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  • What is smart health? From its potential to its practical applications.

    What is smart health? From its potential to its practical applications.

     

    Smart Health

    Due to the declining birthrate and aging population, the healthcare industry is predicted to face increasing difficulties in supporting the daily lives of the elderly, driven by a sharp rise in the number of people requiring long-term care and a shortage of caregiving staff. As physicians concentrate in urban areas, regional healthcare faces a shortage of doctors, threatening people’s lives as patients struggle to receive adequate medical care. Furthermore, an aging population with a low birthrate leads to soaring social security costs, resulting in high social insurance premiums for workers and potentially leaving some without sufficient social security coverage. While continuous social security system reform is required, it is also necessary to consider methods for efficiently operating public services like medical care and long-term care while maintaining quality. Smart health is gaining attention as one means of addressing these social issues.

     

    The Concept and Effects of Smart Health

    Smart health inlves managing people’s health using IT and communication technologies. In Japan, the Digital Agency is promoting smart health, with the expectation of realizing a society where people can lead healthy and fulfilling lives.

    What is Smart Health?

    Smart health is healthcare that utilizes ICT (Information and Communication Technology) . It aims to improve the quality of medical and welfare services and enhance the QOL (Quality of Life) of patients and users. Specifically, it involves using wearable devices, remote treatment, and AI (Artificial Intelligence) to promote preventive medicine, increase the efficiency and sophistication of medical care, and enhance patient autonomy.

    What is Healthcare?

    Healthcare, in a narrow sense, refers to medical and pharmaceutical equipment and services. In a broader sense, it includes elements beyond medical treatment, such as health and physical condition management. In Japan, the super-aged society resulting from the declining birthrate and aging population is causing significant changes, particularly in the business environment surrounding elderly health management, medical care, and pharmaceuticals. In the context of Digital Transformation (DX) undertaken by companies, it is predicted that alongside efforts towards digitalization, the connection between healthcare businesses and IT platformers from other industries will deepen. Additionally, as the COVID-19 pandemic has heightened people’s interest in health management, the role of healthcare businesses has become increasingly important.

    Benefits of Smart Health for Healthcare and Wellness

     

    Smart health is expected to bring various benefits. By utilizing ICT, it enables advancements in areas previously difficult to address, leading to the creation of new medical services.

    BenefitSpecific Expected Outcomes
    Promotion of Preventive MedicineContinuous monitoring of health status using wearable devices can lead to early disease detection and prevention.
    Increased Efficiency & Sophistication of Medical CareThe use of telemedicine and AI can increase the efficiency and sophistication of medical care, leading to reduced medical costs and improved quality of care.
    Enhanced Patient AutonomyUsing wearable devices and online consultations allows patients to more easily manage their own health status, enabling them to take a proactive role in their health management.

    Considering these benefits, let’s explore the possibilities after the realization of remotely provided medical care. If deregulation enables the remote provision of medical care, it could connect with non-medical fields where remote services are already more common. While life-critical actions will remain regulated as before, the boundary between medical and non-medical fields may blur in some areas, making collaboration between the two essential. Services are likely to evolve around the following two key concepts:

    The first is “Just-in-Time,” the concept of delivering what is needed, when it is needed, to the user in an appropriate manner. For example, a combination of online consultations, medication guidance, and drone-delivered pharmaceuticals could complete treatment remotely, even for mild symptoms or for patients who find it difficult to visit a clinic in person. Furthermore, if doctors and pharmacists were replaced by fitness trainers, and pharmaceuticals by health equipment, such systems could be applied beyond medical acts. It may become possible to receive all appropriate services remotely across the phases of prevention, treatment, and recovery, and the stages before and after.

    The second is “Personalization.” Technological advancements enabling more detailed, real-time data acquisition are driving a trend towards customization, allowing services to be tailored perfectly to individual users. Even in the healthcare field, personalized services designed for remote delivery are emerging.

     

    Types and Applications of Smart Health Devices

    IoT (Internet of Things) is a mechanism where everyday “things” connect to the internet, enabling them to exchange information and coordinate with other systems. For example, home appliances can be controlled by voice using AI assistants built into smart speakers. Technological advances utilizing AI and IoT are also causing innovation in the healthcare field, manifesting in various products and services that are beginning to permeate our society as smart health.

    Smartwatches and How to Use Them

    We can use the Apple Watch, a wristwatch-type wearable device, as an example. Beyond its timekeeping function, it can measure data during activities like cycling, yoga, swimming, and running, and it has various features that support health. For instance, in heart rate monitoring, it displays alerts when detecting unusually high or low heart rates, allowing the wearer to be aware of potential issues even without noticeable symptoms. Although not yet approved as a medical device in Japan, it also has a function to measure ECG (Electrocardiogram), which can contribute to the early detection of conditions like myocardial infarction. In the United States, there have been several reported cases where users without any apparent symptoms went to the hospital after receiving a warning from their Apple Watch, preventing severe health crises and saving lives.

    Smart Glasses and How to Use Them

    Smart glasses are eyeglass-type devices equipped with cameras, displays, sensors, and more. In the medical field, they can be used to improve operational efficiency, monitor patient vital signs, and facilitate telemedicine. This is expected to enhance the quality of medical care and reduce the burden on healthcare professionals. As smart glasses are still a developing technology, they are anticipated to become more widely used in medical settings alongside the proliferation of IoT.

     

    Strategies and Innovative Means for Promoting Smart Health

    The use of AI is a focal point in promoting smart health. AI is used to analyze data collected from things via IoT technology. A key feature is that the analyzed data is transformed into data with certain patterns, making it easier for humans and computers to utilize. Furthermore, through deep learning (a machine learning method that teaches computers to perform tasks natural to humans), AI can continuously learn, allowing it to propose solutions that lead to better outcomes. The combined use of AI and IoT is expected to further enhance the precision of smart health.

    AI and Smart Health

    In healthcare utilizing AI, services exist that promote people’s health through nutritional management of dietary habits and the combination sale of customized supplements. There are services where users answer questions about their lifestyle and nutritional status via smartphone, leading to the delivery of supplements, ingredients for home cooking, or pre-prepared frozen meals.

    Combinations with online consultations also exist. Services provide effective and efficient medical care by appropriately combining in-person and remote consultations. This includes effective guidance and management for lifestyle-related diseases like diabetes, as well as early prevention of severe conditions through remote monitoring of blood pressure and blood glucose levels.

    AI is also used in the medical field for early disease detection and preventing oversight of lesions, with increasing opportunities for diagnosis using image analysis of projected images from MRI and CT scans. Advancing AI-powered diagnostic imaging support requires vast amounts of data for AI training. The Japan Agency for Medical Research and Development (AMED) is conducting research through its “Medical Image Big Data Cloud Infrastructure” project. This involves collaboration between six academic societies related to medical imaging (such as the Japanese Society of Pathology and the Japan Radiological Society) and the National Institute of Informatics (NII) to build a large-scale medical image database and a common platform for AI development, aiming for sustainable AI advancement.

     

    Promoting Smart Health through Data Acquisition and Utilization

     

    There are primarily three types of data acquired in the healthcare field:

    1. EHR (Electronic Health Record): Data used by professionals within medical and long-term care institutions.

    2. HIE (Health Information Exchange): Enables electronic sharing of medical information among multiple medical and long-term care institutions.

    3. PHR (Personal Health Record): Facilitates electronic information sharing between patients and professionals.

    Methods for Acquiring and Utilizing Health/Medical Data

    Simply put, EHR is an electronic medical chart. An increasing number of medical institutions are digitizing records previously kept on paper, requiring continuous recording of patient medical history and treatment details.

    HIE is a system infrastructure that enables medical institutions to collaborate and share patient health information. If a large-scale medical and long-term care platform is built, patients could smoothly receive diagnoses even when changing hospitals, thanks to shared information, potentially enabling the provision of more advanced medical services.

    PHR allows individuals not only to view their own medical, long-term care, and health data but also to upload data they collect themselves, such as blood glucose levels and weight, enabling two-way interaction where healthcare professionals like doctors can review the data.

    If a large-scale platform linking EHR, HIE, and PHR is established, it could enable more advanced medical services than ever before, while also making personal health management more convenient.

    Concept of a Medical/Healthcare Collaboration Platform

    Using PHR data related to healthcare as an example, we can illustrate the construction of a public medical database. In B2B, horizontal sharing between medical/long-term care providers and pharmaceutical/medical device companies could enable collaborative development of medical equipment and drugs. In B2C, the collaboration between citizens (consumers) and health/insurance providers could advance the personalization of services through individually optimized health and insurance offerings. Furthermore, research and development collaboration between medical/long-term care providers and pharmaceutical/medical device companies could lead to the provision of individually optimized medical and long-term care services. If vertical sharing and horizontal sharing of data advance, it is expected that convenience will improve. This could be achieved by managing only basic information on a collaboration platform, while linking to and retrieving other necessary information from existing databases based on identifiers like the My Number or health insurance card number as needed.

     

    Proper Usage and Precautions for Smart Health

    There are various ways to use smart health. A familiar example is the use of the electronic medication notebook app. You may have scanned the QR code on a prescription to register it in such an app. Additionally, iPhones have a built-in Health app that automatically records daily steps and sleep data.

    Setting Up Smart Health Devices and Apps

    Linking healthcare apps with an electronic medication notebook allows for centralized management on a smartphone. By scanning the QR code on your medication notebook and completing member registration, you can use the service. Managing it via a smartphone app eliminates the need to carry a paper notebook, removing the risks of loss or theft and the mistake of forgetting to bring it, making it a very convenient service.

