28 Data Analysis Methods! | A comprehensive explanation of data analysis methods that can be used in business

What is Data Analytics?

Let's start by understanding what data analytics is, its concept and importance.

Data analysis is the process of systematically organizing large amounts of information and interpreting numbers and patterns to reveal hidden meanings and relationships. Using methods such as statistics and machine learning, it is possible not only to understand the current situation but also to predict future changes. Data can be used in a wide range of situations, from measuring the effectiveness of sales promotion campaigns to demand forecasting.

Evidence-based decision-making is essential for companies to maintain their competitive edge. Using data as a basis for decision-making facilitates smooth communication throughout the organization and enables targeted measures to be implemented. Another major benefit of data analysis is that it makes it easier to respond flexibly and quickly to changes in the business environment.

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Definition and purpose of data analysis

Data analysis can be defined as a series of processes that involve collecting the necessary data for a specific purpose, organizing and visualizing it while ensuring its quality, and then utilizing the insights gained from it in decision-making. The aim is not simply to list numbers, but to clarify business issues and find solutions. There are a wide variety of analytical methods, but they must be used appropriately depending on the problem at hand and the issue to be addressed.

Why data-driven management is attracting attention

The reason data-driven management is gaining attention is that the business environment is changing at an ever-increasing pace, making it difficult to respond quickly and accurately to decisions that rely solely on experience and intuition. Data-based decision-making minimizes errors and enables the formulation of effective strategies. Another major advantage is that companies that utilize data are able to quickly grasp changes in customers and the market, giving them an advantage over their competitors.

The purpose and importance of data analysis

It is essential to understand the key objectives that businesses can achieve by adopting data analytics and why it is important.

By conducting data analysis with a clear purpose, the results are more likely to be directly useful to business activities. For example, analytical data can provide objective support when grasping the state of the competitive market and deciding the direction of new product development. If the purpose is incorrect, there are cases where the time and money spent will not be put to practical use in actual measures, so it is important to reaffirm the importance of the analysis as the starting point.

Data analytics can also be positioned as a long-term growth engine for companies. Making data-driven decisions about acquiring new customers and retaining existing ones allows you to adapt to changing customer needs. Embedding an analytics culture throughout your organization will help you establish a competitive advantage for the future.

Utilization to improve market competitiveness

In order to increase market competitiveness, it is important to first gain a detailed understanding of customer needs and purchasing behavior, and then implement product development and marketing measures in line with those needs. For example, creating customer segments using cluster analysis makes it easier to build a competitive advantage for specific target groups. Quick decision-making based on analysis results is important, as this can also lead to the discovery of new business opportunities.

Improved operational efficiency and cost reduction

When aiming to improve business efficiency, it is essential to quantify on-site processes and identify bottlenecks. For example, time-series analysis can be used to predict inventory trends, reducing the risk of excess inventory and shortages while also achieving cost reductions. By deriving improvement measures based on data, it becomes clear when and where to invest resources, allowing for the optimization of operational costs.

Improving customer satisfaction and optimizing marketing

By utilizing customer data and purchase history, you can measure and optimize the effectiveness of your marketing initiatives in detail. For example, by conducting RFM analysis, you can understand the percentage of repeat customers and the characteristics of loyal customers, making it possible to provide attractive offers at the right time. By implementing measures to increase satisfaction while segmenting customer touchpoints, you can also improve brand loyalty.

Basic steps you should know before proceeding with data analysis

In order to conduct a successful analysis, it is important to understand the entire process from setting objectives to implementing measures.

Data analysis will yield greater results if it is carried out continuously according to a clear process, rather than as a one-off effort. Setting goals and collecting data in the early stages must be done carefully, as this will affect the accuracy of subsequent analysis. Working steadily through each step will enable analysis results to be used for more accurate decision-making.

Each step presents its own challenges, but taking steps can mitigate the risks. For example, insufficient data pre-processing can lead to erroneous conclusions, but sufficient cleaning can prevent problems. Understanding the overall process and identifying potential issues early on is key to success.

Step 1: Clarifying goals and challenges

In the first step, it is necessary to specify as specifically as possible the goals you want to achieve and the problems you need to solve. For example, whether you want to increase the number of new customers or increase the average customer spend will determine the data collection and method you choose later. If you are unclear about your goals here, the interpretation of the analysis results will become unclear and the direction of your measures will become unclear, so be sure to give it careful consideration.

Step 2 | Data collection and preprocessing

The data used for analysis is obtained from many sources, including internal systems and customer surveys. When integrating data, it is important to carefully examine differences in formats and recording methods and manage them in a consistent manner. Furthermore, if missing values and outliers are not handled appropriately, bias may occur in the analysis results, making it difficult to make accurate decisions.

