Data mining is not an invention of the digital age. The concept of data mining has been around for over a century. But it reached the peak of public attention in the 1930s. Alan Turing presented one of the first examples of data mining in 1936. He came up with the idea of a machine that could perform calculations similar to today’s computers.
We have come a long way since then. Businesses are now using data mining and machine learning to improve everything from an organization’s sales processes to interpreting financial resources for investment purposes. As a result, data science professionals have become vital to organizations worldwide, as organizations seek to achieve more significant goals with data science than ever before.
What is the difference between data and information?
Before entering the topic of data mining, it is necessary to understand the difference between data and information.
The terms data and information are often used interchangeably. However, there is a subtle difference between the two. In short, data can be a number, symbol, word, code, graph, etc. On the other hand, information is data that has been analyzed. Information is used by humans in some way (such as decision-making, prediction, etc.). A simple example of using information is a computer. Computers use programming scripts, formulas, or software programs to convert data into information.
What is data mining?
Data mining, also known as knowledge discovery in data, is the process of discovering patterns and other valuable information from big data sets. Due to the evolution of data storage technology and big data growth, data mining techniques have increased dramatically in the last two decades. Data mining aims to transform organizations’ raw data into valuable knowledge. Despite the technology constantly evolving to handle large-scale data, leaders still face challenges around scalability and automation.
Data mining has improved organizational decision-making through the analysis of data insights. The data mining techniques that underlie these analyses can be divided into two main objectives. They can describe the target data set and predict the results using machine learning algorithms. These methods provide information such as fraud detection, user behaviors, bottlenecks, and security issues.
Data Mining Process
The data mining process includes several steps, from data collection to visualization of valuable information from big data sets using process modeling. As mentioned, data mining techniques generate, describe and make predictions about a target data set. Data scientists describe data by observing patterns, connections, and correlations. They also classify and cluster data through various methods.
Data mining consists of four main stages, determining the goals, collecting and preparing data, using data mining algorithms, and evaluating the results.
Determining the goals of the organization:
This step can be the most challenging part of the data mining process. However, many organizations spend the least amount of time on this step. Data science professionals and business stakeholders must work together to identify business problems. This will help speed up the process and identify the parameters of a specific project. Analysts may also need additional research to understand the business context properly.
Data Preparation:
Once the problem domain is defined, data scientists identify which data sets are best suited to answer business questions. As soon as they collect the relevant data, the data is cleaned, and any errors such as duplicates, missing values, and outliers are removed. Depending on the dataset, an additional step may be added to reduce the number of dimensions because the variety of features can reduce the speed of subsequent calculations.
Modeling and pattern extraction:
Depending on the type of analysis, data scientists may examine data relationships such as sequential patterns, association rules, or correlations between data. While high-frequency patterns have broader applications, sometimes deviations in data can highlight areas of potential fraud.
Deep learning algorithms may also be applied to classify or cluster a dataset depending on the available data. A classification and clustering model may be used to categorize the input data if the input data is labeled. If the dataset is unlabeled, independent data points in the training set are compared to each other to discover underlying similarities. This data is clustered based on these features.
Evaluation of the results:
After data collection, the results should be evaluated and interpreted. The results should be valid, original, practical, and understandable. When these criteria are met, organizations can use this knowledge to implement new strategies and achieve their desired goals.
How does data mining work?
A typical data mining project begins by asking the right questions of the business, collecting the correct data to answer them, and preparing the data for analysis. Success in the following actions depends on what happened in the previous steps. To use data mining, organizations must ensure the quality of the data they use for analysis because poor data quality does not lead to desirable results.
Data mining professionals usually achieve desirable and reliable results by following a structured and repeatable process that includes six steps. In the following, we give a brief explanation of these steps:
- Knowledge and understanding of business: At this stage, a complete understanding of the project parameters, including the current state of the business, the main goal of the project, and the project success criteria are done.
- Knowledge and understanding of data: In this step, the data needed to solve the problem is determined and collected from the available sources.
- Data preparation: In this stage, data preparation in a suitable format to answer business questions and solve data quality problems such as missing or duplicate data is done.
- Modeling: In this step, using algorithms, the patterns in the data are identified and modeled.
- Evaluation: At this stage, it is determined how much the provided results help to achieve the organization’s goal. Often this step is repeated several times to find the best algorithm to achieve the best result.
- Implementation: At this stage, the project results are available to decision-makers.
During these steps, close collaboration between domain experts and data mining experts is essential to understand the relevance of data mining results to business questions.
Data mining techniques
Data mining converts a large volume of data into useful information using different algorithms and techniques. Here are some of the most common ones:
Association rules:
Association rule is a rule-based method for finding relationships between variables in a given data set. These methods are frequently used for market portfolio analysis. They also allow organizations to understand the relationships between different products better. Understanding consumer spending habits enables businesses to implement better cross-selling strategies and recommendation engines.