    Furthermore, the Health app pre-installed on iPhones accumulates health-related data, which forms the basis for various health management smartphone apps available on the market.

    [Sources: List of medication notebooks compatible with eYakuLink: Japan Pharmaceutical Association]

    Using Health Apps for Step Counting, Sleep, etc.

    The data recorded on an iPhone for daily steps and sleep duration can be viewed directly, but linking with an Apple Watch allows for more accurate data recording. By wearing an Apple Watch daily and continuously recording health data, the information accumulates within the iOS ecosystem and can be viewed on the iPhone. Health management apps installed on the iPhone can analyze this data and optimize personalized management methods within the app. In the United States, health management systems are often linked with medical institutions’ systems. Therefore, if an anomaly like abnormal pulse rate is detected for registered members due to illness or injury, the medical institution may send a notification. There are reportedly many cases where early detection prevented severe conditions and saved lives.

    Importance of Proper Information Handling and Privacy Protection

    A crucial element in digitizing the healthcare field is the ID (Identifier) . Typically, when receiving treatment at a hospital or clinic, a medical ID card issued by the institution is required. This card contains a unique ID assigned by that institution, and the medical records are managed using that ID. Visiting a different hospital means records are managed under that institution’s unique ID. However, over a long lifespan, people may change their surnames due to marriage or their addresses due to relocation. If past medical records are stored individually at each hospital or clinic, when a patient visits a new medical institution, their medical history must be gathered and understood by the attending physician. The time and effort required to coordinate this information can be a significant burden, and missing information could prevent the patient from receiving adequate care. To advance digitalization, further consideration is needed regarding how IDs can cover the broad healthcare domain.

     

    The Impact of Smart Health on Life and Society

    In Japan, against the backdrop of rising medical costs due to aging and the potential strain on the social security system supporting these costs due to the declining birthrate, the government is promoting a shift in healthcare from “treatment to prevention.” The expectations and role of smart health in this area are set to grow significantly.

    Improving Lifestyle through Smart Health

    Smart health is a method of improving people’s health and wellness (a proactive lifestyle approach aimed at living better, distinguished from traditional health) using technologies such as wearable devices, mobile apps, and medical equipment. Smart health helps improve lifestyle in various ways.

    Health Management, Lifestyle Improvement, and Improved Access to Care
    Smart health helps improve people’s health and wellness. Wearable devices can track various health data, including heart rate, steps, and sleep quality. This data helps individuals better understand their health status and achieve their goals. Smart health also helps reduce medical costs. Wearable devices aid in early detection and prevention. This allows people to receive treatment before they become ill, leading to reduced medical expenses.

    Improving QOL through the Fusion of Life Sciences and Digital Technology
    In a society with a declining birthrate and aging population, it is predicted that addressing rising medical costs and widening health disparities will be necessary. In the future of healthcare, it is likely to become common practice to measure cost-effectiveness through objective outcome indicators, assessing how well healthcare services, coordinated across the fields of health, medicine, and long-term care, contribute to improving people’s QOL (Quality of Life) . Raising the QOL of citizens gives significant importance not only to material wealth but also to mental well-being and satisfaction through health.

    ItemDescription
    ① From Life Support to QOL FocusPromoting medical and long-term care aimed at maintaining a fulfilling social life.
    ② From Treatment to PreventionAvoiding disease onset and severity through daily health management.
    ③ From Fragmentation to CoordinationProviding more precise prevention, diagnosis, and treatment through the integration of health, medical, and long-term care data.
    ④ Management & Evaluation Based on EffectivenessImplementing cost-effectiveness analysis through the measurement of objective outcome indicators.

    In this way, healthcare is being redefined as lifestyle design and support. It is expected that the goal will be to improve the QOL of all citizens, not just treating illnesses in patients, but also including healthy individuals. As medical care and healthcare become more closely integrated, the shift “from treatment to prevention” will likely accelerate.

     

    Smart Health and the Future of Society

    Smart health has the potential to significantly impact various aspects of society. Specifically, society may change through improved health and wellness, increased efficiency in medical care, and the creation of new industries.

    Impact on Society, Expectations, and the Potential of the Smart Health Market
    For example, with the “Spread of Telemedicine,” remote healthcare using wearable devices and mobile apps becomes widespread, allowing patients to consult doctors from home or work. This is expected to improve patient convenience and expand access to medical care. “Realization of Personalized Medicine” uses data to achieve medical care tailored to individual patient needs. This improves the precision and effectiveness of treatment, leading to enhanced QOL for patients. “Increased Efficiency in Long-Term Care” enables care staff to grasp patients’ health status in real-time and provide appropriate care. This improves the quality of care and reduces the burden on care staff.

    In these ways, smart health is poised to make our future lives richer and healthier.

     

    Conclusion

    Finally, let’s summarize smart health:

    • Smart health is healthcare that utilizes ICT (Information and Communication Technology).

    • Smart health offers benefits including “promotion of preventive medicine,” “increased efficiency and sophistication of medical care,” and “enhanced patient autonomy.”

    • Utilizing smart health may lead to the creation of services focused on “Just-in-Time” and “Personalization.”

    • Smart health devices include wearable items like smartwatches and smart glasses.

    • By utilizing AI, along with data analysis of EHR, HIE, and PHR, advancements in medicine and health promotion can be expected.

    • Smart health accelerates the shift in the future of medicine “from treatment to prevention.”

    Through smart health, there are high expectations for improving our lifestyles and realizing a society where people can lead healthy and fulfilling lives.

     

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  • What are the ethical issues in using AI? Explaining specific examples and points that companies should be aware of

    What are the ethical issues in using AI? Explaining specific examples and points that companies should be aware of

    ethical issues in AI

    Rapidly advancing AI technology has made our lives and businesses more convenient and prosperous. However, are you aware that this progress also highlights significant ethical challenges in the use of AI?

    This article provides a comprehensive overview of ethical issues in AI, including case studies and initiatives by private companies, as well as key points to consider to avoid AI ethics problems. If your company is considering implementing AI, please read on.

     

    What are Ethical Issues in AI Utilization?

    Ethical issues in AI utilization refer to the social and moral challenges that arise behind the convenience and efficiency gains brought by AI. For example, bias problems, which occur when AI learns from skewed data, risk leading to unfair judgments based on race or gender.

    Furthermore, the use of facial recognition technology and personal data raises concerns about potential privacy violations. Another representative ethical issue is the lack of clarity regarding accountability for decisions and actions made by AI.

    Thus, in our modern world where AI is widespread, AI ethics has become a critically important challenge. It is clear that the more AI permeates society, the more essential it becomes to address these ethical issues.

     

    Key Problems in AI Ethics

    What specific aspects are considered problematic in AI utilization? This chapter explains the main issues in AI ethics.

    Privacy Violations

    As AI technology evolves, vast amounts of personal data are collected and analyzed, making privacy violations a major concern. For instance, facial recognition technology and tracking via surveillance cameras can threaten individual freedom and privacy in exchange for convenience. Therefore, there is a growing need to establish clear rules for AI utilization, defining the acceptable limits of data collection and use.

    Bias and Discrimination

    Since AI systems learn from data provided by humans, they can directly reflect the biases present in that data. This includes cases where AI makes decisions that disadvantage specific genders or races, thereby exacerbating social inequalities. To solve this problem, it is crucial to improve data quality and develop AI with fairness in mind.

    Lack of Accountability

    The process by which AI arrives at a specific decision is often a “black box,” and this lack of accountability is a significant concern. Especially when AI is involved in critical decisions in fields like medicine or finance, transparency is required so users can trust the outcomes. To ensure this transparency, there is an urgent need to develop technologies that can explain the workings of AI algorithms.

    Unclear Allocation of Responsibility

    When a decision or action by AI causes a problem, the difficulty in pinpointing responsibility is another classic AI ethics issue. For example, if content generated by AI contains misinformation or copyright infringement, multiple parties are involved—platform operators, AI developers, users—but identifying who is responsible is challenging. This points to the need for measures at the national and governmental level, such as legal frameworks and ethical guidelines, to clarify responsibility in AI utilization.

     

    Three Case Studies of AI Ethics Issues

    Recently, AI ethics issues have frequently become major topics of discussion. This chapter introduces three real-world examples of such problems.

    Amazon (Gender Discrimination in Recruitment)

    Previously, Amazon used an AI-powered recruitment system. This system learned from past hiring data to judge candidates’ suitability. However, it was found to favor male candidates and disadvantage female candidates.

    This case demonstrates the risk that AI can perpetuate unfair judgments if its training data contains bias. Although Amazon eventually discontinued the system, it served as a catalyst for renewed focus on the transparency of data and algorithms in AI utilization.

    Samsung Electronics (Privacy Violation)

    In 2023, an incident occurred at Samsung Electronics where employees used an AI tool to process sensitive information, leading to a data leak. The primary cause was believed to be an engineer using ChatGPT to fix source code bugs. During this process, code containing sensitive information was sent to the AI’s servers, resulting in a partial leak of the data.

    This highlights the indispensable need for security measures to protect confidential data when companies utilize AI. Leaking sensitive information can lead to irreparable consequences, such as loss of competitiveness and social trust, making this a critical consideration for any company using AI.