Step 3 | Select and implement the analysis method

It is important to select an analysis method that is appropriate for the purpose and type of data. For example, if the data is mainly time series data, an ARIMA model is used, while regression analysis or cluster analysis is effective if there is a lot of customer attribute information. When performing analysis, it is necessary to properly verify the accuracy of the model to avoid risks such as overfitting.

Step 4 | Visualize and extract insights

Visualization is a powerful tool for communicating analytical results in an easy-to-understand way. Graphs and dashboards allow you to see trends and patterns at a glance that might be difficult to grasp with numbers alone. The insights gained from these visual elements can help facilitate decision-making among stakeholders and clarify problems and new opportunities.

Step 5: Verify results and implement measures

After conducting the analysis, it is important to try out measures based on the results and verify their effectiveness. Quantitatively evaluate whether the predictions were correct and whether the expected results were achieved, and revise the analysis policy and model as necessary. Repeating this cycle will improve the accuracy of the analysis and the level of data utilization throughout the organization.

A systematic introduction to 28 representative data analysis methods

There are various approaches to data analysis, and the key is to use the most appropriate method depending on the purpose and characteristics of the data.

Here we introduce a list of representative analysis methods. There are a wide variety of methods available, from statistical techniques to machine learning approaches, so it is important to carefully consider the purpose of the analysis and the nature of the data before making a selection. Understanding the characteristics of each method will help you build an analysis process that is more likely to lead to results.

Key points for choosing a data analysis method

Here are some points to keep in mind when considering which analytical method to choose from the many available.

When selecting an analysis method, it is important to fully understand the format and purpose of the data, and to consider the possibility of combining multiple methods. Sticking to a specific method may result in missing insights. Depending on the situation, a hybrid approach, such as combining simple statistical tests with machine learning, may be effective.

Check the data type and format

There are many different types of data, including quantitative data, qualitative data, time series data, and panel data. For example, if your data is primarily text, text mining is an option, while if you're dealing with time series data, the ARIMA model is an option. Accurately understanding the data format will help you determine the appropriate preprocessing and analysis methods.

Alignment with business challenges and objectives

Ensuring that your analytical methodology is aligned with your business problem is crucial to success. For example, if you want to predict customer churn rates, decision tree analysis or logistic regression, which are strong at classification problems, may be useful. If you choose a methodology without clarifying your goals, there's a high chance that the results won't lead to action and your analysis will end up being self-satisfied.

Balance with resources and skills within the organization

Even if you introduce advanced analytical methods and tools, they can go to waste if you cannot secure the human resources and time within your organization to utilize them. If your team is small in programming skills, it makes sense to start by introducing Excel analysis and BI tools. Taking into account your current resource situation, it is best to gradually increase the level of analysis while proceeding with in-house development.

Tools that can be used for data analysis

To carry out analysis efficiently, it is essential to understand the characteristics of the tool and select and use it in a way that suits your purpose.

Choosing the wrong tool can disrupt the smooth flow of analysis and affect the quality of the final output. Because there are so many different tools available, it's important to compare them based on specific applications and business needs. It's also a good idea to consider the possibility of upgrading and expanding your tools as your company grows.

Excel

Excel is highly versatile and is often used for trial analysis of data from small-scale projects. Basic calculations and visualizations can be easily performed using functions and pivot tables. Compared to specialized statistical software, the introduction barrier is low, and many employees already have experience using it, which is another major advantage.

Programming analysis using Python and R

Python and R are the main programming languages used for data processing and building machine learning models. Their abundant libraries and community support make them appealing, as they make it easy to incorporate new analytical techniques. They also offer the flexibility to handle everything from large-scale data handling to advanced statistical analysis and visualization, all in one place.

BI tools (Tableau, Power BI, etc.)

BI tools are highly valued as platforms that allow for easy visualization and analysis of data after centrally managing it. Their advantage is that they can be easily shared with internal stakeholders by creating dashboards using drag-and-drop operations. By integrating multiple data sources and updating graphs in real time, they can also speed up management decision-making.

Integration with MA tools

Linking with marketing automation (MA) tools is particularly effective for companies that place importance on accumulating customer behavior data and automatically executing measures. Data collected by MA tools can be used directly for analysis, allowing for continuous measurement of the effectiveness of measures. Integrating data analysis and measure execution makes it easier to implement the PDCA cycle, which is expected to improve marketing efficiency.

Common issues and points to note

When analyzing data, it is important to not only keep in mind the key points to improve results, but also the precautions to take to avoid failure.

