be structured or unstructured and scattered over the internet.

Difference between Data Harvesting and Data Mining

Below are the following differences between data harvesting and data mining:

Data Harvesting	Data Mining
Data harvesting is extracting data from websites to retrieve quality information.	Data mining is executing data into an analysis pattern for better client study.
Data harvesting stresses finding data that will help brands execute, improvise, learn, and apply solutions to assist their needs.	Data mining stresses creating an analysis chart so that brands can conduct necessary actions according to clients' behavior patterns.
The main agenda of data harvesting is to collect information about clients whose behavior patterns will help you better understand their needs.	The main agenda of data mining is to create a solution that will matter or will alter in the next few years.
Data harvesting gives solutions that are coming directly from the mouth of what clients are expecting.	Data mining gives a predictive analysis.
Data harvesting provides solutions that are needed on the spot to assist clients.	Data mining provides a long-term solution to assist clients fluctuating preferences.
Data harvesting can be done automated or manually.	Data mining is an automated process.
Data harvesting extracts any data you require to easily have it in your system to keep a closer check on.	Data mining collects tons of data you have in hand and creates a clear report of what the next few years will be like regarding clients.
Another word for data harvesting is data scraping.	Another word for data mining is knowledge discovery in a database.
With data harvesting, the process is simple. You need to click on the website which you want to scrape data from, and the process begins henceforth.	With data mining, algorithms are used so that valuable data can be easily structured.
Data harvesting doesn't require an expert's attention. Even a beginner can conduct this process without any hassle.	A team of experts is required to conduct efficient data mining processes.
Data harvesting tools: Import.io, OutWithHub, Octaparse, Visual Web Ripper, and Web scraper (top 5)	Data mining tools: Rapidminer, Orange, Weka, KNIME, and Sisense (top 5)