Category: Automation

  • 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.

     

    Follow us on Facebook for updates and exclusive content! Click here: Maga AI

  • TOP 5 World Changing Technologies 2024

    TOP 5 World Changing Technologies 2024

    we’re living in an age where technology isn’t just about smartphones and gadgets anymore it’s about creating solutions that have the potential to shape the destiny of our planet the year 2024 will be like no other and by the end of this video you will probably be surprised about what will take place soon so let’s begin 

    Artificial Intelligence

    World changing technology we’re diving into is already changing our lives in both good and negative ways it’s none other than artificial intelligence or Ai and its intrinsic part machine learning gone are the days when AI was confined to the realm of sci-fi today organizations and researchers across the globe are harnessing their data mountains and unleashing immense computing power to bring Advanced AI capabilities into our day-to-day lives a key Trend in AI That’s Turning Heads is computer vision imagine computers that don’t just compute but see and recognize objects in a video or a photograph that’s not all language processing has come a long way too making it impossible for machines to comprehend our voices and respond back almost like having a conversation with a human and here’s a cherry on top the rise of low code or no code this trend is set to revolutionize AI accessibility this year what does it mean simply put you’ll be able to construct your own AI using intuitive drag and drop graphical interface says this means anyone can develop extraordinary applications without the need to become a coding Guru so prepare to Bitter due to those daunting lines of code and embrace the future of AI development  next up 

     

    Quantum Computing

    We’re stepping into the realm of quantum Computing this isn’t just an incremental change it’s a whole new paradigm Quantum Computing represents a giant leap processing information using special Quantum States this allows machines to handle information in a radically different way than traditional computers imagine a computing power a trillion times greater than today’s most advanced supercomputers sounds mind-boggling doesn’t it by 2024 we predict quantum computers could redefine how we tackle complex problems from optimizing Logistics and managing portfolios more effectively to innovating pharmaceutical Solutions faster than ever before the impact of quantum Computing is said to be phenomenal this isn’t just an upgrade it’s a leap towards a new age of problem solving It seems like just yesterday when AR was a novel concept fast forward to now and we have robust AR capabilities in the palm of our hands especially on our phones and tablets alongside this there’s an ever increasing momentum

     

     Augmented reality or VR

    Towards virtual reality or VR 2024 promises to be a game-changing year for VR we expect to see lighter more portable devices that shed the weight and constraints of the bulky headsets of your instead think of glasses like devices that seamlessly connect to your phone providing Superior VR experiences wherever you may be these AR advancements are setting the stage for our immersion into the metaverse a persistent shared virtual world accessible across different devices and platforms so get ready to strap on your VR glasses and step into a reality where the physical and digital worlds beautifully converge

     

    Genomic and Nanotechnology

    As we journey further into our Tech Voyage we come upon the Revolutionary field of genomics in 2020 Emmanuel charpentia and Jennifer a Dudner received the Nobel Prize in chemistry for their groundbreaking work in genome editing fast forward to 2024 and genomics Gene editing and synthetic biology are at the Forefront of technological Trends why because these advancements have the potential to revolutionize our world from modifying crops for better yield and resilience curing and eradicating diseases to developing rapid vaccines like the ones we saw for covert 19. the possibilities seem almost Limitless in tandem with genomics we see nanotechnology reshaping the world of materials by manipulating materials at a subatomic level we’re granting them new improved attributes this year we can look forward to Creations like foldable screens enhanced batteries water repellent and self-cleaning fabrics and even self-healing paint imagine spilling coffee on your shirt and it just slides off or a scratch car that repairs its paint That’s the incredible potential of nanotechnology 

     

