Data Mining vs Data Extraction – Meaning and Key Difference

If you are more or less familiar with web scraping, you’ve probably heard about data mining, data science, or big data. Let’s focus on the most usable methods associated with web scraping: data collection vs. data mining. There is a difference between these two methods. In this article, we’ll tell you about each method separately and summarize the key differences between data mining vs data extraction to give you more perspective on this topic.

It is the approach of analyzing patterns from a unique perspective and summarizing them into usable information for effective business decisions. These analyses are done with the help of mathematical and statistical algorithms to get specific insights. The process is also known as KDD (Knowledge Discovery in Data). One of the key benefits of knowledge analysis is the prediction of events, which is a prevalent challenge for business organizations. What does it mean to mine data? The best answer can be formulated according to French statistician Jean-Paul Benzeeri: “Data analysis is a tool for extracting the jewel of truth from the slurry of information.” Here, you may wonder what the difference between data mining and data analysis is. The goal of data analysis is to organize knowledge in order to find useful insights, while knowledge extraction makes models that help find patterns and connections.

Data mining involves extracting valid information by using advanced approaches like machine learning techniques. However, applying the right algorithm to acquire the necessary knowledge to solve a given business challenge is a skill that can be developed through practice.

Data mining is equivalent to information harvesting, knowledge extraction, pattern analysis, and knowledge discovery in databases. To understand how the information harvesting process is organized, let’s proceed with the following steps.

There is a variety of information sources that should be combined to find what you are looking for. You need to identify the content first, then the dataset from which you’ll be able to extract the valuable elements.

In terms of the complexity of the content you are dealing with, the selection of information can be simple or complicated, but the whole volume will not be useful. Things become simple when you can make selections and integration based on past analysis of similar content sources.

When the material is selected, it must go through cleansing, aggregating, and formatting processes. The analyzing patterns should be compacted in a way that makes an efficient mining process possible.

Now, it is time to identify valuable knowledge patterns from the enormous volume of material and present them in structured models using clustering and classification techniques.

When you are done modeling, you can test the info patterns based on specific measures, summarize and visualize in a readable form, and represent the mined details as reports or tables.

Today, knowledge extraction is a must-have technology for any company dealing with information. However, it can be somewhat abstract for a non-expert, so let’s look through general use cases to understand what data mining can do for business growth.

Knowledge extraction is the most powerful tool to predict trends and behaviors in the entire financial market and make the right decision regarding monetary investments. Using statistical figures and machine learning tactics provides you with an effective and accurate analysis to estimate the business’s stability and profitability. Trends in sales, inventory checks, and income analysis through knowledge extraction will help to determine the worth of your business.

Sales forecasting using information harvesting is the most accurate prediction method. Through pattern analysis, you may predict your short-term or long-term sales based on customers’ purchase history, industry trends, and comparisons. Sales forecasting will also provide insights into how you should manage your company resources, workforce, and cash flows.

Customer retention is one of the more important challenges in today’s competitive commercial arena, especially in the sales and services industries. Web scraping solutions that integrate pattern analysis techniques help test customers’ lifetime value and market segmentation. Thanks to this form of knowledge extraction in data mining, you can identify when your customers are going to leave you and suggest incentives to persuade them to stay.

In order to detect fraudulent activities, organizations and business entities trust special pattern analysis techniques. For example, pattern analysis is widely used in identifying and fighting cyber credit-card fraud thanks to competent AI techniques that are implemented to detect fraud from anomaly patterns gathered from extracted data.

Nowadays, knowledge analysis is an essential technology for companies and large enterprises in many spheres, but it is still developing and may come with temporary — yet noteworthy — disadvantages.

The knowledge discovered through pattern analysis is helpful if it is in an understandable form. Better visualizations and readable displays of mined knowledge require a lot of work.

As knowledge extraction is a complicated and long-run process, it requires a skilled labor force that will cost you extra in regard to both budget and time.

The most common disadvantage in the mining process is the use of different approaches based on extracted info. Some algorithms may require only clear figures, which may lead to complexity in the analysis and have a negative impact on results.

The execution of knowledge extraction completely depends on the algorithm. If these algorithms are not efficient or scalable enough, mining a large-scale amount of information would be impossible. The continuous improvement of mining algorithms is a must.

The collection and use of information require appreciable security. Illegal access to private details of individuals or any confidential data may become an issue.

Data extraction is a procedure of extracting materials from online sources; structuring and storing them in the centralized database. According to data science, where two ETL (extract-transform-load) and ELT (extract-load-transform) processes are widely used, data extraction is the starting point.

And what is the purpose of extracting data in business? It is an essential process that helps collect both structured and unstructured data as a means of staying competitive in the market. Brand monitoring, lead generation, price optimization, product intelligence, competitive monitoring, and much more can be enhanced with the help of extracted data.

