How do you suppose big corporations manage the sales and production of their products? How do they figure out which product requires more attention and what is more popular? This is where Data Mining steps in. Data mining is a process that has proved to be useful not only in the case of product-sale scenarios but also in testing different theories and identifying the best practices to improve the current conditions. If you use this method strategically, you will be able to answer all the questions about your business goals.
Data mining has become such an integral part of every company, allowing it to step into the cutthroat competition in the market. This article will cover the process and techniques followed by the advantages and disadvantages of data mining to help you understand better the importance of data mining today.
What is Data Mining?
Data mining, with the help of different tools, inputs a large amount of raw data, processes it and gives out information about the different patterns and trends in the data. The identification of these patterns allows the company owners to gain insights into their company and develop effectual strategies that would, in return, increase their sales and minimize their costs. Not only this, but data mining also reduces the risk of being swindled using fraud detection and filtering out spam emails, etc. This entire process takes the help of Machine Learning, statistics, and complex Databases.
The Process Behind Data Mining
There are five main steps in order to mine your data. The collection of data, storage of data, the organization of data, analysis of data, and finally the presentation of data. We will now be discussing those steps in detail.
For your understanding, the process behind data mining has been broken down into some simple steps. First, data is collected from various sources and stored in the cloud. Once it has been stored, it is analyzed by professional analysts, and then finally, the results are shared with the user.
1. Collection of Data:
The first and foremost step in data mining is the collection of data. Data is collected from all relevant sources. An exceedingly popular method for data mining is Cross-Industry Standard Process for Data Mining (CRISP-DM). This six-step model understands, interprets, and evaluates data to give it meaning. It is a good guideline that can be followed when starting data mining.
2. Storage of Data:
After the data is collected, it’s stored either in the data warehouses available in the company, in-house servers, or clouds. It’s essential to choose a stable server because all the information processed and the results will be stored on these servers.
Companies usually hire a team of business analysts and information technology (IT) professionals who are given access to all the collected data. They are supposed to organize the data in a logical order. Before passing it onto the data mining software, the data itself must be fully organized and properly labeled to maximize the accuracy of the outcome; therefore, the people hired must be familiar with what they are doing to avoid any inconveniences in the future.
Another task that these people carry out is defining the purpose of their research. They need to be clear about their purpose in order to set suitable parameters to judge the data. This team of professionals may carry out their own research in order to understand the purpose. This may often be considered the most challenging step of the entire process because the results will be based on the initial goal.
4. Analyzing The Data:
Given the image above are the different tools used to analyze your data. This shows how much your data is processed at the back end and all the tools used to perform data the data mining process.
After organizing the data according to your needs, you must clear out all the irrelevant data. This may include all the repeated data, eliminating anything that was left incomplete. Reducing the amount of data passed on to the software will speed up the whole process and increase the accuracy of the results.
The type of analysis depends on the type of data. It’s determined during the pattern mining process.
- Sequential, correlational, and associative patterns can be seen in related data, i.e., different variables in the dataset depend on one another.
- Frequency patterns are formed due to a high occurrence rate of something, i.e., detection of spam emails that will save you from frauds.
- Deep Learning tools are used to examine several types of data, i.e., dividing the data into supervised and unsupervised data accordingly.
5. Presentation Of the Data to The User:
Finally, when you have your final results, you need to recheck if the result answers the question you defined in the purpose of your research. The result should be simple and easy to understand for the user, and the way it is presented should not be overly complex, i.e., easy-to-share formats, because the user will need this data to plan out the next steps for his company. An accurate result must highlight all the problems in order for the user to identify and take the next step carefully.
Pros and Cons of Data Mining
By now, you are probably familiar with what data mining is and how you can mine data to extract the information that you require. We will now be moving on to the pros and cons of data mining. If data mining can be used to analyze your data and provide you with some valuable insights, then it can also manipulate your data and invade your privacy.
Let us discuss some of the advantages and disadvantages of data mining in detail:
- No matter how much information you have, the data mining software will take in all of it and process it in a noticeably short interval of time. The processing of such enormous amounts of data was once considered impossible but is now done on a daily basis by almost every company.
