In recent years, many media outlets, including television, have reported various AI-based analysis results. Now that “analysis by AI” has become a hot topic in various fields, the demand for it is growing rapidly. So what exactly is this AI analysis used for, and what kind of analysis is it performing?
To answer these questions, this article explains in detail what AI analytics is, the process, benefits, and challenges of analytics. It also introduces case studies of data analysis and analysis tools.
What is AI analysis?
AI analysis is when AI analyzes trends in data and makes judgments about the future and the current situation. AI is capable of various analyses, as if it were intelligent, with human-like thinking.
AI Data Analysis Techniques – Data Mining
Mining is an English word that means excavation. In other words, data mining is a technology and method for discovering useful information from huge amounts of data.
Data mining removes unnecessary parts from the data and finds relationships between useful information. We also use data mining to make predictions and classify data.
Methods used in data mining include pattern recognition, regression analysis, and clustering. Models are generally created using machine learning algorithms using statistics and AI, and are based on the theory of information engineering.
What AI analysis can do
I think there are many people who are considering introducing AI analysis. Therefore, we will introduce the merits of introducing AI analysis here. There are three main advantages.
Strengthen human resource utilization
The first is that it can be used to strengthen the utilization of human resources.
With AI taking over tasks that were traditionally done by humans, it will be possible to allocate human resources to tasks that require more manpower. It also makes it possible to systematize abstract things such as intuition and tricks, leading to a reduction in in-house training costs.
Defects can be detected
The second is defect detection.
When humans detect defects in manufacturing equipment and products, accuracy varies, but machines can detect them more accurately.
Labor shortage can be resolved
Third, AI will help solve the labor shortage by supplementing the labor force in operations where there is a shortage of manpower.
With the development of AI technology, the number of tasks that can be entrusted to AI is increasing, so it can be said to be a great advantage for companies facing the problem of labor shortages.
Challenges in introducing AI analysis
The benefits of using AI analysis are great, but there are the following issues when introducing it.
- Difficult to prepare data
- Lack of human resources who can utilize AI analysis
- Costly and time consuming to implement
I will explain in detail below.
Difficult to prepare data
A huge amount of data is required for data analysis using AI.
Since the types of data required will differ according to the purpose of analysis, it is necessary to prepare different types of data rather than using the data that has been held as it is.
Also, since quality is important as well as quantity, the preparation of data can be said to be the first challenge in introducing AI analysis.
Lack of human resources who can utilize AI analysis
People who can analyze data with AI are called data scientists.
According to a survey by the Ministry of Economy, Trade and Industry, as of 2018, there was a shortage of about 20,000 data scientists. The shortage of data scientists is becoming more serious now that the demand has increased further.
According to a survey by the Japan Data Scientist Association, 62% of companies answered that they were unable to secure data scientists as planned in 2021, making it difficult to secure human resources in-house.
In order to secure human resources who can utilize AI analysis, it will be outsourced to an external company.
Costly and time consuming to implement
It takes time and money to prepare the environment for introducing AI analysis.
In order to perform AI analysis, storage for accumulating data and a high-performance computer for analysis are required.
We also have to develop a system and analysis AI to efficiently collect data.
If the company does not have the personnel capable of developing the environment, the development will be outsourced, which may incur further costs.
The process of introducing AI analytics
Next, I will introduce how AI performs analysis. The five main steps of AI analysis are:
- Clarification of goals
- Data preparation
- Data preprocessing
- model making
- model evaluation
Clarification of goals
First, you need to define your business goals and your analytical goals. A business goal is an economic impact such as an increase in sales or a reduction in costs, and an analysis goal is the result of analyzing data.
These specific goals are easy-to-understand indicators when considering how the results of analyzing the collected data will actually be useful and produce results.
Data preparation
Once the goals of the analysis have been clarified, the next step is to prepare the necessary data.
It is important to decide the data to prepare after calculating back from the goal and examining the data that matches the goal.