So, let’s learn how to do data extraction, and what methods are available. There are 3 steps:

1. Defining the source. The first step is selecting the source (web page, social media platform, review site).
2. Collecting materials. The second step is web scraping by using the “get” query and parsing html pages.
3. Storing the content. The last step is saving the extracted data in local or cloud storage.

According to Gartner, about 80% of extracted content is unstructured, as it is taken from social media, emails, chats with customers, and so on. So before starting the process, we need to prepare the content by removing symbols, spaces, duplications, and other unnecessary stuff with the help of special cleaning techniques.

When the data is structured, the intake is comparatively easy and performed using the below-stated methods:

This method is used when you extract information for the first time and you have no records to track changes. It is advisable if there are large tables with millions of records. Full extraction loads a network because of large-scale material, and while it is usually the simplest and fastest method, it is not recommended. The only way to decide whether to do extraction full or by stages is to implement both for the same piece of content, then test execution and practicability according to your needs.

This method requires extraction in increments. There is no need to extract the whole material — only the changed or added part after a defined event that can be tracked by using timestamps or triggers. The event could be the end of the year, month, or day. Incremental extraction is ideal for a transactional system where it is not necessary to extract the full data every time; the extraction of changed details can be enough. This method may be complex, but it reduces system load. The only weakness is that it is not possible to find records that may have already been deleted from the source.

The easiest way to extract info is based on notifications from when the changes were recorded. Many databases offer such a mechanism using binary logs or change data capture, and there are also different honeypots with similar functionality.

Automated extraction is the most efficient approach. It is realized with the support of modern tools and allows the creation of logical steps to choose the extraction method for a specific operation.

Data extraction is more than scraping helpful content. By using proper data extraction techniques, you can transform your business activity by saving time and money. By obtaining valuable materials, you can improve almost everything in your business, from general success to competitor monitoring.

The competitive product needs extra features, and web crawling can be a valuable asset to your product development process. Based on informative and significant details regarding your customers’ needs and overall sentiments about your product, you will have a clear vision of what to enhance and optimize.

Lead generation is more than having a list of potential customers with their contact details. You can also collect leads from blog posts, status updates, and business connections. Here, knowledge extracting will help you create a complete lead generation system with a minimal marketing budget. Read more: Lead Generation Marketing: Workable Tactics and Steps to Create a Strategy

With brand monitoring, you can stay on top of what people are saying about your brand by parsing their comments from social networks or review websites. Such an approach will help you not only make your clients happy — but you will also know how to develop relevant marketing communications. Therefore, the right data extraction strategy will lead to the right marketing strategy.

One key factor of a successful business is researching your competitors. Learning about your competitors is much easier with web scraping; it will help you look deeper and find out not only what they are promoting or advertising/what people are saying about their brand, but parsing websites like Crunchbase and its analogs will also reveal information about their financial statistics.

Web scraping will help automate many areas of your business. Manual collection may provide you with imprecise details, while modern web extraction services identify inconsistencies and inaccuracies in materials and provide you with deep insights to help realize your business growth.

To get a complete picture of data extraction services, it is necessary to understand their major disadvantages.

To non-experts, the parsing procedures may be confusing, and the only way to deal with that is to hire professionals. Besides, in most cases, extracted materials should be cleared and formatted, which can be another headache for business owners.

Large-scale scraping may take a really long time, and because of the extra load, the web server may go down and challenge the interests of the target website.

If you do not have special tools to overcome anti-scraping techniques, you cannot get the materials from most websites. Another risk is getting involved in lawsuits over parsing bot activities if you are not familiar with the Terms of Service of the sources you are scraping.

And in the end, we suggest watching an insightful video about the difference between big data, data science, and data analytics:

Also known as “web scraping”, it is the process of extracting data from (usually unstructured or poorly structured) data sources into centralized locations and concentrating it in one place for storage or further processing. Specifically, unstructured data sources include web pages, email, documents, PDF files, scanned text, mainframe reports, reel files, announcements, and so on. Centralized storage can be local, cloud, or hybrid. Data extraction does not include processing or other analysis.

Data mining, also called Database Knowledge Discovery (KDD), is a technique often used to analyze large amounts of data using statistical and mathematical methods to find hidden patterns or trends and extract value from them.

Data extraction can be reduced to three steps: Select the data source you want to extract from, such as a website. Data collection and analysis were obtained in an HTML document using programming languages such as Python, PHP, R, Ruby, etc. Data keeping in a local database or cloud storage for future use.

Data collection and data mining bring a lot of benefits to business entities and society, but privacy issues and inaccurate information may lead to problems if you do not consult with professionals. At DataOx, we know all traps and pitfalls of both methods and are always ready to provide you with more useful insights and recommendations. Just schedule consultation with our expert for free and stay tuned!