Neural Networks:
Neural networks, which primarily use deep learning algorithms, process training data by mimicking the interconnections of the human brain through neural nodes. Each node consists of inputs, weights, thresholds, and an output. If the output value exceeds a specified threshold, it activates the node and forwards the data to the next network layer. Neural networks tune this mapping function through supervised learning. This is adjusted based on the performance of the loss function. When the cost function is at or close to zero, we can be confident that the model is accurate enough to provide the correct answer.
Decision Tree:
This data mining method uses classification or regression methods to classify or predict potential outcomes based on a set of decisions. As the name suggests, a tree representation is used to show the possible consequences of these decisions.
K-Nearest Neighbor:
This non-parametric algorithmic technique classifies data points based on their proximity and relationship with other available data. This algorithm assumes that similar data points can be found near each other. As a result, it tries to calculate the distance between the data points through the Euclidean distance. It then assigns a category for the most frequent group or average.
Data Mining Advantages
Diverse data is pouring into businesses in high numbers at an unprecedented speed and volume. The success of your business depends on how quickly you can discover big data insights and use them in business decisions and processes and take better actions throughout your organization. However, with so much data to manage, this is impossible. Data mining gives businesses the power to predict what will happen next and improve the organization’s future by understanding the past and present.
You can use data mining to solve any business problem that involves data. Here are some of them:
- Increasing the income
- Understanding customer preferences
- Acquiring new customers
- Improving cross-selling and up-selling
- Maintaining and increasing customer loyalty
- Increasing return capital
- Detection and identification of fraud in the organization
- Identification of credit risks
- Monitoring the performance of processes
Through data mining techniques, decisions can be based on business intelligence and provide more consistent results. These results make the organization surpass its competitors.
Today, large-scale data processing technologies such as machine learning and artificial intelligence are readily available. So, organizations can now analyze several terabytes of data in little time. This helps them innovate and grow faster.
Data Mining Disadvantages
Although data mining has many advantages, it has disadvantages that cannot be ignored. Here are some of these disadvantages:
1. The need for an expert for data mining
In general, the available tools for data mining are very powerful. But they need a skilled expert to prepare the data and understand the results. Since data mining shows various patterns and relationships, the importance and validity of their patterns must be established by the user, and the presence of a skilled expert is one of its necessities.
2. Privacy Issues
Data mining collects information about people using specific IT techniques. This data mining process includes several factors, and by involving these factors, this system invades the privacy of its users. For this reason, it is lacking in the security of its users, and ultimately, it causes false communication between people.
3. security problems
Since huge data is being collected in data mining systems, some of these very critical data may be hacked by hackers, as has happened in many large companies.
4. Misuse of Data
In the data mining system, safety and security capabilities are very low. For this reason, some can misuse this information and harm others. Therefore, the data mining system should change its working process to reduce the misuse of data through process mining.
Data Mining Applications
Data mining techniques are widely accepted among business intelligence and business data analysis teams. These techniques help them to use the extracted knowledge for organizations and industries. Some of the uses of data mining include the following:
Sales & Marketing
Organizations collect large amounts of data about their customers and prospects. By viewing consumer demographics and online user behavior, organizations can use the data to optimize their marketing efforts, improve segmentation, cross-sell offers, and customer club programs, and achieve greater marketing efficiencies. Predictive analytics can help organizations determine their expectations with the help of stakeholders. Also, data mining can provide any increase or decrease in marketing capital.
Education
Educational institutions are beginning to collect data to understand student populations and the right environment for student success. As courses continue to be transferred to online systems, they can use different dimensions and criteria to observe and evaluate performance, such as student profiles, classes, universities, elapsed time, etc.
Operational optimization
Process mining uses data mining techniques to reduce costs in operational functions. They also make organizations more efficient. This method has helped business leaders identify costly bottlenecks and improve decision-making.
Banking
Automated algorithms help banks understand their customers and the millions of transactions in the financial system. Data mining helps financial services firms gain better visibility into market risks, detect fraud more quickly, and manage regulatory obligations.
After identifying the applications of data mining, we introduce some of the most important data mining software:
- Carrot2
- org
- ELKI
- GATE
- Angoss KnowledgeSTUDIO
- BIRT Analytics
- Clarabridge
- E-NI (e-mining, e-monitor
- IBM SPSS Modeler
- Microsoft Analysis Services
- Oracle Data Mining
This branch of data science comes from the similarities between searching for valuable information in a large database and mining a mountain for ore. In this article, we learned that data mining is analyzing a massive amount of data to discover business intelligence. Data mining helps organizations solve problems, reduce risks and take advantage of new opportunities.
Data mining can answer your business questions in no time. Users can use various statistical techniques to analyze data differently to identify patterns, trends, and relationships they may be missing. They can use these findings to predict what will happen in the future and take action for changes in their business.
