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As the promotion of DX accelerates, the use of data is becoming increasingly important in various decision-making aspects of business. What are the benefits of data analysis when utilizing data? What are the key points to keep in mind for more efficient data analysis? Knowing this will make a big difference in the quality of your data.
Therefore, this time we will explain the overview, benefits, and points to keep in mind regarding data analysis. We will also introduce success stories at the end, so you can easily imagine how they can be used in practice. Please watch till the end.
In my journey through the dynamic field of system development, app development, and digital transformation, I've realized the pivotal role of data analysis. Data analysis, as I've come to understand, is not just a process but an art. It involves the meticulous collection of large amounts of data through various data analysis tools, followed by organizing, processing, and selecting key pieces for in-depth analysis.
In my experience, data analysis is a cornerstone in a multitude of sectors, including business, science, government, medicine, and engineering. However, its role in business is particularly profound, influencing areas like management, finance, sales, and human resources. Through exploratory data analysis, businesses can delve into management and sales analysis, inventory control, human resource analysis, financial scrutiny, competitive analysis, and even future predictions.
The objective of data analysis, as I've learned, is to extract useful insights from collected data and juxtapose it with historical data. This comparison is crucial to comprehend the present and forecast the future, thereby bolstering business decision-making. In my practice, proper data analysis, aided by robust data analysis software, has enabled me to make reliable predictions, grasp market dynamics, understand customer needs, pinpoint challenges, and make informed decisions.
Furthermore, the digital era has revolutionized the availability of products and services. While this has made it easier to meet people's needs, it has simultaneously obscured the visibility of these needs. Here, data analysis emerges as a critical tool to unearth these latent demands. In the current business landscape, mastering data analysis is indispensable for steering a highly viable business.
An often-mentioned term alongside data analysis is "data utilization". In my exploration, I've discerned that while data analysis is about drawing conclusions from data, data utilization involves using these insights to enhance business efficiency and productivity. It's about leveraging data continuously for operational improvements and strategizing marketing efforts to boost profits.
As explained above, one of the major roles of data analysis is to support business decision-making. We will also introduce other benefits that come from analyzing and extracting the collected data.
・Decision making support
Without data analysis, decisions are made with low certainty and uncertain reproducibility. By conducting data analysis, it is possible to obtain more objective analysis results, so based on those results, rational decisions can be made regarding product/service development, business strategy formulation, marketing strategy, funding, etc. can. It is also possible to compare with past analysis, and data analysis can be based on various data.
・New discoveries of issues and needs
By conducting data analysis, we can analyze customer behavior and preference patterns, and potentially discover new needs and business opportunities that were previously unknown. Data can be used to predict sales, understand trends, and manage inventory, and by continually improving products and services based on this data, it can be used to improve sales and customer experience.
・It is possible to understand the current situation
Through data analysis, you can objectively and accurately understand your company's position in the market, strengths and weaknesses, past sales trends, and future forecasts. Also, based on the results, you can understand areas that need to be fixed in the future and areas that are in demand.
・Improved efficiency
Data analysis allows you to adjust inventory and optimize processes to minimize waste and waste.
Here, we will discuss some of the obstacles that can arise when tackling data analysis. However, by understanding its nature and taking countermeasures, you may be able to successfully overcome it.
・Incurrence of time and cost
Proper data analysis requires the right tools, techniques, and skills. However, introducing tools for analysis and hiring human resources who can immediately work can be costly. Also, it takes time to train existing members to be capable of data analysis.
・Occurrence of labor
Data analysis requires time and effort to process and analyze large datasets. Therefore, we need the help of analysts with specialized skills. Additionally, the results obtained through complex analysis do not necessarily lead to good results, and there is a possibility that you may not get the results you were looking for.
・Actual situation that cannot be understood from numbers alone
The results obtained from data analysis have the disadvantage that it is difficult to understand how people actually feel. For example, if there is data indicating that the congestion level is 150%, you cannot know whether people feel uncomfortable at 150% unless you actually experience it based on the data. It is also important to directly experience the results so that there is no gap between the numerical results and the actual experience, and to avoid producing incorrect results.
After understanding the benefits of data analysis and the obstacles that we have explained so far, we will now focus on the key points when actually working on data analysis.
・Clear goal setting
First, check what kind of data you want, why you need it, and how to get it. Then, by setting clear goals, subsequent processes can be carried out efficiently and unnecessary steps and costs can be avoided.
・Use appropriate and correct data
Processing data is a time-consuming and difficult step, but if data with noise or data with different numbers is mixed, it will be difficult to obtain accurate results from data analysis, and your analysis may be wasted. not. Therefore, the shortcut to our goal is to spare no time in the data processing process and obtain high-quality data.
・Visualization of collected data
By visualizing the collected data in easy-to-understand formats such as graphs, it becomes easier to analyze the data. By understanding patterns and trends, you can make decisions and share information smoothly, and proactively avoid problems.