    New Energy Solutions

    Finally we arrive at the last but certainly not the least important Trend newer Energy Solutions as our planet grapples with climate change technological innovation becomes more crucial than ever in creating sustainable ways of powering our world in 2024 we’re anticipating significant advances in energy storage technology notably in the batteries at Power are electric vehicles this not only implies at longer ranges for our EVS but also potentially a substantial decrease in charging times but the energy Revolution doesn’t stop there Innovations in nuclear power and the rise of green hydrogen are set to redefine how we power not just our cars but our ships planes and trains and generate power for the public too as we March towards a sustainable future these new energy Trends offer a Beacon of Hope showcasing how technology can help us overcome some of the greatest challenges of our times this is a future  where clean efficient and sustainable energy solutions become the norm not the exception and there you have it folks the top five world-changing Technologies for 2024 we are truly living in an era where today’s science fiction is rapidly transforming into tomorrow’s reality we’re not just Spectators but participants in this technological Revolution now if you enjoyed our journey into the future today  

     

    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 Data Mining and Data Science? Thorough explanation of differences and outline

    What is Data Mining and Data Science? Thorough explanation of differences and outline

     

    In recent years, “big data” has attracted a great deal of attention, and how to utilize big data in corporate activities has become an urgent issue in all industries. Therefore, in this article, we will focus on “data mining” and “data science” related to the handling of data.

     

    About data mining and data science


    First, let’s take a look at the definitions and differences between data mining and data science.

    What is data mining?

    Data mining is a technique for finding “knowledge” in a large amount of data by making full use of analysis methods such as statistics and AI. As the word data mining implies, it means mining useful information (data).

    What is data science?

    Data science is a research field for extracting meaningful data using methods in various fields such as statistics and information engineering. Data science is a collection of many research fields, and has received more attention in recent years due to the growing social needs.

    Differences between data mining and data science

    Data science is required to carry out all processes from data acquisition, accumulation, analysis, model construction, verification, and problem solving. Data mining, on the other hand, is primarily focused on analysis and model building within this step.

     

     

    The main methods of data mining


    Many of the methods used in data mining are those used in statistical analysis and are considered to be useful in data mining as well. From here, I will explain the typical methods of data mining.

    Market basket

    A market basket is a technique used to discover items that are often bought at the same time from retail store sales data. By visualizing products that seem to have little relevance, such as baby diapers and canned beer, but are often purchased at the same time, it helps to create an effective sales floor.

    Clustering

    Clustering is a method of grouping people who have similar behaviors from purchasing data and taking appropriate measures for each group. Classification based on data similarity makes it easier to launch different marketing for each group.

    Logistic regression analysis

    Logistic regression analysis is a statistical method that can explain and predict the probability that a value result (objective variable) will occur from several factors (explanatory variables). Since it is an analysis method that determines the “occurrence rate of a certain event,” it can be expected to be used in various business situations.

    Machine learning

    In some cases, data mining uses machine learning that utilizes AI. Programming languages ​​such as “Python” and “R” are often used for data analysis by machine learning. In particular, Python has a wealth of libraries that are useful for data analysis, making it an effective language for discovering knowledge that finds rules and relationships from data.

     

     

    Data mining implementation procedure


    When performing data mining, it is important to take the right steps. The following describes the specific steps required to perform data mining.

    Collect data

    First, collect the data that suits your purpose. By collecting as much data as possible, it will be easier to find useful data.

    Process and organize data

    Next, we will process and organize the collected data into a form suitable for learning. If there is a lot of useless information called “noise” or irrelevant information, AI will not be able to learn correctly. Therefore, when organizing your data, you should remove noise and analyze using only the information you need.

    Analyze the data

    After processing and organizing the data, we will discover and group the patterns of the data using the methods such as clustering, logistic regression analysis, and market basket introduced above.

    Conduct verification / evaluation

    You may find some rules or relationships in the patterns and groups derived from the analysis. In such cases, apply the discovered rules and relationships to other data, verify and evaluate whether it can be said as a general theory or as a tendency.

     

    Example of data science utilization

    So how is data science actually used in the business scene? Below, we will introduce specific use cases of data science.

    Retail business

    In the retail industry, leveraging a customer database can help you run more effective campaigns and make effective offers to your customers. For example, linking purchase-related data such as “when”, “who”, “where”, “what you purchased”, “what other products you were interested in”, market data, customer data, etc. By aggregating, it is possible to clarify customer behavior patterns and preferences. On top of that, if you narrow down the targets that are likely to be purchased, you can come up with effective marketing measures such as coupon distribution according to customer preferences.
    It is also possible to predict future trends by combining SNS posts and Web behavior data. As a result, product demand can be predicted accurately, the number of inventories to be secured can be grasped, and inventory control can be performed, which can be expected to increase sales and reduce inventory loss at the same time.