- The main reason data mining is being used is to find hidden patterns and trends from your data. These insights are beneficial when it comes to making crucial business decisions. It checks the frequency of how often something occurs and informs you about it so you can plan out different strategies accordingly.
- In a way, data mining can be used in the opposite direction as well. We can use the insights that we have gathered to produce more data for different marketing campaigns.
- It separates risky material from authentic material. For instance, do you see that spam folder in your emails? Your email server separates emails that have been sent from ambiguous IDs and repetitive emails from the emails that are actually important to you to protect you from being defrauded.
- It not only gives you accurate and reliable information but also helps you make decisions that are important for your business’s future. Even the government and almost every organization take help from data mining software to extract information because it is that reliable. They then use the extracted information in the decision-making process.
- Privacy infringement. There have been many cases on many different websites and applications because they collected and misused their users’ privileged information. Some developers use the collected data and sell it on the black market online, and if not, they use it to blackmail people or commit identity theft. This usually ends catastrophically.
- Security issues and mismanagement of your data. Where there is a large amount of data, there is a problem in the storage process. When some people are given full access to your confidential information, such as your medical records, they can use it for their benefit. Any sort of mismanagement can also occur where they might lose it.
- Some decisions may be ethically wrong. This means that not every suggestion that results from the data mining process can be used for marketing purposes. For example, using someone’s medical condition to market your product can be considered ethically and morally wrong by many people.
- When you do not pass complete and accurate data to the software, the outcome may also provide you with some inaccurate solutions. As a result, you only waste your money by doing it all over again.
- Nevertheless, we have not yet refined the algorithms for data mining enough to rely on its outputs entirely. At the end of the day, it is only a machine that is making decisions, so obviously, it would not consider the feeling of the people who the decision will impact that it makes; therefore, the data miners must be the ones to keep that in mind.
Some Popular Data Mining Techniques
There are so many different approaches to data mining. There is a vast collection of algorithms that can be used to analyze large amounts of data to give you your required output through various techniques.
In this article, we will be discussing some of the most common techniques adopted by professionals.
· Neural Networks:
This method works similarly to a human brain by taking help from multiple deep learning algorithms. The simplest way to explain how they work is by dividing them into three components:
So basically, when data is entered into the software, it is passed to multiple “layers.” Each layer has its input, threshold, and output. The threshold of each layer is set through the means of supervised learning. That means each layer has its evaluation criteria, and once the data exceeds the limits of the threshold, it passes to the next layer. At every layer, it is evaluated through the gradient descent approach, and the most optimal one is chosen as the definitive answer.
This happens to be among the most critical steps in data mining. It’s where you collect the most insights from the data. Instead of displaying the numerical data, some visualizations use different metrics to display the output. The use of colors in this method makes it easy to identify and differentiate between various patterns and variables. Visualizations are popular for being dynamic, and they can easily display real-time data. Most companies set different parameters for different dashboards, which makes it easier for them to see different trends and their mining results because it efficiently highlights all the significant changes.
· KNN (K – Nearest Neighbors):
This data mining method assumes that all related data is entered closely, so it forms a cluster of all associated points based on how closely associated they are. It classifies different points into distinct categories by calculating the distances between them through the Euclidean distance or by averaging them. This is why this method is considered to be a non-parametric method.
Why is Data Mining Bad?
It is a known fact that everything has two sides – good and bad – and the case is no different for data mining. This tool on its own is not actually wrong but how it is used is what makes it. In the wrong hands, this tool can obviously do a lot of damage since a lot of information is passed on as an input to this, and he who is in control has leverage over all this information. Even if someone does not misuse the stored information, there is still some potential of something going wrong where a large amount of data is being stored. Another reason why data mining could be considered harmful is the reason for which it is being done. The purpose behind every action defines whether it is going to be good or bad.
Although data mining has proved to be a handy tool, and we still use it every day, however, in the end, we need to keep in mind that we must not rely on machines ultimately, as they too can be unreliable sometimes. We need to keep an eye out to ensure that these machines are not being misused or if we are not the ones misusing them.