    Tokyo 2020 Olympics (Unclear Responsibility in Traffic Accident)

    The introduction of autonomous vehicles was a major talking point during the Tokyo 2020 Olympics. However, one of these vehicles was involved in a collision with a visually impaired athlete within the athletes’ village.

    Subsequent investigations into the accident revealed an unclear allocation of responsibility among the multiple parties involved, including:

    • The vehicle manufacturer

    • The Olympic organizing committee

    • On-site guides

    This case illustrates the necessity of clarifying responsibility sharing when accidents or problems occur, especially as collaboration between AI and humans becomes more prevalent.

     

    Private Sector Initiatives Addressing AI Ethics

    Many companies are now undertaking various initiatives to tackle AI ethics issues. This chapter introduces some of these efforts.

    Google

    In 2018, Google established three AI principles to guide the integration of AI throughout its operations:

    1. Be bold with innovation.

    2. Develop and deploy AI responsibly.

    3. Work together to advance.

    These principles articulate the company’s key priorities and philosophies regarding AI use. Based on these principles, Google promotes the beneficial use of AI, explicitly stating its responsibility in AI development and deployment while addressing grand themes like economic development and scientific progress. By publicly committing to such high-level principles, Google solidifies its position as a leading company at the forefront of the AI field.

    Microsoft

    Based on the philosophy that “AI should be developed and used based on trust, for the benefit of all people,” Microsoft established six core principles for responsible AI development and use in 2018. These principles include:

    • Fairness

    • Reliability and safety

    • Privacy and security

    • Inclusiveness

    • Transparency

    • Accountability

    Guided by these principles, Microsoft develops AI systems with a strong emphasis on ethical considerations. Furthermore, the company promotes the socially responsible use of AI by focusing on internal ethics education and collaboration with external experts.

    Fujitsu

    Fujitsu has established the “Fujitsu Group AI Commitment” to actively promote the ethical development and use of AI. This commitment clearly outlines several key guidelines, including:

    • Human-centric AI

    • Ensuring accountability and transparency

    • Protecting privacy

    • Ensuring security

    • Maintaining fairness

    Based on this policy, Fujitsu employs a consistently ethics-focused approach, from AI research and development to social implementation. Additionally, the company has established an ethics committee comprising internal and external experts to continuously review and address ethical challenges related to AI.

    OKI Group

    In September 2019, the OKI Group established and published its “OKI Group AI Principles.” These principles define ethical standards for AI development and use, incorporating various elements such as:

    • Ensuring transparency

    • Ensuring accountability

    • Protecting privacy

    • Maintaining fairness

    The company is also strengthening collaboration with various stakeholders to promote the social implementation of AI, driving actions toward achieving human-centric AI development.

     

    Key Points for Companies to Avoid AI Ethics Issues

    When introducing and utilizing AI, companies must be mindful of several points. Finally, based on the discussion so far, this chapter explains key points for companies to avoid AI ethics problems.

    Ensuring Transparency and Accountability

    A crucial point when using AI is to clarify how its algorithms and decision-making processes function. Establishing a system that can explain to users and stakeholders how AI makes decisions builds trust in the AI. If an AI system becomes a black box, it risks unexpected outcomes or misunderstandings, making accountability a key to ethical AI use.

    Eliminating Bias and Ensuring Fairness

    Since AI makes decisions based on training data, biases within that data can be directly reflected in the output. Therefore, when developing AI, companies must take sufficient measures to avoid biased data and unfair judgments. Using diverse datasets and implementing mechanisms to monitor and adjust for biased AI decisions contribute to ensuring fairness and building social trust.

    Strengthening Privacy and Security

    For companies utilizing AI, protecting privacy and strengthening security are indispensable. Especially when handling personal or confidential information, implement robust security measures to protect company data from cyberattacks and unauthorized access. Regularly checking AI systems for vulnerabilities and ensuring data safety can help prevent AI ethics issues before they arise.

     

    Conclusion

    This article has explained AI ethics issues through examples and case studies, introduced initiatives by private companies, and discussed key points for avoiding these problems.

    AI is an extremely useful tool, but its use requires careful consideration of ethical implications. Revisit this article to understand the main problems and specific examples within AI ethics.

     

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  • How to succeed in cost reduction by AI? Detailed explanation along with examples!

    How to succeed in cost reduction by AI? Detailed explanation along with examples!

     

    When introducing AI, I think there are many people who want to streamline operations and reduce costs such as labor costs and development costs. There are several tips for successful cost savings with AI. In this article, we will introduce the points and examples of cost reduction by AI.

     

    How to choose AI for cost reduction?


    When introducing AI, many companies set sales increase and cost reduction as improvement goals. I will explain the points to pay attention to when considering AI.

    Select by utilization technology

    Image recognition / image analysis

    Technology for image recognition / image analysis that uses AI to identify what is reflected in an image or video and whether there are any abnormalities on the computer. At the site, there is no need for discriminating by skilled workers and training for new employees, which leads to reduction of labor costs.

    Demand Forecast Demand forecast

    that forecasts purchase volume and sales volume based on past data. It is now an indispensable part of marketing strategy. The greater the fluctuations in the products handled and the balance between supply and demand, the longer it takes to analyze, so AI intervention can significantly reduce work time.

    Data analysis Data analysis using

    AI makes it possible to discover new patterns and acquire knowledge (data mining) from a huge amount of data. In addition to reducing the cost of data analysis, the information obtained can be used for marketing.

    Optimization

    Optimization technology uses data analyzed by AI to derive how to achieve goals under limited conditions. Delivery routes in logistics companies and supply chains in the manufacturing industry can also be streamlined with this optimization technology.

    Building
    a data infrastructure By building a data infrastructure, you can collect, store, analyze, and visualize the accumulated data by integrating it. By consolidating them into one, you can shorten the time required for data analysis and utilization.

    Choose according to your company’s needs

    Business efficiency improvement
    The first keyword that comes to mind when reducing costs is business efficiency improvement. You can expect to improve work efficiency by having AI take over data entry work such as invoices and customer information management.

    Cost reduction

    By entrusting simple work and routine work to AI, it is possible to carry out work with a small number of people, and it is possible to reduce costs such as labor costs.

    Defective product detection and failure prediction
    AI, which has defective product detection and failure prediction functions, is a very useful function for manufacturers, and enables highly reliable work in a short time.

    Maximizing Profit

    If the introduction of AI streamlines operations and reduces labor costs and wasteful operations, it will ultimately be possible to significantly increase profits.

    Service development

    By incorporating AI into services, the range of service development can be expanded and service development with a higher degree of freedom can be performed.

    Decision-making power

    Demand forecasting and data analysis make it easier to determine the direction of new products and services to be developed. Explaining the results of AI will make it easier to obtain internal consent and improve decision-making ability.

    In an external environment where promotion of human resources development
    DX is indispensable, introducing AI in-house will lead to the development of AI human resources in the future.

    Benefits of introducing AI


    Now that we’ve explained how to choose AI, let’s talk about the benefits of introducing AI. If you are worried about the balance between the cost of introducing AI and the improvement of business efficiency, please refer to it.

    Reduction of labor costs and elimination of labor shortages

    With the introduction of AI, machines will automatically process simple tasks and routine tasks that were previously personalized. As a result, even a small number of people will be able to carry out internal operations without delay, and will be the savior of companies with serious labor shortages.

    In addition, mechanization leads to shorter working hours and reduces overtime and holiday allowances. There is an initial cost to introduce AI, but in the long run, it will reduce labor costs and reduce the cost of the entire company.

    Business efficiency and productivity improvement

    AI is by no means all-purpose, and there are some tasks that require human intervention within the company. By leaving the work that can be mechanized to AI, you will have more room and you will be able to concentrate more on the work that is personalized. As a result, work efficiency will be improved and productivity can be expected to improve.

    Data analysis / analysis prediction

    Accumulating and verifying a huge amount of customer data is a very time-consuming task. If it is AI, it can capture and analyze data in a short time. By deepening the depth of deep learning, more accurate analysis prediction can be realized. In addition, data analysis is effective not only when developing products and services, but also as a measure for human resource education and operational efficiency improvement.

    Improved safety

    Automation of safety management work at dangerous work sites leads to ensuring the safety of employees. Specifically, it is possible to quickly and automatically detect worker vital monitoring and dangerous behavior with a camera.

     

    Precautions for introducing AI


    The introduction of AI can be said to have many advantages, but it is not without its disadvantages. Here are two things to keep in mind when considering the introduction of AI.

    There are industries where costs cannot be reduced by AI alone

    While the introduction of AI by companies is progressing, there are some industries where mechanization is difficult. For example, it is difficult to fully automate creative jobs where sensitivity is important, the medical industry where reliability is important, and sales positions. When considering the introduction of AI, it is necessary to carefully consider what kind of department or business the company is likely to use.

    Requires knowledge of risk management

    By introducing and operating AI, various problems such as errors and biases in input data, consideration for privacy, and lack of operational know-how can occur. One of the concerns is the blackboxing of AI, which makes it impossible to visualize what kind of information is accumulated. Companies are required to educate AI personnel who can handle these risks.

     

    6 examples of cost reduction by AI


    The shortcut is to learn from the cases of other companies about specific ways to utilize AI. Here are six examples of successful cost reductions by AI.

    1. Inquiry department

    Many companies, including EC sites and manufacturers, have introduced AI chatbots that can respond to inquiries from users. This not only reduces human costs, but also enables uniform, highly accurate and speedy response. As a result, user satisfaction will improve. In addition, it is effective to improve employee satisfaction by utilizing it internally.

