What Is a Data Analysis Tool?
A Data Analysis Tool is software designed to collect, organize, analyze, and visualize large sets of data.
Data analysis tools find applications across various fields such as business and academic research. These tools make it easy to understand data trends by providing visual representations. Leveraging the results of data analysis, they are employed for making business decisions and judgments, including policy formulation, issue identification, and the discovery and determination of improvement methods.
Additionally, one category of data analysis tools includes Business Intelligence tools (BI tools). BI tools specialize in collecting, analyzing, and visualizing data tailored to business processes, with the goal of facilitating decision-making and judgment in business.
Uses of Data Analysis Tool
Data analysis tools can analyze large and diverse datasets, making them applicable in various industries. In general business operations, analyzing extensive data allows for obtaining necessary information to aid in management. Specific use cases include:
- Business and financial analysis
- Sales analysis
- Human resources data analysis
- Budget management systems
- Data aggregation
- Automated report generation
Beyond business activities, data analysis tools are also utilized in initiatives like regional revitalization for policy decisions related to industrial and tourism promotion. Other specific use cases in various industries include:
1. Manufacturing Industry
In manufacturing, data analysis tools can analyze various data within a factory to enhance operational efficiency and productivity. Specific examples include:
- Real-time collection and analysis of data from sensors and cameras in the factory for understanding the operational status of production lines
- Predicting faults or issues from inspection data and utilizing it for equipment maintenance
- Analyzing inspection data for products to identify processes with high error rates
2. Retail Industry
In the retail industry, data accumulation includes sales records and various attributes. Analyzing this extensive data enables effective marketing strategies. Examples of data used in retail include:
- Sales data for products
- Customer attributes such as age and gender (obtained from membership information)
- Store location and catchment area
- Weather and temperature
- Data within an e-commerce site: customer behavior, banner click rates, abandoned carts, payment methods
- Social media mentions and posts related to the company
3. Agriculture
In agriculture, the increasing use of IoT allows for data collection and effective utilization of data analysis tools. Specific applications include collecting climate and soil data through sensors for planning cultivation and determining optimal inspection timings based on the operational status of agricultural machinery.
4. Other Industries
Data analysis tools are effectively used in various fields. Examples include:
- Healthcare and welfare: Analyzing medical information about patients, including imaging data (CT scans, X-rays) and prescription data
- Education: Analyzing data such as learning histories and exam results of children and students
- Food service industry: Analyzing customer attributes, visit dates, location, weather, sales for each menu item, and food ingredient waste
- Finance industry: Analyzing customer attribute data and transaction histories
Principles of Data Analysis Tool
1. Overview of Analysis
Data analysis tools collect and organize necessary data and extract information aligned with the objectives. The process of analysis involves:
- Data Processing
Transforming extensive data into a manageable form. - Data Extraction
Selecting only the necessary parts from processed data. - Reporting
Visualizing complex data through graphs, charts, and tables to create quick and accurate reports.
These processes make extensive data manageable and enhance the accuracy and understandability of reports, allowing for precise decision-making and judgment.
2. Analysis Techniques
Data analysis tools employ various analysis techniques based on different situations. Some of the primary analysis techniques include:
- Cross-tabulation
Analyzing data by dividing it into categories such as age groups or responses to survey questions. This method is useful for understanding trends based on attributes. - Cluster analysis
Grouping similar data to analyze high similarities within the data. This is effective, especially for understanding customer preferences. - Basket analysis
Analyzing items that customers put together in their shopping baskets to reveal correlations between products or services. It is a type of association analysis. - Regression analysis
Examining the relationship between a numeric result and numeric factors to reveal the relationship. - Decision tree analysis
Iteratively predicting multiple results through repeated cross-tabulation of explanatory variables against the target variable. - Principal component analysis
Summarizing multiple data into a few data points (principal components). It simplifies data for better understanding.
Apart from these, other techniques include ABC analysis, which ranks items like products or sales, gray models for predicting ambiguous data from clear and unclear data, and factor analysis to explore common factors and causes in multiple variable data. These techniques are often used in combination.
How to Choose a Data Analysis Tool
The main focal points when choosing a data analysis tool are categorized into four aspects:
- Purpose of Implementation
- Content of Features
- Usability
- Cost and Support
1. Purpose of Implementation
To effectively utilize a data analysis tool, the purpose of implementation must be clear. The tool is a means to achieve business objectives and clarity on why data analysis is needed and what the business objectives are to prevent the tool implementation itself from becoming the primary goal.
During the tool selection stage, clearly understanding why data analysis is necessary and the business objectives it aims to achieve helps in defining the required features of the tool. This makes the tool selection process smoother and contributes to achieving the objectives smoothly after implementation.
2. Content of Features
When evaluating a tool’s features, it is essential to consider whether they align with the company’s industry, and business operations and whether they include the necessary features to achieve the company’s objectives. Additionally, checking compatibility with existing data and tools within the organization is crucial. Different data formats such as proprietary databases, PDFs, and Excel files (.xls, .xlsx) exist within companies, and compatibility with these formats is necessary to collect and analyze existing data.
3. Usability
Ease of use for end-users at the operational level is crucial when introducing a data analysis tool. While some tools are intuitive and easy to use, others may require programming skills or specialized knowledge. Selecting a product that aligns with the skills of the personnel is essential.
Trial versions of tools are available for testing, and trying out a trial is recommended.
4. Cost and Support
Data analysis tools come in both free and paid versions, each with different cost structures. It is necessary to carefully consider the return on investment and select a product within the budget that can be sustained continuously.
Support from the developer varies, and checking the level of support is essential. Some tools, especially those developed abroad, may not provide support in Japanese. Additionally, free tools may lack support channels for addressing questions or issues.