It's been a while since businesses and organizations have been using data and insights to make calculated decisions. Companies that are not using this information will face competition from the ones who are using this technology. In this blog, we will see all about what is data mining, data mining tools, applications, and much more. Let's start the guide with the basic question of what is data mining.
What Is Data Mining?
Data mining is the process of going through large sets of data to find patterns and trends. Various data mining techniques help businesses to identify future trends at the right time and grab the opportunity for the organization. Data mining, along with other processes like data analysis & machine learning, all come under one umbrella term, which is data science.
Professionals use data mining features to get more information about customer behavior and improve the product and services of the business.
After knowing what is data mining, let's explore the features of data mining, to get more information about this concept.
Features of Data Mining
- Multiple features of data mining benefit businesses; let's see some of them:
- Data mining automatically predicts hidden patterns and trends in the huge data pool.
- Data mining helps the government sector to identify fraudulent activities like money laundering, criminal activities, etc.
- Data mining successfully provides decision-based information that helps managers and professionals make decisions faster.
After knowing the meaning and function of data mining, you might wonder what data mining applications are in real-time on a practical basis. Let's explore this in our next part:
Data Mining Applications
Businesses today are staying in the benefit they get from data. They make all the important decisions by using data mining techniques. Thus, irrespective of the type of business, the motive behind data mining is extracting the right insights for the business. Let's see what are the most common data mining applications.
Retail Industry
The retail industry is one with a huge dataset of customers. It has all the information like, customer's purchase history, buying behavior, consumption patterns, and much more. Also, due to more purchases due to online sites, this data is increased by multiple times. Online retail sellers can leverage this data by recommending products to leads by analyzing this dataset. Therefore, from the recommendation of products to understanding the buying pattern, the retail sector gets a huge benefit from data mining functionalities.
Financial Analysis
Another important sector that depends on huge sets of data is the finance sector. With more digitalization in the banking sector, the banking system is flooded with huge datasets. With the help of data mining, analysts can solve various data-related problems like identifying credit ratings, predicting loan repayment dates, and much more.
Telecommunication industry
If there's one single industry that will see continuous growth, then it's telecommunication. With this being the internet generation, people will find it hard to stay without an internet connection.
- Data mining can be used in mobile communication as well as in mobile computing.
- Customer Relationship management
- An important aspect of business life is CRM. Continuous service and improvement in product/service are some of the critical elements of CRM. Organizations can use data mining to collect customer data and analyze it for better customer relationship management.
- Other than these, there are various other data mining applications in the education industry, research industry, and various other multiple sectors. With this, let's move on to see our next topic, which is data mining functionalities.
Data Mining Functionalities
As you've seen that the concept of data mining is applicable across multiple sectors. But, other than identifying the patterns, there are several data mining functionalities, some of which are stated below:
Classification
As understood by the name, classification functionality includes the categorization of similar datasets based on certain features or categories. The classified data can be searched with a user-generated query.
Data Discrimination
Another data mining functionality is data discrimination which compares one class with another set of classes based on certain rules that are pre-defined and are known as discriminant rules.
Prediction
This is one of the most used data mining functionalities. The data collected by the organization can never be flawless. This module helps in finding the missing values or numbers in the data. This functionality also helps in predicting future outcomes of certain activities based on data. For example, how is the product going to perform among customers, etc?
Cluster Analysis
This data mining technique is performed on unclassified datasets. Here the dataset or the objects are grouped based on similarities. This is done in various fields like artificial intelligence, image processing, machine learning, etc. Apart from these, various other data mining functionalities are outlier analysis, evolution, deviation analysis, etc. Now, as most of you are interested in the career field of data mining, you must be aware of the advantages and disadvantages of data mining, which we'll cover in the guide.
Advantages And Disadvantages Of Data Mining
As you already know, data mining is identifying patterns from the data. This technique provides businesses with multiple advantages, some of which are:
Risk Identification
Data mining techniques help identify risks and fraudulent activities that are not easily found by old analysis methods. The process can easily work with unorganized data too.
More reliable information
Data mining helps get more advanced and reliable data information that professionals can use and analyze to make important decisions like procedures, planning campaigns, etc.