    2. Sales department

    By introducing AI into sales activities, it is possible to extract customers who are likely to make a contract with high probability from data such as customer attributes and purchase history. Personnel who have been doing sales activities randomly can switch to efficient sales activities by referring to the recommended recommendations. By leaving the selection of the approach destination to AI, the person in charge can concentrate on the closing work that is important when the contract is concluded.

    3. Human resources department

    AI can evenly arrange staffing and evaluation, which have tended to be subjective. Employees will be able to quantitatively evaluate their satisfaction with their work, and can expect to maintain their motivation. Furthermore, by having AI set objective recruitment standards, it will be possible to improve the efficiency of recruitment activities, which will lead to a decrease in turnover rate and prevention of mismatches.

    4. Logistics department

    In the logistics industry, the efficiency of operations by AI has been remarkably improved, and the automation of processes such as warehousing / delivery work, order processing, inspection work, and baggage sorting is progressing. In addition, it is possible to optimize the delivery route of the driver, automate the vehicle allocation plan, and detect dangerous driving, which is useful for improving safety as well as improving work efficiency.

    5. Maintenance / maintenance department

    AI plays a major role in the maintenance department from the perspective of accident prevention. It is possible to automate inspection work, which used to require visual confirmation, improve inspection efficiency and improve safety of buildings and equipment that are difficult to inspect. It can contribute not only to accident prevention but also to ensuring the safety of workers, and it is expected that the introduction of AI will accelerate in the future.

    6. Factory department

    It is said that quality tends to differ depending on the career of the worker at the production site of the factory. However, even so, a certain level of quality is guaranteed by AI learning veteran techniques and building a system to support inexperienced workers. It is also effective in ensuring safety in the factory and preventing accidents.

     

    Problems and solutions when introducing AI


    Here, we will introduce the problems and solutions that often occur when thinking about cost reduction by AI.

    What should I start with when introducing AI?

    As explained at the beginning, the introduction of AI can be expected to increase sales and reduce costs.

    In order to realize the effect, first clarify your company’s needs and compare the corresponding software. There are various plans, but I think the best is to start with the basic one, verify the effect, and gradually expand. By accumulating know-how, you can reduce mistakes.

    How to choose with an emphasis on cost reduction?

    If you focus on cost reduction by AI, clarify your needs and then estimate the budget for the expected economic effect. Start experimentally on a small scale first so that you can keep costs down even a little.

    What are the expected AI fields in the future?

    As explained so far, AI has some tasks that can be mechanized and some that cannot. Consider the scope of leaving it to AI and consider introducing it. Future AI trends include the fields of image recognition, natural language processing, and speech recognition. These areas are attracting attention as more useful functions are expected to be developed in the future.

     

    “UMWELT” is recommended for cost reduction by AI tools

    If you want to reduce the introduction cost and reduce your own cost, we recommend using “UMWELT” provided by TRYETING. UMWELT has a lower usage fee level than similar AI systems, and it is relatively easy for small businesses to introduce it.

    It is possible to reduce the number of hours required for work x unit hourly wage, and you can expect an improvement in business productivity when viewed in total.

    No-code AI

    Click here for details

    First of all, easy

    Free consultation

    Summary

    This time, we introduced the key points and examples of cost reduction in our company when introducing AI. If cost reductions are achieved, they should be recognized as useful within the company. If you want to reduce the introduction cost and reduce the total cost, please consider UMWELT.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy

  • How to succeed in cost reduction by AI? Detailed explanation along with examples!

    How to succeed in cost reduction by AI? Detailed explanation along with examples!

     

    When introducing AI, I think there are many people who want to streamline operations and reduce costs such as labor costs and development costs. There are several tips for successful cost savings with AI. In this article, we will introduce the points and examples of cost reduction by AI.

     

    How to choose AI for cost reduction?


    When introducing AI, many companies set sales increase and cost reduction as improvement goals. I will explain the points to pay attention to when considering AI.

    Select by utilization technology

    Image recognition / image analysis

    Technology for image recognition / image analysis that uses AI to identify what is reflected in an image or video and whether there are any abnormalities on the computer. At the site, there is no need for discriminating by skilled workers and training for new employees, which leads to reduction of labor costs.

    Demand Forecast Demand forecast

    that forecasts purchase volume and sales volume based on past data. It is now an indispensable part of marketing strategy. The greater the fluctuations in the products handled and the balance between supply and demand, the longer it takes to analyze, so AI intervention can significantly reduce work time.

    Data analysis Data analysis using

    AI makes it possible to discover new patterns and acquire knowledge (data mining) from a huge amount of data. In addition to reducing the cost of data analysis, the information obtained can be used for marketing.

    Optimization

    Optimization technology uses data analyzed by AI to derive how to achieve goals under limited conditions. Delivery routes in logistics companies and supply chains in the manufacturing industry can also be streamlined with this optimization technology.

    Building
    a data infrastructure By building a data infrastructure, you can collect, store, analyze, and visualize the accumulated data by integrating it. By consolidating them into one, you can shorten the time required for data analysis and utilization.

    Choose according to your company’s needs

    Business efficiency improvement
    The first keyword that comes to mind when reducing costs is business efficiency improvement. You can expect to improve work efficiency by having AI take over data entry work such as invoices and customer information management.

    Cost reduction

    By entrusting simple work and routine work to AI, it is possible to carry out work with a small number of people, and it is possible to reduce costs such as labor costs.

    Defective product detection and failure prediction
    AI, which has defective product detection and failure prediction functions, is a very useful function for manufacturers, and enables highly reliable work in a short time.

    Maximizing Profit

    If the introduction of AI streamlines operations and reduces labor costs and wasteful operations, it will ultimately be possible to significantly increase profits.

    Service development

    By incorporating AI into services, the range of service development can be expanded and service development with a higher degree of freedom can be performed.

    Decision-making power

    Demand forecasting and data analysis make it easier to determine the direction of new products and services to be developed. Explaining the results of AI will make it easier to obtain internal consent and improve decision-making ability.

    In an external environment where promotion of human resources development
    DX is indispensable, introducing AI in-house will lead to the development of AI human resources in the future.

    Benefits of introducing AI


    Now that we’ve explained how to choose AI, let’s talk about the benefits of introducing AI. If you are worried about the balance between the cost of introducing AI and the improvement of business efficiency, please refer to it.

    Reduction of labor costs and elimination of labor shortages

    With the introduction of AI, machines will automatically process simple tasks and routine tasks that were previously personalized. As a result, even a small number of people will be able to carry out internal operations without delay, and will be the savior of companies with serious labor shortages.

    In addition, mechanization leads to shorter working hours and reduces overtime and holiday allowances. There is an initial cost to introduce AI, but in the long run, it will reduce labor costs and reduce the cost of the entire company.

    Business efficiency and productivity improvement

    AI is by no means all-purpose, and there are some tasks that require human intervention within the company. By leaving the work that can be mechanized to AI, you will have more room and you will be able to concentrate more on the work that is personalized. As a result, work efficiency will be improved and productivity can be expected to improve.

    Data analysis / analysis prediction

    Accumulating and verifying a huge amount of customer data is a very time-consuming task. If it is AI, it can capture and analyze data in a short time. By deepening the depth of deep learning, more accurate analysis prediction can be realized. In addition, data analysis is effective not only when developing products and services, but also as a measure for human resource education and operational efficiency improvement.

    Improved safety

    Automation of safety management work at dangerous work sites leads to ensuring the safety of employees. Specifically, it is possible to quickly and automatically detect worker vital monitoring and dangerous behavior with a camera.

     

    Precautions for introducing AI


    The introduction of AI can be said to have many advantages, but it is not without its disadvantages. Here are two things to keep in mind when considering the introduction of AI.

    There are industries where costs cannot be reduced by AI alone

    While the introduction of AI by companies is progressing, there are some industries where mechanization is difficult. For example, it is difficult to fully automate creative jobs where sensitivity is important, the medical industry where reliability is important, and sales positions. When considering the introduction of AI, it is necessary to carefully consider what kind of department or business the company is likely to use.

    Requires knowledge of risk management

    By introducing and operating AI, various problems such as errors and biases in input data, consideration for privacy, and lack of operational know-how can occur. One of the concerns is the blackboxing of AI, which makes it impossible to visualize what kind of information is accumulated. Companies are required to educate AI personnel who can handle these risks.

     

    6 examples of cost reduction by AI


    The shortcut is to learn from the cases of other companies about specific ways to utilize AI. Here are six examples of successful cost reductions by AI.

    1. Inquiry department

    Many companies, including EC sites and manufacturers, have introduced AI chatbots that can respond to inquiries from users. This not only reduces human costs, but also enables uniform, highly accurate and speedy response. As a result, user satisfaction will improve. In addition, it is effective to improve employee satisfaction by utilizing it internally.

    2. Sales department

    By introducing AI into sales activities, it is possible to extract customers who are likely to make a contract with high probability from data such as customer attributes and purchase history. Personnel who have been doing sales activities randomly can switch to efficient sales activities by referring to the recommended recommendations. By leaving the selection of the approach destination to AI, the person in charge can concentrate on the closing work that is important when the contract is concluded.

    3. Human resources department

    AI can evenly arrange staffing and evaluation, which have tended to be subjective. Employees will be able to quantitatively evaluate their satisfaction with their work, and can expect to maintain their motivation. Furthermore, by having AI set objective recruitment standards, it will be possible to improve the efficiency of recruitment activities, which will lead to a decrease in turnover rate and prevention of mismatches.

