What is data analysis? A simple explanation for beginners, from the basics to how to use it

The definition and importance of data analytics

Data-driven decision-making has become an essential process in every field.

Data analysis refers to a series of techniques for organizing collected information and deriving useful insights and patterns from it. In business, it is often used to optimize product strategies and marketing measures based on sales and customer data. Nowadays, it is possible to utilize the vast amounts of data generated online, and data-driven approaches are being taken to even more complex problems.

For companies, the appeal of utilizing data is that it allows them to develop highly accurate measures while minimizing risk. In fields such as academia and medicine, data analysis is also essential for objectively evaluating research data and leading to new discoveries. For these reasons, data analysis plays an extremely important role in improving business and research results.

Data analysis objectives

The purpose of data analysis is to objectively understand problems and derive rational solutions. By utilizing data accumulated within the company, it becomes possible to take measures aimed at clear targets such as increasing sales or reducing costs. Furthermore, it is possible to visualize the progress of processes and speed up decision-making throughout the organization.

Why data analysis is gaining attention

In today's rapidly advancing information society, all kinds of data are now being stored digitally. With the spread of big data and cloud computing, the importance of technologies and methods for efficiently processing large amounts of information and deriving useful insights is increasing. Against this backdrop, companies and research institutions are turning their attention to data analysis as a way to increase their competitiveness, and it is being used in a wide range of areas.

Main types of data analysis

There are various analytical approaches. Selecting the appropriate method depending on the purpose and quality of the data is the key to successful analysis. Below are some typical analysis examples.

Descriptive Analytics

• Summarize past data and understand the current situation
• Example: Sales trend analysis, customer segmentation

Diagnostic Analytics

• Analyzing the "why" of data
• Example: Analyzing the causes of declining sales, identifying factors behind employee turnover

Predictive Analytics

• Predicting the future based on past data
• Example: sales forecast, demand forecast

Prescriptive Analytics

• Suggest the best course of action
• Examples: inventory management optimization, personalized advertising

Benefits of Data Analysis

By using data objectively, you can enjoy a variety of benefits.

Introducing data analysis into corporate activities will first reduce wasteful measures and make the decision-making process much smoother. This is because by quantitatively understanding market trends and customer behavior, it becomes possible to come up with evidence-based strategies. This increases the probability of success of measures, leading to overall efficiency and productivity improvements for the organization.

Sharing visual information such as numbers and graphs is also extremely useful for building consensus within the company. Whether it's management decisions or considering new business ventures, presenting quantitative data allows for faster decision-making. Furthermore, proposals backed up by data are more persuasive, making it easier to reach a consensus within the team.

Faster decision-making

Because discussions can be based on quantitative evidence, it becomes easier to share your views with internal and external stakeholders. Data provides persuasive evidence, making it easier to reconcile differing opinions. As a result, you can significantly improve the speed at which you can start implementing measures, increasing the chances of taking action before your competitors.

Actions can be taken based on objective evidence

A major strength is the ability to make decisions based on concrete figures and analytical results, rather than just intuition or empirical judgment. Business is always accompanied by uncertainty, but having objective data reduces risk. This allows the entire organization or project team to take actions that are convincing.

Discover new business opportunities

By digging deeper into data, you may be able to uncover unexpected best-selling products or hidden customer needs. In particular, by combining multiple data sets and examining correlations, you can uncover previously unnoticed business connections. These new insights can be a differentiating factor in a competitive market and provide clues for business expansion and new approaches.

How to analyze data (basic flow)

Basic steps of data analysis

The following steps can be taken to analyze the data:

1. Setting goals: Clarifying what you want to analyze and what problems you want to solve
2. Data collection: Collect the necessary data (sales data, survey results, sensor data, etc.)
3. Data organization and processing: Eliminate unnecessary data and organize it into a usable format
4. Data visualization: Spot trends with charts and dashboards
5. Data analysis: Extracting insights using statistical methods and machine learning​
6. Decision-making and execution: Implement measures based on the analysis results

Data Type

The data can be broadly divided into two types:

• Quantitative data (numerical data): sales amount, number of hits, age, etc.
• Qualitative data (non-numerical data): free-form responses to surveys, reviews, social media posts

It is important to select appropriate data depending on the purpose of the analysis.