When observing data to be put into AI analysis, indicators such as whether it is suitable for machine learning, whether the learning data is large and stable, and whether there is little exceptional data are important. Rather than entering data blindly, it seems sometimes more efficient to start the analysis with simple data and enter additional data after the analysis has been completed.
The diagram below illustrates that process.
It is necessary to utilize existing data that you have, open data published on the Internet, etc., and data sets published by companies and research institutes.
However, since it is unknown when open data will become unusable and the format of the data may change, in the case of systems that need to be operated continuously, such as AI analysis, open data can only be used as supplementary data. It is better to use
Also, when searching for necessary data from existing data, it will be easier to search if you are conscious of the 5W2H: when, where, who, what, why, and how.
Data preprocessing
After collecting the data, we move on to the data preprocessing stage.
Data preprocessing (processing performed before data learning) takes the most time in a series of processes, and statistical data shows that 80% of the total man-hours are spent here. And there are six ways to process this data.
- Processing target variables
- Manipulation of explanatory variables
- Outlier handling
- Number processing of learning data
- Image data processing
- Processing text data
These tasks will enable highly accurate AI analysis.
In addition, tools such as “nehan”, a service that streamlines analysis work and accelerates business improvement through data utilization, are gradually appearing that aim to reduce costs by reducing data preprocessing as much as possible. Consider using these as well.
model making
At this point, we are ready to enter the modeling stage.
Modeling is about deciding which data to put into which algorithm.
There are about 20 algorithms in use today alone. At the model creation stage, it is necessary to focus on two things: choosing an algorithm that is consistent with the goals of the analysis, and examining the number of data and the compatibility of the algorithm.
There is a compatibility between the amount of data and the algorithm, such as whether it is easy for people to interpret the analysis results depending on the algorithm.
model evaluation
After deciding on an algorithm and letting it learn with machine learning, the final step is to evaluate the model. There are four basic indicators for evaluation:
- accuracy
Machine learning predicts the future from past data, so model accuracy is an important indicator.
- Degree of overfitting
In machine learning, overfitting is common and should be taken into account as well.
- Interpretability
It is evaluated from the perspective of whether people can understand the results produced by the model. It is important to be able to understand the point of “what factors led to a conclusion like ‘A’?”
- Implementation time
Since machine learning can handle large amounts of data, the learning process can take a long time. It is a judgment material whether the study time is sufficient or not.
Use cases of AI analysis
Next, we will introduce an example of using AI analysis. Currently, AI analysis is widely used in various fields.
Autonomous driving technology
Autonomous driving has become a hot topic in recent years. AI analysis is also used in this autonomous driving technology. We collect and analyze many driving scenes and traffic data, and let AI learn how to drive safely. As a result, we were able to achieve normal operation even in automatic operation.
Recruitment activities
AI analysis is also used in corporate recruitment activities. By collecting big data such as individual skills and history information and performing AI analysis, it is now possible to recruit human resources suitable for the company.
AI analysis can also be used to identify entry sheets. In fact, some companies use a two-step recruitment system in which, in addition to making hiring decisions based on AI analysis, entry sheets that AI has judged to be unsuccessful are rechecked by human eyes. This system has the advantage of allowing applicants to be evaluated equally without human prejudice.
Voice emotion analysis API
Voice emotion analysis API is a service that analyzes the voice database of tens of thousands of people and analyzes the four emotions of joy, normal, anger, and sadness in real time, regardless of language, and the level of vitality. It has been successfully implemented in many fields such as healthcare.
Image-based pathological examination
By analyzing images, AI can remember the signs of illness and accurately diagnose illness.
For example, early gastric cancer has a variety of shapes, and it was difficult for even experts to recognize it. Therefore, using image recognition technology that utilizes deep learning, we have established a highly accurate display method with a positive predictive value of 93.4% and a negative predictive value of 83.6%.