    4. Logistics department

    In the logistics industry, the efficiency of operations by AI has been remarkably improved, and the automation of processes such as warehousing / delivery work, order processing, inspection work, and baggage sorting is progressing. In addition, it is possible to optimize the delivery route of the driver, automate the vehicle allocation plan, and detect dangerous driving, which is useful for improving safety as well as improving work efficiency.

    5. Maintenance / maintenance department

    AI plays a major role in the maintenance department from the perspective of accident prevention. It is possible to automate inspection work, which used to require visual confirmation, improve inspection efficiency and improve safety of buildings and equipment that are difficult to inspect. It can contribute not only to accident prevention but also to ensuring the safety of workers, and it is expected that the introduction of AI will accelerate in the future.

    6. Factory department

    It is said that quality tends to differ depending on the career of the worker at the production site of the factory. However, even so, a certain level of quality is guaranteed by AI learning veteran techniques and building a system to support inexperienced workers. It is also effective in ensuring safety in the factory and preventing accidents.

     

    Problems and solutions when introducing AI


    Here, we will introduce the problems and solutions that often occur when thinking about cost reduction by AI.

    What should I start with when introducing AI?

    As explained at the beginning, the introduction of AI can be expected to increase sales and reduce costs.

    In order to realize the effect, first clarify your company’s needs and compare the corresponding software. There are various plans, but I think the best is to start with the basic one, verify the effect, and gradually expand. By accumulating know-how, you can reduce mistakes.

    How to choose with an emphasis on cost reduction?

    If you focus on cost reduction by AI, clarify your needs and then estimate the budget for the expected economic effect. Start experimentally on a small scale first so that you can keep costs down even a little.

    What are the expected AI fields in the future?

    As explained so far, AI has some tasks that can be mechanized and some that cannot. Consider the scope of leaving it to AI and consider introducing it. Future AI trends include the fields of image recognition, natural language processing, and speech recognition. These areas are attracting attention as more useful functions are expected to be developed in the future.

     

    “UMWELT” is recommended for cost reduction by AI tools

    If you want to reduce the introduction cost and reduce your own cost, we recommend using “UMWELT” provided by TRYETING. UMWELT has a lower usage fee level than similar AI systems, and it is relatively easy for small businesses to introduce it.

    It is possible to reduce the number of hours required for work x unit hourly wage, and you can expect an improvement in business productivity when viewed in total.

    No-code AI

    Click here for details

    First of all, easy

    Free consultation

    Summary

    This time, we introduced the key points and examples of cost reduction in our company when introducing AI. If cost reductions are achieved, they should be recognized as useful within the company. If you want to reduce the introduction cost and reduce the total cost, please consider UMWELT.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy

  • What are the challenges of data mining? Introducing the points of utilizing AI tools

    What are the challenges of data mining? Introducing the points of utilizing AI tools

    With the development of AI, there are more opportunities to hear the keyword “data mining”. It is expected to be used for smooth management and sales, and I think some companies are considering introducing it. Although there are challenges in data mining, there are many parts that can be solved by introducing tools using AI. This article explains from the basic knowledge of data mining to the challenges in implementation.

     

    What is data mining?

    Meaning of data mining

    Data mining is the search for useful information, patterns, and causal relationships from a large amount of data by making full use of data analysis techniques such as statistics and machine learning.
    It is called this because it uses big data to mine useful information. Today, it has become an indispensable analytical tool for utilizing big data in marketing and sales.

    History of data mining

    The origin of data mining is “Knowledge Discovery in Databases”, a process that seeks to find knowledge from databases that appeared around 1989. After that, data mining continued to develop as the performance of computers improved and a large amount of data could be stored. In the 2000s, it became possible for ordinary households to always connect to the Internet, and the amount of data stored on the Internet increased at an accelerating rate. Currently, various companies, mainly IT companies, are developing and introducing data mining systems as a method for analyzing data.

    Types of data mining and information that can be extracted


    There are two types of data mining and four types of information that can be extracted.

    Types of data mining

    Data mining can be divided into two types: “knowledge discovery” and “hypothesis testing”.

    “Knowledge discovery”: From the collected data, we automatically search for knowledge such as new patterns and rules that are useful to the company. The feature is that no hypothesis is prepared in advance. It is an effective means for big data, and machine learning is often used.

    “Hypothesis verification”: We collect necessary data and analyze whether the hypothesis made in advance is correct according to the problem or event to be verified.

    What can be extracted by data mining

    The profits obtained by performing data mining can be organized into four categories: “data”, “information”, “knowledge”, and “wisdom”.

    Data: Numerical values ​​that have not been organized or classified, or unstructured character strings
    Information: Those that have been organized or classified for “data”
    Knowledge: Trends and knowledge that can be obtained from “information”
    Wisdom: “Knowledge” The power of human judgment using

    Challenges in data mining

    There are no professional employees

    The existence of data scientists who are familiar with data analysis is indispensable for data mining, which targets a huge amount of data and has specialized analysis methods. However, there may be times when your company does not have staff with such expertise. Even if we hire people, it is difficult to find human resources with specialized knowledge because the annual income is high and the absolute number is small.

    Data utilization does not work

    It’s a common story for data mining companies to have data but not use it well because of the sheer volume. .. If you can’t analyze it, you just collect data, and you don’t get the results you want with data mining, there is a possibility that data utilization itself will be hindered.

    Data analysis is time consuming and costly

    When you actually start data mining, you will spend a lot of time and effort on data acquisition and analysis. In some cases, the burden on the site will increase and labor costs will be extra.

    Challenges when not doing data mining


    In an era when big data utilization is being called for, it is essential to efficiently obtain information to advance business in an advantageous manner. Leveraging data mining can lead to the discovery of business tips and challenges that previously buried humans may not be aware of. From here, I will explain what happens when you do not do data mining and possible problems.

    Know-how is not accumulated

    Within the company, the know-how possessed by each employee is not shared, and there are tasks that are personalized. Unless we analyze text data that contains a lot of useful information such as daily business reports and work reports, know-how will not be accumulated and it may lead to overlooking issues and problems in internal operations. ..

    Customer data cannot be analyzed

    Understanding customer needs is important in marketing. By analyzing customer data, it is possible to develop products that encourage customers’ purchasing motivation and effectively promote them. Inadequate analysis may not be sufficient to address issues such as customer churn, lack of repeaters, and inability to increase customer satisfaction.

    Sales are sluggish

    If you can’t analyze customer data and purchasing data, you can’t come up with measures that will lead to sales. For example, if data mining can find products that can be sold at the same time, which was previously unknown, it is possible to promote migration within the store by bringing the sales floors of the products closer together or intentionally arranging them far away. By discounting only one of them and selling the other at the regular price without discounting, it may lead to an increase in sales. The optimal approach may not be possible due to the lack of analysis of purchasing information, which may affect sales.

    Data mining issues can be solved by introducing AI tools


    The advantages of introducing a data mining tool are as follows.

    • Anyone can analyze, no specialized staff required
    • There is a hint to find a problem from a huge amount of data
    • Reduced time and effort spent on analysis
    • Accumulation of business know-how is possible
    • Detailed customer analysis and sales analysis are hints for improving business performance

    Points for utilizing data mining tools


    Currently, various tools for data mining have been released. There are three points to keep in mind when choosing a tool.

    • Carefully select the data used for analysis
    • Clarify the purpose of introducing the tool
    • When installing for the first time, choose one that is easy to operate

    Carefully select the data used for analysis

    You don’t just have to have a lot of data. Data mining can be used even if the amount of data is small. If there is too much data, it will be difficult to extract only the necessary information, so it is important to carefully consider what information you want before selecting and reading the data.

    Clarify the purpose of introducing the tool

    It is necessary to clarify the “purpose” of what to do when introducing data mining. For example, if you want to improve the efficiency of your business and if you want to increase the purchase rate of products, the data you need and the tools you should choose will differ depending on your purpose.

    When installing for the first time, choose one that is easy to operate

    It is important to add the user interface (UI) -related parts such as data handling and expression method to the judgment criteria when choosing a tool. The point is that the operation is not too complicated so that anyone can check the extracted information.

    Data mining issues are solved with TRYETING’s AI tool “UMWELT”!

    There is great potential for utilizing data mining tools. With the no-code AI cloud “UMWELT” provided by TRYING, you can expect the introduction effect because you can use the AI ​​engine that has already been proven. Since “UMWELT” is always equipped with more than 100 algorithms, we can build a highly accurate data mining system using AI in a short period of time. It will be a ready-to-use tool for companies that want to introduce it immediately.

    Summary

    In this article, I explained the challenges of data mining. By introducing a data mining tool, you can save the trouble of analyzing customer/sales data and realize smooth management/sales. Please use data mining for your business.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy

  • What are the challenges of data mining? Introducing the points of utilizing AI tools

    What are the challenges of data mining? Introducing the points of utilizing AI tools

    With the development of AI, there are more opportunities to hear the keyword “data mining”. It is expected to be used for smooth management and sales, and I think some companies are considering introducing it. Although there are challenges in data mining, there are many parts that can be solved by introducing tools using AI. This article explains from the basic knowledge of data mining to the challenges in implementation.

     

    What is data mining?