・Use of BI tools
BI (Business Intelligence) tools are software that allows organizations and companies to analyze and visualize data to assist in management, strategy, and operations. Business analysis requires specialized skills and knowledge, and it takes time to do everything manually from scratch. By using BI tools, you can consolidate data in one place and significantly reduce the burden of data aggregation and analysis.
Now that we have covered the main points, let's look at the actual flow of data analysis. By understanding this step, you will gain a deeper understanding of the benefits and important points mentioned earlier.
Goal setting (problem definition)
As mentioned above, the key point when analyzing data is to identify the problem to be solved, clearly define the goals, and clarify the purpose of the analysis. In order to efficiently collect useful data for data analysis, it is important to have a clear purpose.
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Hypothesis setting
In a rapidly changing market and needs, it is very important to test hypotheses and find out the level of needs. If you have several hypotheses, it is important to decide on priorities and test them.
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Deciding how to analyze data
Based on the results obtained from the previous hypothesis test, we decide on the data to be collected, the optimal method, period, amount, etc.
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Data collection
Once the analysis method is decided, we begin collecting data. If you find any issues with the plan, such as the period or method, you can revise the plan at this point. Data collection can be made more efficient by using tools and automation.
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Data processing
Remove missing data or transform or split values in a dataset to make data easier to use. By processing the data, you can obtain properly organized and accurate analysis results.
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Data analysis
We will actually analyze the collected and processed data using appropriate methods according to the purpose. Based on the results, we will discover the next goals and issues, and plan improvements.
There are various methods for practicing data analysis, but here we will introduce five representative methods. In addition to understanding the benefits, points, and flow of the process, you will be able to perform higher-quality data analysis by acquiring knowledge of the method.
・Cross Tabulation
Cross tabulation is a method of collecting data based on two or three categories and assigning it to rows and columns in a table to see the relationships between the data. For example, by conducting a questionnaire about a certain product, receiving responses based on interest, age, and gender, and then allocating the collected data to a table, you can determine whether men or women are more interested, and whether they are interested. You can understand the age group.
・Association Analysis
Association analysis is a method for extracting relationships and regularities from multiple seemingly unrelated data. For example, we can identify patterns and trends in purchasing behavior from purchase history. This technique is used to suggest other products to customers who have purchased a particular product.
・Cluster Analysis
Cluster analysis is a technique for grouping similar data and understanding the structure within the data. Dividing your data into clusters based on similarities makes it easier to understand your data. Cluster analysis is used to divide customers into groups, image processing, pattern recognition, etc.
・Logistic Regression Analysis
Logistic regression analysis is a method called "binary classification" that predicts whether a specific event will occur (0 or 1). For example, whether to purchase a product or not, whether to pass or fail a test, etc.
・Decision Tree Analysis
Decision tree analysis represents the decision-making process in a tree structure. This technique is used to make decision rules for a particular problem more visible and easier to understand. It is useful for classifying data and extracting information, and is used for marketing strategies and analyzing consumer behavior.
There are many companies that have improved their existing services, created new services, and improved their business performance through data analysis. Here are some representative examples of companies that have successfully implemented data analytics.
・Sushiro
Sushiro, a conveyor belt sushi restaurant, uses an IC reader installed at the corner of the lane to read information about the ingredients and an IC chip attached to the string, recognize the type and quantity, and send this information to AWS's data analysis service. , collects approximately 1 billion pieces of data annually. We also collect order terminal history and cash register information, all of which can be checked using BI tools.These are useful for countering food loss and analyzing the freshness of toppings, allowing us to achieve efficient management.
・DyDo Drinko
DyDo Drinko, a beverage manufacturer famous for its coffee, increased sales by 20% to 30% by using eye tracking to analyze customer perspective data and improving product displays and packaging images. We also actively sought to secure human resources for data analysis, and not only hired people who could work immediately, but also trained our employees in data analysis skills. The company has announced that it will actively incorporate data analysis into beverages other than coffee in the future.
・Benesse Corporation
Benesse Corporation has achieved success by focusing on DX in the education field, which is said to be difficult to commercialize. For example, "Shinkenzemi" has been collecting data since the days of traditional paper teaching materials, but after introducing digital learning materials, it has been able to analyze the huge amount of learning data accumulated through digital learning to become more effective. We strive to provide the best possible learning experience and provide higher quality services.
Up to this point, we have explained the overview and benefits of data analysis, points to consider when working on it, and examples of actual use.
As mentioned at the beginning, as the promotion of DX accelerates, the use of data is becoming more important in various decision-making aspects of business, and its weight is increasing year by year. In addition, because the types of data are diversifying and the amount has become too much to process manually, correct data analysis not only speeds up decision-making, but also has a role that cannot be seen in normal management. Being able to visualize parts opens up new possibilities. In the future, collaboration with AI is also expected, making data analysis even more convenient, efficient, and accurate.
In addition, DX of analysis is progressing in order to perform quick data analysis, and various useful data analysis tools have been created, making DX a must-have.
At JIITAK, we support the DX of companies that are taking on the challenge of creating value by making full use of digital technology. If you have any problems, or if you are not sure what your challenges or goals are, but would like to consider ways to improve the environment, Contact JIITAK!
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