What kind of problems can be solved by using big data? It’s a little abstract, but it can be summarized into three main categories: “I can’t get the information I want in an easy-to-use format,” Problems That Can Be Solved With Big Data.
“I can’t make highly accurate predictions about the unknown,” and “I’m going to have surpluses and deficiencies.” I can do it. We will explain this with specific examples below.
I Want To Extract Necessary Information In An Easy-To-Use Format
There are many situations in business where decision-making is required. If you have information to judge, you can make a more accurate decision at that time.
If a massive amount of data is generated, collected, and stored. The necessary and sufficient data for judgment is output (visualized) in a form that is easy to understand and use; even human resources without experience and know-how can reach a certain level.
Results can be obtained.In addition, by utilizing data mining that visualizes superficial data and the deep layer, it is possible to grasp data from unknown situations and angles that have never been seen before, leading to new business planning.
・Investigate the cause of illness from patient data and develop drugs with few side effects
・Install sensors and cameras in the soil of fields and use them to improve the productivity and quality of agricultural
Products Analyze movements and place main products where they can be seen frequently
・Automatically determine parts required for repair by comparing repair request details, past repair history, and equipment model number data
・Store sales data and employee numbers Based on behavior data and product display data, employees are prioritized in areas with high customer unit prices
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I Want To make Highly Accurate Predictions About Unknown Things
If we can predict the future, such as tomorrow’s weather or demand forecasts, we can avoid unfavorable events, achieve better-than-expected results, and prepare in advance for better results. If this goes well, we will gain an advantage over our competitors in business, and in the public sector, we will be able to realize a safe society and a healthy life.
・Automatic display of search candidates in search engines ・
Predict consumer preferences based on past purchase data for promotions
・Link past purchase histories of visitors from cameras installed in stores and use them for customer service.
・Attach sensors to devices and equipment to collect operating status data to prevent accidents and failures
・Attach sensors to bridges to measure vibration and strain to detect deterioration at an early
stage Accidents are prevented by detecting abnormal accelerator operations by comparing with data
・Dangerous places are displayed on a map at that time based on past crime data
I Want To Eliminate Surpluses And Shortages
It is also related to issue 2, prediction, but using big data can eliminate excesses and deficiencies and optimize. You can optimize supply and demand and maintain the proper inventory to reduce losses and costs.
・Analyze the congestion of mobile phone base stations and the location and connection rate of smartphones, and plan the placement of base stations that are easy to connect
・Collect data from the IC chip attached to the conveyor belt sushi plate and use it for freshness management
・Recommend products that suit customers based on past order history and return data for each customer・Contribute to sales of customers・
Representative Analysis Methods For Big Data
It is impossible to obtain suggestions from big data, so it is necessary first to analyze it and make it usable.
If you use self-BI, etc., you don’t need any particular knowledge of data analysis, but if you know it, it may lead to problem-solving.
Here, we introduce six typical methods for analyzing data.
Cross-tabulation is a method of dividing data by attribute and multiplying it by multiple attributes for analysis. It is said to be the most basic data analysis method and is also installed in Excel as a standard function.
For example, analyze with two axes, “questionnaire item” and “respondent’s age.”
It is also possible to further subdivide by increasing the number of analysis axes to be multiplied; for example, it is called “triple cross-tabulation” when using three analysis axes.
Cross-tabulation allows you to compare trends in different attributes.
Decision Tree Analysis
Decision tree analysis is a technique that repeatedly performs cross-tabulation to discover multiple factors for a specific result and clarify more substantial grounds. The analysis forms a tree-like model, hence the name decision tree analysis.
Decision tree analysis enables prediction, discrimination, and classification, so it can be used when you want to know the characteristics of people who are likely to purchase your company’s products and the attributes of people with high customer satisfaction.
Logistic Regression Analysis
Logistic regression analysis is a type of “multivariate analysis” that analyzes multiple variables, and the result is “probability.” So it’s a number between 0 and 1.
It predicts variables that have not yet been identified from previously identified variables and show relational expressions to explain the results that have already been identified. It is a method that is used for
By performing logistic regression analysis, you can examine the factors that predict the outcome from the factors that affect the outcome.
Cluster analysis is a method of grouping data based on similarity and analyzing the characteristics of each group. Grouped populations are called “clusters.” Cluster analysis is a method suitable for extensive data analysis because the amount of calculation is small.
This method is used when it is impossible to classify data by demographic attributes such as age and gender and is used for customer segmentation and brand positioning.
However, after deciding how many clusters to divide, it is necessary to try several types, and it will be a process of trial and error.
Association analysis is an analysis method that finds relationships mainly in purchasing data and makes it possible to make predictions such as “If this were the case, it would be like this.” It is most suitable for data mining of big data. It is used for product analysis.
The analysis uses a “machine learning model” that extracts patterns and relationships from the data. By performing association analysis, it is possible to examine the correlation of data such as “Customers who purchase product A tend to purchase product B as well..”
Points To Be Aware Of When Analyzing And Utilizing Big Data
Analyzing and utilizing big data makes it possible to measure effects with high precision and discover new businesses.
However, when analyzing and utilizing big data, be careful of the following three points.
Clarification Of Purpose
Just as there are six primary analysis methods introduced in the previous chapter, the analysis method that should be adopted will change depending on what you want to achieve using big data. Furthermore, the method of data collection and what kind of data is collected will also change depending on the purpose.
In addition, it becomes necessary to return to the original purpose when problems must be solved, or judgments arise using big data. When using big data, first clarify the purpose and, if possible, set goals before working on it.
Repeat The Analysis
Big data is analyzed and utilized to achieve the clarified purpose, but there are many cases in which suggestions cannot be obtained from a single analysis. It will be necessary to continue working from a medium- to long-term perspective while changing analysis and data collection methods.
Penetration Into The Management Layer (Management Layer)
Although it is related to the previous two points, big data is deeply related to management strategy depending on the purpose of utilization.
In addition, it takes time to produce results through the use of big data, and there is a cost burden during that time, so understanding and consent from the management layer is essential when promoting the use of big data from the bottom up.
It is essential to spread the significance of considerable data utilization to the management layer and, if possible, have the management layer touch the data through dashboards and the like.