    Meaning of data mining

    Data mining is the search for useful information, patterns, and causal relationships from a large amount of data by making full use of data analysis techniques such as statistics and machine learning.
    It is called this because it uses big data to mine useful information. Today, it has become an indispensable analytical tool for utilizing big data in marketing and sales.

    History of data mining

    The origin of data mining is “Knowledge Discovery in Databases”, a process that seeks to find knowledge from databases that appeared around 1989. After that, data mining continued to develop as the performance of computers improved and a large amount of data could be stored. In the 2000s, it became possible for ordinary households to always connect to the Internet, and the amount of data stored on the Internet increased at an accelerating rate. Currently, various companies, mainly IT companies, are developing and introducing data mining systems as a method for analyzing data.

    Types of data mining and information that can be extracted


    There are two types of data mining and four types of information that can be extracted.

    Types of data mining

    Data mining can be divided into two types: “knowledge discovery” and “hypothesis testing”.

    “Knowledge discovery”: From the collected data, we automatically search for knowledge such as new patterns and rules that are useful to the company. The feature is that no hypothesis is prepared in advance. It is an effective means for big data, and machine learning is often used.

    “Hypothesis verification”: We collect necessary data and analyze whether the hypothesis made in advance is correct according to the problem or event to be verified.

    What can be extracted by data mining

    The profits obtained by performing data mining can be organized into four categories: “data”, “information”, “knowledge”, and “wisdom”.

    Data: Numerical values ​​that have not been organized or classified, or unstructured character strings
    Information: Those that have been organized or classified for “data”
    Knowledge: Trends and knowledge that can be obtained from “information”
    Wisdom: “Knowledge” The power of human judgment using

    Challenges in data mining

    There are no professional employees

    The existence of data scientists who are familiar with data analysis is indispensable for data mining, which targets a huge amount of data and has specialized analysis methods. However, there may be times when your company does not have staff with such expertise. Even if we hire people, it is difficult to find human resources with specialized knowledge because the annual income is high and the absolute number is small.

    Data utilization does not work

    It’s a common story for data mining companies to have data but not use it well because of the sheer volume. .. If you can’t analyze it, you just collect data, and you don’t get the results you want with data mining, there is a possibility that data utilization itself will be hindered.

    Data analysis is time consuming and costly

    When you actually start data mining, you will spend a lot of time and effort on data acquisition and analysis. In some cases, the burden on the site will increase and labor costs will be extra.

    Challenges when not doing data mining


    In an era when big data utilization is being called for, it is essential to efficiently obtain information to advance business in an advantageous manner. Leveraging data mining can lead to the discovery of business tips and challenges that previously buried humans may not be aware of. From here, I will explain what happens when you do not do data mining and possible problems.

    Know-how is not accumulated

    Within the company, the know-how possessed by each employee is not shared, and there are tasks that are personalized. Unless we analyze text data that contains a lot of useful information such as daily business reports and work reports, know-how will not be accumulated and it may lead to overlooking issues and problems in internal operations. ..

    Customer data cannot be analyzed

    Understanding customer needs is important in marketing. By analyzing customer data, it is possible to develop products that encourage customers’ purchasing motivation and effectively promote them. Inadequate analysis may not be sufficient to address issues such as customer churn, lack of repeaters, and inability to increase customer satisfaction.

    Sales are sluggish

    If you can’t analyze customer data and purchasing data, you can’t come up with measures that will lead to sales. For example, if data mining can find products that can be sold at the same time, which was previously unknown, it is possible to promote migration within the store by bringing the sales floors of the products closer together or intentionally arranging them far away. By discounting only one of them and selling the other at the regular price without discounting, it may lead to an increase in sales. The optimal approach may not be possible due to the lack of analysis of purchasing information, which may affect sales.

    Data mining issues can be solved by introducing AI tools


    The advantages of introducing a data mining tool are as follows.

    • Anyone can analyze, no specialized staff required
    • There is a hint to find a problem from a huge amount of data
    • Reduced time and effort spent on analysis
    • Accumulation of business know-how is possible
    • Detailed customer analysis and sales analysis are hints for improving business performance

    Points for utilizing data mining tools


    Currently, various tools for data mining have been released. There are three points to keep in mind when choosing a tool.

    • Carefully select the data used for analysis
    • Clarify the purpose of introducing the tool
    • When installing for the first time, choose one that is easy to operate

    Carefully select the data used for analysis

    You don’t just have to have a lot of data. Data mining can be used even if the amount of data is small. If there is too much data, it will be difficult to extract only the necessary information, so it is important to carefully consider what information you want before selecting and reading the data.

    Clarify the purpose of introducing the tool

    It is necessary to clarify the “purpose” of what to do when introducing data mining. For example, if you want to improve the efficiency of your business and if you want to increase the purchase rate of products, the data you need and the tools you should choose will differ depending on your purpose.

    When installing for the first time, choose one that is easy to operate

    It is important to add the user interface (UI) -related parts such as data handling and expression method to the judgment criteria when choosing a tool. The point is that the operation is not too complicated so that anyone can check the extracted information.

    Data mining issues are solved with TRYETING’s AI tool “UMWELT”!

    There is great potential for utilizing data mining tools. With the no-code AI cloud “UMWELT” provided by TRYING, you can expect the introduction effect because you can use the AI ​​engine that has already been proven. Since “UMWELT” is always equipped with more than 100 algorithms, we can build a highly accurate data mining system using AI in a short period of time. It will be a ready-to-use tool for companies that want to introduce it immediately.

    Summary

    In this article, I explained the challenges of data mining. By introducing a data mining tool, you can save the trouble of analyzing customer/sales data and realize smooth management/sales. Please use data mining for your business.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy

  • What are the challenges of data mining? Introducing the points of utilizing AI tools

    What are the challenges of data mining? Introducing the points of utilizing AI tools

    With the development of AI, there are more opportunities to hear the keyword “data mining”. It is expected to be used for smooth management and sales, and I think some companies are considering introducing it. Although there are challenges in data mining, there are many parts that can be solved by introducing tools using AI. This article explains from the basic knowledge of data mining to the challenges in implementation.

     

    What is data mining?

    Meaning of data mining

    Data mining is the search for useful information, patterns, and causal relationships from a large amount of data by making full use of data analysis techniques such as statistics and machine learning.
    It is called this because it uses big data to mine useful information. Today, it has become an indispensable analytical tool for utilizing big data in marketing and sales.

    History of data mining

    The origin of data mining is “Knowledge Discovery in Databases”, a process that seeks to find knowledge from databases that appeared around 1989. After that, data mining continued to develop as the performance of computers improved and a large amount of data could be stored. In the 2000s, it became possible for ordinary households to always connect to the Internet, and the amount of data stored on the Internet increased at an accelerating rate. Currently, various companies, mainly IT companies, are developing and introducing data mining systems as a method for analyzing data.

    Types of data mining and information that can be extracted


    There are two types of data mining and four types of information that can be extracted.

    Types of data mining

    Data mining can be divided into two types: “knowledge discovery” and “hypothesis testing”.

    “Knowledge discovery”: From the collected data, we automatically search for knowledge such as new patterns and rules that are useful to the company. The feature is that no hypothesis is prepared in advance. It is an effective means for big data, and machine learning is often used.

    “Hypothesis verification”: We collect necessary data and analyze whether the hypothesis made in advance is correct according to the problem or event to be verified.

    What can be extracted by data mining

    The profits obtained by performing data mining can be organized into four categories: “data”, “information”, “knowledge”, and “wisdom”.

    Data: Numerical values ​​that have not been organized or classified, or unstructured character strings
    Information: Those that have been organized or classified for “data”
    Knowledge: Trends and knowledge that can be obtained from “information”
    Wisdom: “Knowledge” The power of human judgment using

    Challenges in data mining

    There are no professional employees

    The existence of data scientists who are familiar with data analysis is indispensable for data mining, which targets a huge amount of data and has specialized analysis methods. However, there may be times when your company does not have staff with such expertise. Even if we hire people, it is difficult to find human resources with specialized knowledge because the annual income is high and the absolute number is small.

    Data utilization does not work

    It’s a common story for data mining companies to have data but not use it well because of the sheer volume. .. If you can’t analyze it, you just collect data, and you don’t get the results you want with data mining, there is a possibility that data utilization itself will be hindered.

    Data analysis is time consuming and costly

    When you actually start data mining, you will spend a lot of time and effort on data acquisition and analysis. In some cases, the burden on the site will increase and labor costs will be extra.

    Challenges when not doing data mining


    In an era when big data utilization is being called for, it is essential to efficiently obtain information to advance business in an advantageous manner. Leveraging data mining can lead to the discovery of business tips and challenges that previously buried humans may not be aware of. From here, I will explain what happens when you do not do data mining and possible problems.

    Know-how is not accumulated

    Within the company, the know-how possessed by each employee is not shared, and there are tasks that are personalized. Unless we analyze text data that contains a lot of useful information such as daily business reports and work reports, know-how will not be accumulated and it may lead to overlooking issues and problems in internal operations. ..

    Customer data cannot be analyzed

    Understanding customer needs is important in marketing. By analyzing customer data, it is possible to develop products that encourage customers’ purchasing motivation and effectively promote them. Inadequate analysis may not be sufficient to address issues such as customer churn, lack of repeaters, and inability to increase customer satisfaction.