Analyze customer behavior
Data mining easily identifies hidden patterns, repeated behaviours of customers, and much more. This information helps businesses to identify which products have great potential in the coming times. This is done by analysing and identifying repetitive instances of purchasing a particular product regularly. But, as you already know, with advantages comes a particular set of disadvantages. Similarly, there are certain drawbacks to the concept of data mining which are, of course, solvable. So, let's see what some of these are:
Data mining tools
Professionals require a certain set of tools to perform the data mining process. These tools have multiple features and therefore require some amount of training to get used to the user's face. Therefore, to become a data mining expert, one must first get familiar with data mining tools and techniques.
Privacy concerns
Consumers whose data is being used for analysis are worried about their data's privacy. Businesses collect this data from multiple sources like social media, websites, etc. Consumers worry about this data being leveraged for unethical reasons.
Huge Datasets
As you might already know, the process of data mining is not much beneficial with a small amount of data. For example, with a dataset of 100 people, data mining and data analysis is impossible. But instead, if there's a dataset of ten thousand customers, professionals can derive some useful insights. Collecting massive datasets is often a hindrance for organizations. All this information on what is data mining and how it works is explained. But do you know data mining professionals use many tools to perform their responsibilities? And as a data mining field aspirant, you must know these data mining tools.
Data Mining Tools
Data mining professionals have a bunch of responsibilities and tasks to perform. And for all these tasks, many tools are available for professionals. Each tool comes with different features, and thus, professionals can select any tool based on their requirements. In the guide, we'll be seeing the top five data mining tools.
Orange
The first in the list is orange, which is an open-source tool for professionals. The tool helps with various activities like visualization, presentation, and more. The tool is based on Python programming language, and it is preferred by beginners as it is fun to use as well as very easy to learn. Also, the tool has advanced features for experts to perform additional functions.
RapidMiner
RapidMiner is also an open-source tool that helps in machine learning, data visualization, and deep learning. The tool is completely suitable for beginners and non-tech people as its interface supports drag and drop option for users.
The tool RapidMiner comes with multiple options like in-office premice service or cloud solution. Users can utilize the tool according to their requirements.
KNIME
KNIME is an open-source, free tool for data mining and machine learning. You can create from beginning to-end Data Science workflows with its user-friendly UI that covers everything from modeling to production. Without writing a single line of code, fast modeling is possible thanks to a selection of pre-built components.
Teradata
Customers may use their favorite languages and tools at scale across several data types, using Teradata's Analytical platform's remarkable capabilities and engines. This is achieved by integrating analytics close to the data, eliminating the requirement for data transportation, and enabling users to do their analyses more quickly and accurately on bigger datasets.
SAS Enterprise Minor
Businesses of all sizes use the scalable platform known as SAS Enterprise Miner. Its data mining tools allow you to do crucial data preparation and exploratory analyses while simultaneously generating detailed reports or highlights of your findings. On the downside, it has a useful but somewhat old graphical user interface, which may look a little subpar for an enterprise product. Additionally, because it might slow down considerably, it isn't always the best choice for more difficult machine-learning tasks. As you're now aware of data mining tools used by professionals, let's see what are the bunch of data mining Architecture used that benefits businesses.
Data Mining Architecture
Data mining architecture consists of multiple components like data source, data mining engine, graphical user interface, and many more. Let's look at the components of data mining architecture in detail:
Data source
Database, data system, and data warehouse are part of the data source. Data can be extracted from these sources in various formats like spreadsheets, plain text files, etc.
Database server
Database server consists of all the relevant data that can be, and that is ready to be analyzed at a later stage. This data is extracted based on the data mining request of the client.
Data mining engine
One of the most relevant components of a data mining architect is the data mining engine. All the important data mining techniques are performed at this stage, some of which are categorization, organization of data.
Pattern Evaluation Modules
As the name suggests, at this stage, the data present at the database server will be evaluated and will be searched for any hidden patterns and trends, if any, in the dataset.
Graphic User Interface
This component of data mining architecture is for the users. Since users can't understand the complex nature of the process of data mining, this module makes communication between the user and the interface easier. This was all about data mining architecture. Now that we've mentioned almost all the important elements of data mining let's proceed to conclude the guide.
Conclusion
We live in a data-driven, information-rich world. Although knowing there is a wealth of easily accessible knowledge is comforting, the sheer amount presents difficulties. The longer it takes to locate the relevant insights you need, the more information available. That is why we talked about data mining today. One of the components of the broader picture that can be obtained by using Big Data is data mining. It represents one of the key methods used in running a modern firm. It offers the resources necessary for productive work to be done. As a result, its methods are always changing and becoming more effective at uncovering ideas. The direction that technology is taking is exciting.
