Table of Contents
The importance of shifting to data-driven management through the utilization of big data and data analysis is constantly being emphasized. In particular, data analysis is attracting attention as a technology that creates new business opportunities. But what exactly does data analysis mean? Also, many people may not understand the procedure for data analysis. Therefore, in this article, we will explain the basics, procedures, and typical methods of data analysis for those who want to learn data analysis from now on or who are considering business utilization.
What is data analysis?
In today’s digital age, where everything is digitized, it has become important to formulate a solid business strategy by gaining “awareness” from the analysis results of those data. But what kind of technology is data analysis in the first place?
Data analysis basics
Data analysis is the division of data into several elements to clarify the details of components and composition. As the result of data analysis, more different information will be returned as the content and viewpoint increase. In today’s digital age, where everything is digitized, it is important to formulate a solid business strategy by gaining “awareness” from the analysis results of those data.
Need for data analysis
With the rapid development of technologies such as smartphones and the Internet, the values of modern consumers are extremely diversified. As in the past, the era of selling anything with a huge investment in advertising is gone, and marketing that meets the needs of customers is required. Data analysis can understand complex consumer needs by processing customer behavioral data.
Difference from data utilization
“Data utilization” is often used as a term similar to data analysis. At first glance, the difference is hard to see, but in reality there is a big difference. Data utilization refers to improving operational efficiency and productivity by allowing companies to continuously utilize data. On the other hand, data analysis refers to mobilizing knowledge such as statistics to obtain various information from data such as regularity, outliers, correlation and causal relationships.
Benefits of data analysis
Data analysis will be of great help in the shift to digital transformation and data-driven management of companies, but what exactly will be possible? I will explain the merits of data analysis.
Can predict future results / results
Data analysis enables analysis of the current situation and prediction of the future. By collecting data, it is possible to grasp the market share of the company’s market, which has been done manually so far, and to predict future market trends and sales trends with higher accuracy. By collecting data and analyzing relevance and causal relationships, more reliable results can be obtained and utilized in future measures.
The problem can be extracted
By utilizing data analysis, it is expected to gain the awareness that was overlooked by aggregating various types of information that had been dispersed within the company. This may lead to improved sales and operational efficiency. Highly accurate information derived from accumulated data will be valuable to the organization, rather than the hypotheses and guesses that have been constructed based on intuition and rules of thumb as in the past.
Improve marketing outcomes
If you can use data analysis to accurately understand customer needs, marketing results will inevitably increase. Personalized marketing for each customer is extremely important in today’s diversified consumer values and needs. For example, if you analyze the behavior of a customer, you can see what kind of lifestyle the person wants and what kind of product they are interested in, and how to approach it naturally.
Main flow of data analysis
In order to improve the accuracy of data analysis, it is important to keep the main points in mind and follow the steps in order. From here, I will explain the general procedure of data analysis.
Set the purpose
In order to improve the accuracy of data analysis, it is important to keep the main points in mind and follow the steps in order. We will explain the general procedure along with the introduction of points to be noted when analyzing data.
Make a hypothesis
What is needed in data analysis is the construction of hypotheses. Hypotheses are the basis for data analysis and numerical testing. However, it is not realistic to try to find out all the hypotheses because it would be a huge amount of work. By analyzing the data and extracting issues, you can prioritize from multiple hypotheses and narrow down the actions with higher accuracy.
It is important to set questions that can answer the current problems and problems using Yes or No formulas, multiple formulas, etc., and extract hypotheses that are likely to contribute to problem solving.
Collect data
Once you have a hypothesis, collect the data you think you will need to test it. If you can earn enough good quality data to help you analyze here, you will get better analysis results. In addition, we do not miss “mining” to discover useful information for business from the collected data. What kind of numerical data is needed to test the hypothesis, what kind of analysis method should be used, what data should be used for analysis, and what should be analyzed? After considering the above, we will collect the data.
Analyze the data
Once the data is collected, it will be analyzed. The analyzed information is useful for business strategy planning and decision making. In this case as well, if you introduce a data analysis service that suits your company’s purpose, you will not need much human resources and you will be able to work efficiently, so let’s actively utilize the tools.
How to do data analysis
When you think about introducing data analysis, you might think of Excel as the easiest tool. While many companies are actually using Excel for data analysis, the share of tools specializing in data analysis and analysis has increased dramatically in recent years. In the following, we will show you how to perform data analysis.
Exce
Excel, which has been introduced by many companies as a tool for data aggregation, is good at visualizing data in the same format in a short period of time, in small quantities. The introduction cost is very low, and the operation method is easy to find on the Internet. On the other hand, it is not suitable for analysis of long-term and large amounts of data. In the case of data of different formats, the man-hours required for aggregation work will increase, and if the amount and type of data is large, the processing will take time, and eventually the data may crash without being saved.
IT tools
With IT tools, you can smoothly analyze large amounts of data and data in different formats for a long period of time. The integration of data in different formats, which used to take a lot of effort in Excel, can be linked with less man-hours depending on the tool, enabling more advanced data analysis. Many of them provide cloud-based services, so it is also attractive to be able to realize operations that are compatible with the era of telework. If you want to lay the groundwork for more serious data analysis than Excel, one way is to introduce a tool.
Machine learning
Machine learning, a field of AI (artificial intelligence), is simply a method of finding features and patterns by repeatedly learning data.
By utilizing this for data analysis, it is possible to maximize ordinary income while entrusting the various tasks that had been labored to the computer. By improving work efficiency, human energy can be redistributed to more creative work, and it can be used in a wide range of scenes such as purchasing behavior prediction, image recognition, and accurate recommendation function.
For data analysis using AI, TRYETING’s UMWELT!
If you are looking for a data analysis tool that utilizes AI, we recommend the no-code AI cloud “UMWELT” provided by TRYETING.
In addition to demand forecasting and purchasing forecasting functions that use data analysis, it also has an optimization function for ordering and inventory production management. By combining the set-up algorithms, you can use it at the lowest compact cost in the industry, so you can rest assured that it will cost you. If you are considering using data analysis, or if you want to keep costs down, please feel free to contact us.
summary
Appropriate use of analytical data will enable you to obtain useful information for your business, such as demand forecasting and generation of ideas for new businesses. Data analysis can be dealt with in Excel without the need for specialized tools. However, data analysis by Excel requires knowledge about data analysis. AI tools are useful for companies where it is difficult to secure employees with specialized skills and knowledge. With UMWELT, it is possible to respond according to the accumulated data and format of each company. If you want to use data analysis for your business, please consider UMWELT.