    Sales are sluggish

    If you can’t analyze customer data and purchasing data, you can’t come up with measures that will lead to sales. For example, if data mining can find products that can be sold at the same time, which was previously unknown, it is possible to promote migration within the store by bringing the sales floors of the products closer together or intentionally arranging them far away. By discounting only one of them and selling the other at the regular price without discounting, it may lead to an increase in sales. The optimal approach may not be possible due to the lack of analysis of purchasing information, which may affect sales.

    Data mining issues can be solved by introducing AI tools


    The advantages of introducing a data mining tool are as follows.

    • Anyone can analyze, no specialized staff required
    • There is a hint to find a problem from a huge amount of data
    • Reduced time and effort spent on analysis
    • Accumulation of business know-how is possible
    • Detailed customer analysis and sales analysis are hints for improving business performance

    Points for utilizing data mining tools


    Currently, various tools for data mining have been released. There are three points to keep in mind when choosing a tool.

    • Carefully select the data used for analysis
    • Clarify the purpose of introducing the tool
    • When installing for the first time, choose one that is easy to operate

    Carefully select the data used for analysis

    You don’t just have to have a lot of data. Data mining can be used even if the amount of data is small. If there is too much data, it will be difficult to extract only the necessary information, so it is important to carefully consider what information you want before selecting and reading the data.

    Clarify the purpose of introducing the tool

    It is necessary to clarify the “purpose” of what to do when introducing data mining. For example, if you want to improve the efficiency of your business and if you want to increase the purchase rate of products, the data you need and the tools you should choose will differ depending on your purpose.

    When installing for the first time, choose one that is easy to operate

    It is important to add the user interface (UI) -related parts such as data handling and expression method to the judgment criteria when choosing a tool. The point is that the operation is not too complicated so that anyone can check the extracted information.

    Data mining issues are solved with TRYETING’s AI tool “UMWELT”!

    There is great potential for utilizing data mining tools. With the no-code AI cloud “UMWELT” provided by TRYING, you can expect the introduction effect because you can use the AI ​​engine that has already been proven. Since “UMWELT” is always equipped with more than 100 algorithms, we can build a highly accurate data mining system using AI in a short period of time. It will be a ready-to-use tool for companies that want to introduce it immediately.

    Summary

    In this article, I explained the challenges of data mining. By introducing a data mining tool, you can save the trouble of analyzing customer/sales data and realize smooth management/sales. Please use data mining for your business.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy

  • What is the impact of AI on the media? Introducing examples of media AI utilization

    What is the impact of AI on the media? Introducing examples of media AI utilization

     

    AI has come to be used in all our daily lives and business scenes. The wave of AI innovation is also coming to the media and content industries. In this article, we will focus on “the impact of AI on the media” and explain its outline and application examples.

    The influence of AI is spreading to the media

     

     

    What is AI?

    AI (Artificial Intelligence) is an artificial reproduction of a part of human intelligence using software. Humans make many guesses and judgments in their lives, but AI can automatically extract and judge features such as patterns and rules for making judgments.

    Impact of AI on the media

    In the days when there was only TV, there was competition for ratings. However, with the advent of YouTube, Netflix, and smartphones, the range of content choices for viewers has greatly expanded. Even in such video distribution services, AI that proposes works that match the tastes of each viewer is used.

    Mediaization of physical stores is also progressing

    Real spaces such as physical stores are also becoming media. For example, the case of purchasing an actual product online after seeing it in a store is a typical example of mediaization. With AI, it will be possible to understand the purchasing consciousness and consumption behavior of users, which will be useful for digitizing real stores.

     

    Types of media that are greatly affected by AI

     


    There are three main types of media that are greatly affected by AI. Here, we will explain the outline and features of each medium.

     

    Mass media

    Mass media is a “mass-oriented” communication medium that sends information to an unspecified number of people, such as tens of thousands to tens of millions of people. The mass media plays multiple roles such as news, commentary / enlightenment, education, entertainment, and advertising, and is also known for its great social influence.

    Web media

    Web media are websites that send out some information on the Internet. It can be accessed not only on a personal computer but also on a terminal that can connect to the Internet, such as a smartphone or tablet.

    Social media

    Social media refers to media that includes social elements such as information dissemination by individuals and connections between individuals. In receiving information, it can also be a sender of information at the same time, and it is a major feature of social media that “diffusion” occurs due to interaction.

    Media AI utilization cases

     


    Vendors serving media agencies are also stepping up their efforts to leverage AI. Here, we will introduce six examples of media AI utilization.

     

    Real-time Japanese conversion system

    The real-time Japanese conversion system is an AI technology mainly used in mass media. TV Asahi, which covers the Kanto area as a broadcast target area, uses AI-OCR to display telops of athlete names in overseas sports broadcasts, and realizes automatic Japanese conversion in real time.

    Fully automatic real-time subtitles

    Internet TV “AbemaTV” uses AI voice recognition technology to develop live broadcast programs that display AI subtitles in real time. In addition, the subtitles sent by broadcasting are saved in the log and can be linked with various recording and broadcasting servers, so in recording and rebroadcasting, subtitles can be sent out with the touch of a button.

    Image recognition

    AI technology is also used for image recognition. A particular topic in image recognition using AI was “SEER” announced by the Facebook research team. SEER is a self-supervised learning technique from unlabeled random image groups on the Internet. It autonomously examines the contents of the dataset and learns in the process, achieving top-level accuracy in tasks such as object detection.

    SNS video collection

    A lot of attention is also being paid to “Newsdeck,” a service that automatically collects images and videos of incidents, accidents, disasters, etc. from the Internet using AI and provides them to the news media with the permission of the poster. Newsdeck collects images and videos related to incidents, accidents, and disasters in real time from various SNS, and AI classifies them into items such as “earthquake,” “traffic accident,” and “fire.” As a result, the labor of the employees in charge of the survey can be reduced, leading to an improvement in labor productivity.

    Recommended engine

    A recommendation engine is software that identifies the right offers, products, and content to website and mobile app users, as well as customers interacting through digital channels, to personalize the customer experience. .. AI technology mainly used in web media is being introduced by major companies such as Amazon and Netflix.

    Chatbot

    A chatbot is a robot program that handles real-time response work on behalf of humans. AI chatbots use AI’s ability to derive correct answers based on past statistical data and provide accurate answers to customer inquiries. In the media industry, Korona-ka has regained attention as a non-contact technology for measures against denseness and leveling of congestion, and the range of utilization has expanded.

    Introducing TRYETING’s AI tools

     

    We recommend the two AI tools developed by TRYETING for corporate personnel who want to utilize AI for internal operations and promote DX conversion. Here, we will introduce the no-code AI tool “UMWELT” and the automatic shift creation AI tool “HRBEST”, the product features of each, and the merits of their introduction.

     

    No-code AI tool “UMWELT”

    The no-code AI tool “UMWELT” is a cloud service that allows you to easily introduce AI without a server by using the existing system as it is. With a large number of proven algorithms, no programming language knowledge or special environment required for AI implementation is required. You can easily build AI just by operating the mouse. In addition, the period until the introduction of AI is 1/4 of the conventional one, and high-speed introduction is realized. Another advantage is that the introduction cost is 1/10 of the conventional cost, which is the lowest level in the industry.

    Shift automatic creation AI tool “HR BEST”

    With the shift creation service “HR BEST” that utilizes AI, it is possible to automatically create the optimum shift by machine learning. Employees submit the “desired date and time of shift” from within the smartphone app, and the shift creator displays the submitted information on the calendar and automatically arranges it. You can also propose “members who are likely to enter the shift” after learning past shift information. The shift table creation work, which was all done manually in the past, can be greatly streamlined.

    Summary

    This time, we have explained the impact of AI on the media, examples of media AI utilization, and recommended AI tools. AI technology is evolving day by day, and will become indispensable for human life and corporate development in various fields in the future. By all means, please refer to this article to deepen your knowledge about AI and use AI for your own business.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy
  • What is the impact of AI on the media? Introducing examples of media AI utilization

    What is the impact of AI on the media? Introducing examples of media AI utilization

     

    AI has come to be used in all our daily lives and business scenes. The wave of AI innovation is also coming to the media and content industries. In this article, we will focus on “the impact of AI on the media” and explain its outline and application examples.

    The influence of AI is spreading to the media

     

     

    What is AI?

    AI (Artificial Intelligence) is an artificial reproduction of a part of human intelligence using software. Humans make many guesses and judgments in their lives, but AI can automatically extract and judge features such as patterns and rules for making judgments.

    Impact of AI on the media

    In the days when there was only TV, there was competition for ratings. However, with the advent of YouTube, Netflix, and smartphones, the range of content choices for viewers has greatly expanded. Even in such video distribution services, AI that proposes works that match the tastes of each viewer is used.

    Mediaization of physical stores is also progressing

    Real spaces such as physical stores are also becoming media. For example, the case of purchasing an actual product online after seeing it in a store is a typical example of mediaization. With AI, it will be possible to understand the purchasing consciousness and consumption behavior of users, which will be useful for digitizing real stores.

     

    Types of media that are greatly affected by AI

     


    There are three main types of media that are greatly affected by AI. Here, we will explain the outline and features of each medium.

     

    Mass media

    Mass media is a “mass-oriented” communication medium that sends information to an unspecified number of people, such as tens of thousands to tens of millions of people. The mass media plays multiple roles such as news, commentary / enlightenment, education, entertainment, and advertising, and is also known for its great social influence.

    Web media

    Web media are websites that send out some information on the Internet. It can be accessed not only on a personal computer but also on a terminal that can connect to the Internet, such as a smartphone or tablet.