Sentence analysis service
AI analyzes text data such as “customer voices”, “weekly sales reports”, “patents and papers”, and “review data” held by companies and supports solving business problems. You can expect more accurate results because you can analyze your customers’ opinions and emotions. The field of natural language processing, which processes human words, is still in the development stage, and research and development for recognizing and understanding human words is flourishing all over the world.
Free AI analysis tool
Next, I will introduce an AI analysis tool that can be used for free.
Orange
Orange is a free data processing tool. It’s open source, so you can adapt and customize it for your company.
In addition to basic functions such as creating scatterplots and clustering, the ability to visualize multidimensional data on a plane is also available.
If you use paid functions, you can also perform more advanced analysis such as text mining and network analysis
Power BI (desktop version)
Power BI is a data analysis tool provided by Microsoft Corporation. It has excellent compatibility and can work with Excel. It is very convenient, for example, you can change the dashboard to your own specifications. Also, the desktop version is available for free, so you can use it as a trial version.
Even the free desktop version has features such as grouping and forecasting, which help users discover patterns that are often overlooked and provide new insights.
If you update to the paid version, you can search for answers from conversational questions and assist users.
Octoparse
Octoparse is an analysis tool available free of charge to individual users. Teams and companies will be charged a monthly usage fee of ¥1,300 ($89), but there is a 5-day money-back guarantee.
You can extract the necessary information from the infinite amount of information on the web and conduct competitive research and word-of-mouth research.
Easy to use even for beginners as no coding is required. Because it can be scheduled, it also has the ability to automatically collect data based on preset time periods.
In addition, it has high usability and has abundant templates for data collection.
Paid AI analysis tool (some free versions available)
Here are some paid tools. Some are paid and some are free.
WebHarvy
WebHarvy allows you to easily snip text, HTML, images, URLs & emails from any website and save the snipped data in various formats.
It also has a function to automatically collect information related to keywords by registering keywords and a function to categorize data on web pages.
The usage fee varies depending on the number of users, and it is about 20,000 yen (129$) for one person in a one-time purchase format. A free trial version is also available.
Talend
Talend is a great tool for data integration. Integrate data in the cloud and on-premises environments and make effective use of it in your business.
The goal is to realize more data-based business operations by increasing the reliability of data.
All processed data can be output as Java code. You can also try it for free.
There are 4 plans and you can try a demo on the site.
AI analyst
AI analyst can be used simply by linking with the world’s No. 1 access analysis tool “Google Analytics”. Based on Google Analytics data, we propose site improvement proposals, easy-to-understand reports, and interesting page survey results.
You can use this site for free at first.
Site usage fees vary depending on the size of the site.
Tableau
Tableau is a data analysis tool that can be operated in your own data center. A wide variety of analysis charts and models are available, allowing you to analyze data from various perspectives.
Data can be centrally managed on the cloud and has a backup function.
It also has a unique visualization function that makes it easy to see the analysis results. A free trial is also available.
Paid plans can be introduced from 8400 yen per month, and additional functions can be purchased.
FineReport _
FineReport is a data analysis tool with excellent visualization capabilities. By leveraging over 70 different chart analysis models, you can display data with visual impact. It has high usability and can be linked with other business efficiency tools.
You can perform data mining, data input, access management, etc. without programming.
All tools are available for free for 90 days.
SAP Analytics Cloud
SAP Analytics Cloud is a data analysis tool that runs on SAP’s integrated core system. In addition to basic functions, advanced functions such as financial planning and simulation are also implemented.
The main goal of this tool is to use the results of the analysis for decision making. Packages are prepared for each function and industry, so you can use the functions that suit your purpose.
A free version is also available on this site.
summary
In this article, we introduced a wide range of AI analysis, including processes, use cases, and analysis tools.
AI analysis is difficult, and there are still not many people who can handle it. However, the application fields of AI analysis are expanding, and the demand is also expanding.
It would be a good idea to actively consider introducing it not only for individuals but also for companies.