Common causes of analytical failure include basic issues such as insufficient data quality control and the selection of the wrong analytical method. Furthermore, if analytical results cannot be interpreted correctly or knowledge is not shared due to personalization, the results will not be fully utilized. By understanding the issues introduced here and taking measures to address them, you can significantly increase the success rate of your analytical projects.

Data quality and lack of preprocessing

Common issues include missing values, duplicate data, and extreme outliers that reduce the accuracy of analysis. If you proceed to analysis without sufficient preprocessing, the results may not be interpreted correctly and you may reach incorrect conclusions. It is essential to check for data anomalies during the validation stage and correct them in accordance with business logic.

Misinterpretation and bias of analytical results

Because analyses are based solely on models and statistical assumptions, the results cannot always be taken at face value. Strong bias can occur when sample sizes are small or data bias is not properly corrected. It is important to regularly re-examine the results and try to analyze them from different perspectives to minimize misunderstandings.

Dependence on external resources and personalization

Relying on external partners or specific personnel for specialized analysis increases the risk of analytical know-how not accumulating within the organization and becoming dependent on individuals. It is also often difficult to transfer analytical platforms and methods when personnel are transferred or leave the company. It is important to create a system to increase data literacy throughout the organization and thoroughly visualize and document the analytical process.

Creating the data infrastructure necessary for analysis

We've delved into analytical methods so far, but to achieve highly accurate analysis, it's essential to have a solid data infrastructure. For this, we recommend using iPaaS (Integration Platform as a Service).
As the name suggests, iPaaS is a platform for integrating data. It collects various data that is scattered across different locations, cleansing and standardizing the data to maintain its quality, and preparing the data as a reliable analytical platform.

Saison Technology's iPaaS, "HULFT Square," meets these data analysis needs. Please take a look at the product details.

On-premise and cloud. Streamline your data management.

Case studies and key success stories

Through case studies of companies that have actually implemented data analysis, we will examine the ingenuity used at the time of implementation and the factors that led to success.

By studying success stories, you can get a better idea of how to apply analytics to your own company. Data analytics is used in a wide range of applications across a variety of industries, from optimizing marketing strategies to improving production processes. What they all have in common is that setting clear goals and continuously implementing the PDCA cycle are often the keys to success.

Examples of success in the marketing field

One retail company conducted a comprehensive cluster analysis of customer purchase history and web access data to implement targeted campaigns and achieve a significant increase in sales. The key was to centrally manage huge amounts of data and quickly implement the analysis results into measures. There have also been reported cases where wasteful advertising spending was significantly reduced by redefining customer personas.

Improved efficiency of inventory management and demand forecasting

In inventory management, it is common to use time series analysis and regression analysis to forecast demand and determine optimal inventory levels. One manufacturer incorporated seasonal fluctuations and trend factors into their analytical model and succeeded in significantly reducing the risk of excess inventory and out-of-stocks. This has enabled them to prevent lost sales opportunities and reduce costs at the same time.

Improving yield in manufacturing

In the manufacturing industry, multivariate analysis and machine learning are often used to identify abnormalities in the manufacturing process and reduce the rate of defective products. By correctly linking multiple process data and identifying causal relationships, it becomes possible to visualize where there is room for improvement. This leads to improved yields and stabilized product quality, greatly contributing to increased efficiency across the entire business.

There is also a movement to incorporate generative AI into analysis

Recently, there has been a movement to apply generative AI to data analysis in order to encourage employees without specialized knowledge to use data. If analysis can be performed using natural language, many employees will be able to make data-based decisions and implement PDCA cycles.

Case study: Seven Bank Ltd.

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Summary | Determine the optimal method to accelerate your business

The key to success is choosing the right data analysis method for your business objectives and organizational situation from the many available methods. Identify the optimal method to accelerate business growth.

There are a variety of methods for data analysis, and the optimal method varies depending on the subject of analysis and the company's resources. First, it is important to have a process that clearly outlines the desired results, handles the data in the appropriate steps, and verifies the results from an unbiased perspective. Ultimately, the key to accelerating business is to incorporate the insights gained from analysis into measures and establish a system for continuous verification and improvement.

The person who wrote the article

Affiliation: Marketing Department

Yumi Ogawa

After two years of experience as a copywriter at an advertising agency, she has been working in the IT industry ever since. Her experience at a variety of companies, from B2C to B2B, and from Japanese ventures to major foreign companies, is her strength. She has consistently worked in a variety of marketing-related roles, including public relations, branding, product marketing, and campaign management, and has been in her current position since May 2024. In her private life, she loves interacting with nature, hot springs, and public baths.
(Affiliations are as of the time of publication)

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