    Social media

    Social media refers to media that includes social elements such as information dissemination by individuals and connections between individuals. In receiving information, it can also be a sender of information at the same time, and it is a major feature of social media that “diffusion” occurs due to interaction.

    Media AI utilization cases

     


    Vendors serving media agencies are also stepping up their efforts to leverage AI. Here, we will introduce six examples of media AI utilization.

     

    Real-time Japanese conversion system

    The real-time Japanese conversion system is an AI technology mainly used in mass media. TV Asahi, which covers the Kanto area as a broadcast target area, uses AI-OCR to display telops of athlete names in overseas sports broadcasts, and realizes automatic Japanese conversion in real time.

    Fully automatic real-time subtitles

    Internet TV “AbemaTV” uses AI voice recognition technology to develop live broadcast programs that display AI subtitles in real time. In addition, the subtitles sent by broadcasting are saved in the log and can be linked with various recording and broadcasting servers, so in recording and rebroadcasting, subtitles can be sent out with the touch of a button.

    Image recognition

    AI technology is also used for image recognition. A particular topic in image recognition using AI was “SEER” announced by the Facebook research team. SEER is a self-supervised learning technique from unlabeled random image groups on the Internet. It autonomously examines the contents of the dataset and learns in the process, achieving top-level accuracy in tasks such as object detection.

    SNS video collection

    A lot of attention is also being paid to “Newsdeck,” a service that automatically collects images and videos of incidents, accidents, disasters, etc. from the Internet using AI and provides them to the news media with the permission of the poster. Newsdeck collects images and videos related to incidents, accidents, and disasters in real time from various SNS, and AI classifies them into items such as “earthquake,” “traffic accident,” and “fire.” As a result, the labor of the employees in charge of the survey can be reduced, leading to an improvement in labor productivity.

    Recommended engine

    A recommendation engine is software that identifies the right offers, products, and content to website and mobile app users, as well as customers interacting through digital channels, to personalize the customer experience. .. AI technology mainly used in web media is being introduced by major companies such as Amazon and Netflix.

    Chatbot

    A chatbot is a robot program that handles real-time response work on behalf of humans. AI chatbots use AI’s ability to derive correct answers based on past statistical data and provide accurate answers to customer inquiries. In the media industry, Korona-ka has regained attention as a non-contact technology for measures against denseness and leveling of congestion, and the range of utilization has expanded.

    Introducing TRYETING’s AI tools

     

    We recommend the two AI tools developed by TRYETING for corporate personnel who want to utilize AI for internal operations and promote DX conversion. Here, we will introduce the no-code AI tool “UMWELT” and the automatic shift creation AI tool “HRBEST”, the product features of each, and the merits of their introduction.

     

    No-code AI tool “UMWELT”

    The no-code AI tool “UMWELT” is a cloud service that allows you to easily introduce AI without a server by using the existing system as it is. With a large number of proven algorithms, no programming language knowledge or special environment required for AI implementation is required. You can easily build AI just by operating the mouse. In addition, the period until the introduction of AI is 1/4 of the conventional one, and high-speed introduction is realized. Another advantage is that the introduction cost is 1/10 of the conventional cost, which is the lowest level in the industry.

    Shift automatic creation AI tool “HR BEST”

    With the shift creation service “HR BEST” that utilizes AI, it is possible to automatically create the optimum shift by machine learning. Employees submit the “desired date and time of shift” from within the smartphone app, and the shift creator displays the submitted information on the calendar and automatically arranges it. You can also propose “members who are likely to enter the shift” after learning past shift information. The shift table creation work, which was all done manually in the past, can be greatly streamlined.

    Summary

    This time, we have explained the impact of AI on the media, examples of media AI utilization, and recommended AI tools. AI technology is evolving day by day, and will become indispensable for human life and corporate development in various fields in the future. By all means, please refer to this article to deepen your knowledge about AI and use AI for your own business.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy
  • What is the impact of AI on the media? Introducing examples of media AI utilization

    What is the impact of AI on the media? Introducing examples of media AI utilization

     

    AI has come to be used in all our daily lives and business scenes. The wave of AI innovation is also coming to the media and content industries. In this article, we will focus on “the impact of AI on the media” and explain its outline and application examples.

    The influence of AI is spreading to the media

     

     

    What is AI?

    AI (Artificial Intelligence) is an artificial reproduction of a part of human intelligence using software. Humans make many guesses and judgments in their lives, but AI can automatically extract and judge features such as patterns and rules for making judgments.

    Impact of AI on the media

    In the days when there was only TV, there was competition for ratings. However, with the advent of YouTube, Netflix, and smartphones, the range of content choices for viewers has greatly expanded. Even in such video distribution services, AI that proposes works that match the tastes of each viewer is used.

    Mediaization of physical stores is also progressing

    Real spaces such as physical stores are also becoming media. For example, the case of purchasing an actual product online after seeing it in a store is a typical example of mediaization. With AI, it will be possible to understand the purchasing consciousness and consumption behavior of users, which will be useful for digitizing real stores.

     

    Types of media that are greatly affected by AI

     


    There are three main types of media that are greatly affected by AI. Here, we will explain the outline and features of each medium.

     

    Mass media

    Mass media is a “mass-oriented” communication medium that sends information to an unspecified number of people, such as tens of thousands to tens of millions of people. The mass media plays multiple roles such as news, commentary / enlightenment, education, entertainment, and advertising, and is also known for its great social influence.

    Web media

    Web media are websites that send out some information on the Internet. It can be accessed not only on a personal computer but also on a terminal that can connect to the Internet, such as a smartphone or tablet.

    Social media

    Social media refers to media that includes social elements such as information dissemination by individuals and connections between individuals. In receiving information, it can also be a sender of information at the same time, and it is a major feature of social media that “diffusion” occurs due to interaction.

    Media AI utilization cases

     


    Vendors serving media agencies are also stepping up their efforts to leverage AI. Here, we will introduce six examples of media AI utilization.

     

    Real-time Japanese conversion system

    The real-time Japanese conversion system is an AI technology mainly used in mass media. TV Asahi, which covers the Kanto area as a broadcast target area, uses AI-OCR to display telops of athlete names in overseas sports broadcasts, and realizes automatic Japanese conversion in real time.

    Fully automatic real-time subtitles

    Internet TV “AbemaTV” uses AI voice recognition technology to develop live broadcast programs that display AI subtitles in real time. In addition, the subtitles sent by broadcasting are saved in the log and can be linked with various recording and broadcasting servers, so in recording and rebroadcasting, subtitles can be sent out with the touch of a button.

    Image recognition

    AI technology is also used for image recognition. A particular topic in image recognition using AI was “SEER” announced by the Facebook research team. SEER is a self-supervised learning technique from unlabeled random image groups on the Internet. It autonomously examines the contents of the dataset and learns in the process, achieving top-level accuracy in tasks such as object detection.

    SNS video collection

    A lot of attention is also being paid to “Newsdeck,” a service that automatically collects images and videos of incidents, accidents, disasters, etc. from the Internet using AI and provides them to the news media with the permission of the poster. Newsdeck collects images and videos related to incidents, accidents, and disasters in real time from various SNS, and AI classifies them into items such as “earthquake,” “traffic accident,” and “fire.” As a result, the labor of the employees in charge of the survey can be reduced, leading to an improvement in labor productivity.

    Recommended engine

    A recommendation engine is software that identifies the right offers, products, and content to website and mobile app users, as well as customers interacting through digital channels, to personalize the customer experience. .. AI technology mainly used in web media is being introduced by major companies such as Amazon and Netflix.

    Chatbot

    A chatbot is a robot program that handles real-time response work on behalf of humans. AI chatbots use AI’s ability to derive correct answers based on past statistical data and provide accurate answers to customer inquiries. In the media industry, Korona-ka has regained attention as a non-contact technology for measures against denseness and leveling of congestion, and the range of utilization has expanded.

    Introducing TRYETING’s AI tools

     

    We recommend the two AI tools developed by TRYETING for corporate personnel who want to utilize AI for internal operations and promote DX conversion. Here, we will introduce the no-code AI tool “UMWELT” and the automatic shift creation AI tool “HRBEST”, the product features of each, and the merits of their introduction.

     

    No-code AI tool “UMWELT”

    The no-code AI tool “UMWELT” is a cloud service that allows you to easily introduce AI without a server by using the existing system as it is. With a large number of proven algorithms, no programming language knowledge or special environment required for AI implementation is required. You can easily build AI just by operating the mouse. In addition, the period until the introduction of AI is 1/4 of the conventional one, and high-speed introduction is realized. Another advantage is that the introduction cost is 1/10 of the conventional cost, which is the lowest level in the industry.

    Shift automatic creation AI tool “HR BEST”

    With the shift creation service “HR BEST” that utilizes AI, it is possible to automatically create the optimum shift by machine learning. Employees submit the “desired date and time of shift” from within the smartphone app, and the shift creator displays the submitted information on the calendar and automatically arranges it. You can also propose “members who are likely to enter the shift” after learning past shift information. The shift table creation work, which was all done manually in the past, can be greatly streamlined.

    Summary

    This time, we have explained the impact of AI on the media, examples of media AI utilization, and recommended AI tools. AI technology is evolving day by day, and will become indispensable for human life and corporate development in various fields in the future. By all means, please refer to this article to deepen your knowledge about AI and use AI for your own business.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy