What is data mining? How can it help business analytics?

Data Mining

Today, consumers have a wide variety of product needs. This has led to the establishment of various businesses to support those demands. It includes similar types of businesses, different entrepreneurs, the same area, different areas, within the same country, or even across different countries.

When businesses share similarities and pursue the same goal, competition inevitably arises. Businesses must compete to reach the largest customer base, generate profits, and remain sustainable. A fundamental element in devising business strategies is consumer demand data. However, gathering and analyzing large amounts of data from around the world to create a business strategy takes considerable time.

With the rapid advancements in technology, consumers can now quickly find what they need. Entrepreneurs who take too long to gather data and develop strategies may end up reaching their target customers more slowly than their competitors.

To accelerate business development, it is necessary to have a tool that can collect data and analyze business operations. It helps you create strategies and business models. We call this tool Data Mining.

What is data mining?

Data Mining is the process of sorting through large data sets into patterns. There is also the process of analyzing relationships between data. These processes can help you solve problems and improve business practices. You can also interchangeably use the term Knowledge Discovery in Databases (KDD) in practice.

Types of data in data mining

  1. Relational Database is a database stored in table format that shows relationships using the ER model.
  2. Data Warehouse is a database that comes from multiple sources, collected and stored in one place in a single format.
  3. Transactional Database is a database that stores data for each item in various event formats.
  4. Advanced Database is a database stored in various formats, such as text files, multimedia, and web.

The Data Mining Process

Data mining follows a standard process called the “Cross-Industry Standard Process for Data Mining” (CRISP-DM). The steps are as follows:

1. Business understanding

Before looking at any data, the mining process starts by understanding the purpose of your business, identifying the problems, and defining a plan. Then, it transforms the data into a format suitable for analysis and business trend planning.

2. Data understanding

You should collect data from each source and check its accuracy and reliability. This step also includes reviewing data details and assessing the appropriate amount of data for analysis.

3. Data preparation

This step is to convert raw data into data suitable for further analysis. Data preparation can be divided into three steps as follows:

  1. Data selection: Define objectives, analyze, and select data that can be used for analysis.
  2. Data cleaning: Check the completeness and accuracy of the collected data and remove duplicate entries, leaving only one set.
  3. Data transformation: Convert data into a format ready for analysis. For example, convert text to numbers or convert data into the desired intervals.

4. Modeling

Once the data is ready, it’s time to create a prediction model or simulate data for analysis. Then, assess the accuracy of the created model. If it does not meet the requirements, it is necessary to return to the data preparation step.

5. Evaluation

After creating the business model, you should evaluate the efficiency of the model to see if it meets the requirements for practical use. You might create comparative results in various graphs to better understand.

6. Deployment and Monitoring

Finally, you can apply the data from analysis and modeling in practice by using machine learning for market analysis. This is called market basket analysis. This technique involves analyzing data from POS (Point of Sale) and communicating insights through data visualization using tools such as Power BI and Google Data Studio.

Data mining techniques

Descriptive Modeling: It is a form of data analysis that uses basic statistics to understand existing data. It includes checking for and removing duplicate or unused data. This technique can be divided into three types:

  • Association

This involves finding relationships between data based on events that have occurred. For example, if a customer purchases a product from a home appliance store, we analyze the purchase data to understand what products they bought. If they purchase more than one item, we examine how these items are related. Then, we use this information to pair or group the products and create promotions to attract customers.

  • Clustering

This is the process of grouping similar or close items to analyze future data trends. For example, grouping data by age and gender, analyzing spending behavior, and creating tailored campaigns for each group.

  • Time series

This is the process of predicting future events. For example, if the population in an area is steadily increasing, there may be plans to build a market or store in the next three years to support product demand.

Predictive Modeling: This involves using historical and current data for statistical analysis to predict future events. This technique can be divided into three types:

1) Classification

  • Decision Tree: This is a decision-making model. The form is like a tree structure. It starts with a parent node and branches out into child nodes. Then, it analyzes the outcomes of decisions and continues the process until a final decision is made at the leaf node.
  • Naïve Bayes: This method converts existing data into numbers. This is to analyze the probability of events that have not yet occurred, predicting based on what has happened before.
  • Neural Network: A process in machine learning that connects data structures in layers, resembling the human brain. It models the relationship between input and output data. For example, searching for a brand logo within objects in an image.

2) Regression

This method is similar to classification, but it involves estimating numerical values or quantities by looking at trends in past data. Define independent and dependent variables and analyze the results in graph. For example, there were 10 customers last year with sales of 100,000 baht. If there are 20 customers this year, sales could reach 200,000 baht or more.

 All the techniques mentioned above rely on computational and computer system skills. These skills can be summarized as follows:

  • Statistics: It is used to analyze data, link data relationships, and use statistical software to calculate results.
  • AI: It is used to handle large amounts of data quickly, reducing processing time.
  • Programming Languages: You can use languages like Python to create specific commands for analysis.
  • Machine Learning: Understanding the logic of systems makes data management easier.
  • Database: Use SQL knowledge to extract data from multiple sources for analysis.
  • Data Visualization: This involves representing data in various formats, such as graphs, maps, and tables, for in-depth analysis to make it easier to understand.

Examples of data mining in different types of businesses

Sales

Retail businesses use POS (point-of-sale) systems to manage store operations, including processing payments, issuing receipts, checking inventory, and summarizing income and expenses. This allows you to check financial information and analyze consumer behavior to improve marketing strategies.

Marketing

Each brand creates online campaigns to attract customers to purchase newly launched products. For example, IKEA created IKEA Place to encourage customers to make purchases by offering discount campaigns or bundled product sets at lower prices.

Factory and manufacturing

Factories use data mining to predict and plan machine maintenance schedules as they age and estimate the costs required to repair each type of machine. In manufacturing, data mining helps analyze production costs, and the time required to produce each product. It also helps predict target markets, enabling continuous manufacturing.

Hospital

Hospitals use data mining to analyze disease symptoms to determine appropriate prevention and treatment methods.

In conclusion, regardless of the type of business, you need to use the principles of data mining to analyze data. This enables your business to operate continuously in line with consumer demands. Additionally, it helps prevent potential future problems that may affect the business.

Source:

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