Points that beginners often find difficult and solutions

I don't know what data to use

Solution:

• First, organize the data you have available (internal sales data, Google Analytics, etc.)
• Utilize external data such as publicly available competitor data and government statistics

Data preprocessing is tedious

Solution:

• Utilizing Excel and Google Spreadsheet functions

I don't know which analytical method to use

Solution:

• Learn basic techniques for different purposes (e.g., sales trend analysis → time series analysis)
• Before learning about AI and machine learning, first learn basic statistical methods (mean, median, correlation coefficient, etc.)

Analysis tools for beginners

There are various tools for data analysis, but we recommend starting with Excel, which many people use in their daily business.

In fact, Excel has features such as the "Data Analysis ToolPak," "Standard Functions," and "Scenario Analysis Function," which allow for a variety of analyses.

First, let's see what you can do with Excel.

Disadvantages and challenges of data analysis

While data analysis tends to focus only on its advantages, its operational and cost disadvantages cannot be overlooked.

When introducing cutting-edge analytical methods and advanced security measures, the costs of tools and human resources tend to be high. If knowledge is lost due to staff turnover, there is a risk that the quality of the analysis will decline. Identifying these issues in advance and establishing an operational system is the shortcut to success.

Furthermore, the results obtained during the research and development stage may not necessarily match management policies or on-site conditions. Even if data is analyzed carefully, the results will not be utilized if communication within the organization is insufficient. It is necessary to devise ways to accurately share the results of the analysis and reflect them in management and on-site decision-making.

Increased workload and costs

Data analysis requires specialized knowledge, so you need to consider whether to develop personnel or outsource the work. Additionally, introducing sophisticated tools will likely increase licensing and maintenance costs. To maximize the effectiveness of your analysis, it's important to consider how to ensure results that are commensurate with your investment.

Analysis is dependent on individuals and knowledge is not consolidated

If only certain individuals possess analytical skills, the know-how is likely to be lost when they leave the company or are transferred. Data analysis only produces results when it is carried out continuously as an organizational activity. The key to preventing this is to create a system for distributing knowledge and sharing it company-wide.

Gap between analysis results and management decisions

If the analytical results derived diverge from management decisions and the measures to be implemented, the effectiveness of data analysis will be halved. If analysis is carried out without reflecting on-site opinions and management policies, there is a high possibility that valuable insights will not be utilized. It is important to closely collaborate between analysts and decision makers and share mutual intentions.

Key points for successful data analysis

To achieve high effectiveness, it is also necessary to pay attention to operational methods and organizational structure.

The key to successful data analysis is to clarify the purpose of the analysis and establish a process for sharing the results within the organization. Rather than simply reporting the results of the analysis, being able to explain in simple terms the background, methodology, and implications gained will deepen understanding. Furthermore, regular verification and correction will allow for continuous improvement of results.

Introducing the latest BI tools and machine learning techniques can significantly improve the speed of data-driven management. However, neglecting security and privacy considerations during operation can also pose significant risks. It is important to thoroughly create a system that is conscious of data governance throughout the organization.

Sharing and communicating analysis results

It is important to regularly share the results of your analysis and the visualization of your graphs with each department to foster a common understanding. Holding regular meetings and briefing sessions, rather than just email, can help deepen understanding. Making information public also allows you to receive feedback from other departments, leading to more accurate analysis.

Implementing BI tools to improve visualization and decision-making efficiency

BI tools allow you to turn complex data into a dashboard in real time, allowing you to see a variety of indicators at a glance. Top management can quickly grasp the situation and issue instructions as needed. By carefully considering the operability and display format, even employees without specialized knowledge can use it, making it easier to promote the use of information throughout the company.

Security and privacy considerations

When analyzing data, it is essential to take measures to handle personal information and prevent the leakage of confidential information. By strictly adhering to legal rules and corporate policies, you can ensure the trust of your analysis project both inside and outside the company. To minimize the risk of data leakage, be sure to implement thorough security measures such as access permissions and encryption.

summary

While data analysis is a means to increase competitiveness, its implementation and operation require careful planning.

First, it is important to clearly define the purpose and scope of the analysis and establish a system for sharing the results throughout the organization. If the purpose and method are clear, it will be easier to resolve issues related to investment costs and human resources, and you will be able to maximize the results of the analysis. Ultimately, the shortcut to success is to incorporate data analysis into your daily business cycle and strive for continuous improvement.

As data volumes and analytical methods continue to diversify, data analysis will be essential to maintaining competitiveness in business and research. By properly understanding the fundamentals and acquiring practical operational know-how, you can greatly expand the potential of organizations and individuals. Why not take this opportunity to consider introducing and utilizing data analysis?

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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