    Financial industry

    In the financial industry, stock price and foreign exchange forecasts can be made by combining past stock transaction data and foreign exchange data with various economic indicators occurring in the world.
    Nowadays, AI predicts not only the selection of stocks but also the timing of buying and selling, and services for automatically purchasing foreign currencies have begun to emerge, and such services are expected to become more widespread in the future.

    Restaurant business

    In recent years, the use of data science has been promoted in the restaurant industry as well. In fact, many stores have adopted electronic payments and loyalty points cards, and it has become possible to analyze purchasing behavior and store visit history for each customer.
    In addition, when sales are not expected, we can reduce costs such as food loss by optimizing ingredients and personnel. One of the merits of utilizing data science is that it becomes easier for the restaurant industry to think about measures according to sales forecasts in advance.

     

    Skills useful for data science

    Data scientists are required to solve corporate management issues by collecting and utilizing data. To achieve this, three skills, “statistical analysis skills,” “language skills,” and “IT skills,” are indispensable. Here, we will explain why each skill is necessary.

    Statistical analysis skills

    Data scientists are specialists in the handling and analysis of big data. Therefore, skills to analyze statistics based on the derived data are required. Be sure to acquire mathematical knowledge such as probability, statistics, calculus, and matrix.

    Language skill

    In the business scene, it is required to explain the analysis results in an easy-to-understand and smooth manner even for people without specialized knowledge. In particular, in recent years, the employment of foreign workers in Japan has been increasing year by year due to the effects of the declining birthrate and aging population. It can be said that a certain level of language proficiency is an indispensable skill for smooth communication with business partners and employees.

    IT skills

    Data scientists who handle data naturally need general knowledge of IT. “Database knowledge”, “skills for high-speed data processing”, “programming skills”, etc. are indispensable skills for carrying out business, so it is recommended to learn repeatedly.

     

    UMWELT of TRYETING that can effectively utilize big data!

    If you want to make effective use of big data accumulated in-house, why not use TRYETING’s no-code AI cloud “UMWELT”. Since it is equipped with many algorithms that are useful for data analysis, you can easily build an AI system with just a mouse operation. Another strength of UMWELT is that the period until the introduction of AI is 1/4 of the conventional one, which enables high-speed introduction, and the introduction cost is 1/10 of the conventional one, which is the lowest cost in the industry.

    Summary

    This time, we introduced the differences and outlines between data mining and data science, as well as specific application examples. In the modern society where the environment and methods for handling big data have developed, the technology to obtain knowledge from data is an extremely powerful weapon. By all means, please refer to this article to firmly control the data mining process and improve the prediction accuracy.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy
  • What is Data Mining and Data Science? Thorough explanation of differences and outline

    What is Data Mining and Data Science? Thorough explanation of differences and outline

     

    In recent years, “big data” has attracted a great deal of attention, and how to utilize big data in corporate activities has become an urgent issue in all industries. Therefore, in this article, we will focus on “data mining” and “data science” related to the handling of data.

     

    About data mining and data science


    First, let’s take a look at the definitions and differences between data mining and data science.

    What is data mining?

    Data mining is a technique for finding “knowledge” in a large amount of data by making full use of analysis methods such as statistics and AI. As the word data mining implies, it means mining useful information (data).

    What is data science?

    Data science is a research field for extracting meaningful data using methods in various fields such as statistics and information engineering. Data science is a collection of many research fields, and has received more attention in recent years due to the growing social needs.

    Differences between data mining and data science

    Data science is required to carry out all processes from data acquisition, accumulation, analysis, model construction, verification, and problem solving. Data mining, on the other hand, is primarily focused on analysis and model building within this step.

     

     

    The main methods of data mining


    Many of the methods used in data mining are those used in statistical analysis and are considered to be useful in data mining as well. From here, I will explain the typical methods of data mining.

    Market basket

    A market basket is a technique used to discover items that are often bought at the same time from retail store sales data. By visualizing products that seem to have little relevance, such as baby diapers and canned beer, but are often purchased at the same time, it helps to create an effective sales floor.

    Clustering

    Clustering is a method of grouping people who have similar behaviors from purchasing data and taking appropriate measures for each group. Classification based on data similarity makes it easier to launch different marketing for each group.

    Logistic regression analysis

    Logistic regression analysis is a statistical method that can explain and predict the probability that a value result (objective variable) will occur from several factors (explanatory variables). Since it is an analysis method that determines the “occurrence rate of a certain event,” it can be expected to be used in various business situations.

    Machine learning

    In some cases, data mining uses machine learning that utilizes AI. Programming languages ​​such as “Python” and “R” are often used for data analysis by machine learning. In particular, Python has a wealth of libraries that are useful for data analysis, making it an effective language for discovering knowledge that finds rules and relationships from data.

     

     

    Data mining implementation procedure


    When performing data mining, it is important to take the right steps. The following describes the specific steps required to perform data mining.

    Collect data

    First, collect the data that suits your purpose. By collecting as much data as possible, it will be easier to find useful data.

    Process and organize data

    Next, we will process and organize the collected data into a form suitable for learning. If there is a lot of useless information called “noise” or irrelevant information, AI will not be able to learn correctly. Therefore, when organizing your data, you should remove noise and analyze using only the information you need.

    Analyze the data

    After processing and organizing the data, we will discover and group the patterns of the data using the methods such as clustering, logistic regression analysis, and market basket introduced above.

    Conduct verification / evaluation

    You may find some rules or relationships in the patterns and groups derived from the analysis. In such cases, apply the discovered rules and relationships to other data, verify and evaluate whether it can be said as a general theory or as a tendency.

     

    Example of data science utilization

    So how is data science actually used in the business scene? Below, we will introduce specific use cases of data science.

    Retail business

    In the retail industry, leveraging a customer database can help you run more effective campaigns and make effective offers to your customers. For example, linking purchase-related data such as “when”, “who”, “where”, “what you purchased”, “what other products you were interested in”, market data, customer data, etc. By aggregating, it is possible to clarify customer behavior patterns and preferences. On top of that, if you narrow down the targets that are likely to be purchased, you can come up with effective marketing measures such as coupon distribution according to customer preferences.
    It is also possible to predict future trends by combining SNS posts and Web behavior data. As a result, product demand can be predicted accurately, the number of inventories to be secured can be grasped, and inventory control can be performed, which can be expected to increase sales and reduce inventory loss at the same time.

    Financial industry

    In the financial industry, stock price and foreign exchange forecasts can be made by combining past stock transaction data and foreign exchange data with various economic indicators occurring in the world.
    Nowadays, AI predicts not only the selection of stocks but also the timing of buying and selling, and services for automatically purchasing foreign currencies have begun to emerge, and such services are expected to become more widespread in the future.

    Restaurant business

    In recent years, the use of data science has been promoted in the restaurant industry as well. In fact, many stores have adopted electronic payments and loyalty points cards, and it has become possible to analyze purchasing behavior and store visit history for each customer.
    In addition, when sales are not expected, we can reduce costs such as food loss by optimizing ingredients and personnel. One of the merits of utilizing data science is that it becomes easier for the restaurant industry to think about measures according to sales forecasts in advance.

     

    Skills useful for data science

    Data scientists are required to solve corporate management issues by collecting and utilizing data. To achieve this, three skills, “statistical analysis skills,” “language skills,” and “IT skills,” are indispensable. Here, we will explain why each skill is necessary.

    Statistical analysis skills

    Data scientists are specialists in the handling and analysis of big data. Therefore, skills to analyze statistics based on the derived data are required. Be sure to acquire mathematical knowledge such as probability, statistics, calculus, and matrix.

    Language skill

    In the business scene, it is required to explain the analysis results in an easy-to-understand and smooth manner even for people without specialized knowledge. In particular, in recent years, the employment of foreign workers in Japan has been increasing year by year due to the effects of the declining birthrate and aging population. It can be said that a certain level of language proficiency is an indispensable skill for smooth communication with business partners and employees.

    IT skills

    Data scientists who handle data naturally need general knowledge of IT. “Database knowledge”, “skills for high-speed data processing”, “programming skills”, etc. are indispensable skills for carrying out business, so it is recommended to learn repeatedly.

     

    UMWELT of TRYETING that can effectively utilize big data!

    If you want to make effective use of big data accumulated in-house, why not use TRYETING’s no-code AI cloud “UMWELT”. Since it is equipped with many algorithms that are useful for data analysis, you can easily build an AI system with just a mouse operation. Another strength of UMWELT is that the period until the introduction of AI is 1/4 of the conventional one, which enables high-speed introduction, and the introduction cost is 1/10 of the conventional one, which is the lowest cost in the industry.

    Summary

    This time, we introduced the differences and outlines between data mining and data science, as well as specific application examples. In the modern society where the environment and methods for handling big data have developed, the technology to obtain knowledge from data is an extremely powerful weapon. By all means, please refer to this article to firmly control the data mining process and improve the prediction accuracy.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy
  • What is a service robot? Explanation of definitions and usage examples

    What is a service robot? Explanation of definitions and usage examples

    Today, due to the declining birthrate and aging population, the shortage of human resources and the aging of the workforce are accelerating, and there is a need to both save labor and improve productivity. The emphasis here is on the use of service robots. This article provides an overview of service robots, the benefits of introducing them, and specific usage scenarios.

     

    What is a service robot? Explain the definition

    Service robot are robots that are used in public places, medical facilities, stores, hotels, commercial facilities, and general households. The main purpose is to improve the quality of services and comfort of life, and it is used in a variety of areas such as transportation, reception, guidance, cleaning, nursing care, catering, security, inspection, and inventory management. In recent years, many office buildings have introduced complex service robots that are equipped with multiple functions such as security, reception, and guidance.

    Difference between service robots and industrial robots

    We are currently at the dawn of the Fourth Industrial Revolution, and robotics is attracting attention as one of the technologies that will support its realization. The robot market can be broadly divided into service robots and industrial robots. These differences are where the technology and features are utilized. The Japanese Industrial Standards define the difference between service robots and industrial robots as follows:

    ■Industrial robots

    A robot that is an automatically controlled, reprogrammable, versatile manipulator, programmable in three or more axes, fixed in place or with mobile capabilities, used in industrial automation applications.

    ■Service robot

    A robot that performs tasks that are beneficial to people or equipment. Excludes those used for industrial automation purposes.

    Industrial robots are capable of complex control of multi-axis manipulators (arm-shaped remote control devices), and are primarily used to replace human work at manufacturing sites. Service robots, on the other hand, support human tasks and movements for purposes other than industrial use. In other words, industrial robots are robots that are mainly used in production factories and production lines, and service robots are robots that are used in other areas of life and services. Additionally, unlike industrial robots that simply carry out predetermined actions, service robots are characterized by the fact that many of them can communicate with humans. Furthermore, service robots are broadly divided into commercial and personal use, and indoor and outdoor use.

     service robot

    Advantages of introducing service robots

    There are three main benefits of using service robots in business fields:

    Can reduce labor costs

    By introducing service robots, it is possible to reduce labor costs. For example, serving robots are becoming popular in the food and beverage industry. AI-equipped robots automatically serve food and drinks, significantly reducing the workload on hall staff. In this way, the introduction of service robots not only contributes to reducing labor costs, but also eliminates imbalances in hourly wages such as late at night, weekends, and holidays when there are fewer workers.

    In Japan, as the shortage of human resources worsens due to the effects of the declining birthrate and aging population, the average hourly wage of part-time workers is increasing year by year. Therefore, how to reduce labor costs while maintaining service quality and labor productivity is an important issue for small and medium-sized enterprises and privately run restaurants that lack financial resources. Although introducing a service robot requires a certain amount of cost, it can reduce labor costs by reducing the workload and contribute to stabilizing cash flow.

    Improved customer satisfaction

    The introduction of service robots will also lead to improved customer satisfaction. For example, if tasks such as serving and preparing meals at a restaurant can be automated, hall staff will have more free time to focus on customer service. This makes it possible to provide personalized and attentive service to each customer, which can be expected to improve overall customer satisfaction.

    Another major benefit is that if service robots can replace tasks such as office reception and cleaning, freed human resources can be focused on core tasks that directly lead to improved business performance. Originally, there is no superiority or inferiority to any business, but since a company’s management resources are limited, it is extremely important to distinguish between core business and non-core business. Concentrating resources on highly important tasks will contribute to improving the quality of products and services, which in turn will lead to maximizing customer satisfaction.

    Eliminating labor shortages

    According to data from the Statistics Bureau of the Ministry of Internal Affairs and Communications, the estimated total population of Japan is 124.56 million as of July 1, 2023, which has continued to decline since peaking at 128.08 million in 2008. As a result, the working-age population is decreasing, and the current situation in Japan is that the shortage of human resources and the aging of the workforce are becoming increasingly serious in various fields. In order for companies to continue to develop in this social context, they must both reduce labor and improve productivity.

    If non-core tasks can be streamlined and automated by introducing service robots, tasks that would previously require multiple people can now be handled by a small number of people, making it possible to achieve productivity equal to or higher than before with fewer resources. Additionally, in order to design the placement and wiring of service robots, it is necessary to understand the overall picture of the business process. If the existing business flow can be visualized during this process, it has the advantage of contributing to streamlining operations and reducing man-hours.

     

    Service robot introduction example

    Here, we will introduce scenarios in which “security robots” and “cleaning robots” are used as typical examples of service robots.

    Security robot example

    One of the typical applications of service robots is security work for office buildings, commercial facilities, etc. Robots equipped with IoT sensors and cameras autonomously guard the building and detect suspicious movements and abnormalities. Among these, security robots are particularly good at patrolling within facilities and monitoring (sentry) at specific locations. For example, one office building had six security guards guarding 20 floors. However, there is a case in which the introduction of four security robots automated patrolling and monitoring, cutting the number of security guards in half to three.

    Example of cleaning robot

    Cleaning robots are service robots that are increasingly being introduced in public facilities, commercial facilities, medical facilities, restaurants, etc. It can not only clean floors, walls, windows, etc., but also sterilize them. Since robots automatically run and clean the facility, there is less unevenness in quality, and it is also possible to clean and sterilize places that humans cannot reach or dangerous areas. Coupled with the renewed recognition of the importance of disinfection and sterilization due to the spread of the new coronavirus, the number of cases in which robots are responsible for cleaning and sterilization work is increasing.

     

    Utilization of robots in the office and future prospects

    Service robots are a technology that is attracting attention in various fields, but their current scope of use is extremely limited, such as cleaning, security, reception, and guidance. However, information and communication technology is developing rapidly, and as AI and IoT become more sophisticated, the scope of its use is expected to expand, including improving the efficiency of backyard operations and managing the performance of human resources.

    For example, service robots could be used to manage employee health to help maximize performance, or to support communication by translating and summarizing languages. In the future, it will become an intermediate platform that connects the office environment and online environment, and may become an indispensable solution for building a digital workplace.

     

    Summary

    Service robots are robots whose purpose is to improve the quality of life and services. The introduction of service robots reduces the work load on human resources, providing benefits such as “reducing personnel costs,” “improving customer satisfaction,” and “resolving human resource shortages.” It continues to attract a lot of attention as a technology that will be used in a variety of office environments.

     

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

  • What is a service robot? Explanation of definitions and usage examples

    What is a service robot? Explanation of definitions and usage examples

    Today, due to the declining birthrate and aging population, the shortage of human resources and the aging of the workforce are accelerating, and there is a need to both save labor and improve productivity. The emphasis here is on the use of service robots. This article provides an overview of service robots, the benefits of introducing them, and specific usage scenarios.

     

    What is a service robot? Explain the definition

    Service robot are robots that are used in public places, medical facilities, stores, hotels, commercial facilities, and general households. The main purpose is to improve the quality of services and comfort of life, and it is used in a variety of areas such as transportation, reception, guidance, cleaning, nursing care, catering, security, inspection, and inventory management. In recent years, many office buildings have introduced complex service robots that are equipped with multiple functions such as security, reception, and guidance.

    Difference between service robots and industrial robots

    We are currently at the dawn of the Fourth Industrial Revolution, and robotics is attracting attention as one of the technologies that will support its realization. The robot market can be broadly divided into service robots and industrial robots. These differences are where the technology and features are utilized. The Japanese Industrial Standards define the difference between service robots and industrial robots as follows:

    ■Industrial robots

    A robot that is an automatically controlled, reprogrammable, versatile manipulator, programmable in three or more axes, fixed in place or with mobile capabilities, used in industrial automation applications.

    ■Service robot

    A robot that performs tasks that are beneficial to people or equipment. Excludes those used for industrial automation purposes.

    Industrial robots are capable of complex control of multi-axis manipulators (arm-shaped remote control devices), and are primarily used to replace human work at manufacturing sites. Service robots, on the other hand, support human tasks and movements for purposes other than industrial use. In other words, industrial robots are robots that are mainly used in production factories and production lines, and service robots are robots that are used in other areas of life and services. Additionally, unlike industrial robots that simply carry out predetermined actions, service robots are characterized by the fact that many of them can communicate with humans. Furthermore, service robots are broadly divided into commercial and personal use, and indoor and outdoor use.

     service robot

    Advantages of introducing service robots

    There are three main benefits of using service robots in business fields:

    Can reduce labor costs

    By introducing service robots, it is possible to reduce labor costs. For example, serving robots are becoming popular in the food and beverage industry. AI-equipped robots automatically serve food and drinks, significantly reducing the workload on hall staff. In this way, the introduction of service robots not only contributes to reducing labor costs, but also eliminates imbalances in hourly wages such as late at night, weekends, and holidays when there are fewer workers.

    In Japan, as the shortage of human resources worsens due to the effects of the declining birthrate and aging population, the average hourly wage of part-time workers is increasing year by year. Therefore, how to reduce labor costs while maintaining service quality and labor productivity is an important issue for small and medium-sized enterprises and privately run restaurants that lack financial resources. Although introducing a service robot requires a certain amount of cost, it can reduce labor costs by reducing the workload and contribute to stabilizing cash flow.

    Improved customer satisfaction

    The introduction of service robots will also lead to improved customer satisfaction. For example, if tasks such as serving and preparing meals at a restaurant can be automated, hall staff will have more free time to focus on customer service. This makes it possible to provide personalized and attentive service to each customer, which can be expected to improve overall customer satisfaction.

    Another major benefit is that if service robots can replace tasks such as office reception and cleaning, freed human resources can be focused on core tasks that directly lead to improved business performance. Originally, there is no superiority or inferiority to any business, but since a company’s management resources are limited, it is extremely important to distinguish between core business and non-core business. Concentrating resources on highly important tasks will contribute to improving the quality of products and services, which in turn will lead to maximizing customer satisfaction.

    Eliminating labor shortages

    According to data from the Statistics Bureau of the Ministry of Internal Affairs and Communications, the estimated total population of Japan is 124.56 million as of July 1, 2023, which has continued to decline since peaking at 128.08 million in 2008. As a result, the working-age population is decreasing, and the current situation in Japan is that the shortage of human resources and the aging of the workforce are becoming increasingly serious in various fields. In order for companies to continue to develop in this social context, they must both reduce labor and improve productivity.

    If non-core tasks can be streamlined and automated by introducing service robots, tasks that would previously require multiple people can now be handled by a small number of people, making it possible to achieve productivity equal to or higher than before with fewer resources. Additionally, in order to design the placement and wiring of service robots, it is necessary to understand the overall picture of the business process. If the existing business flow can be visualized during this process, it has the advantage of contributing to streamlining operations and reducing man-hours.

     

    Service robot introduction example

    Here, we will introduce scenarios in which “security robots” and “cleaning robots” are used as typical examples of service robots.

    Security robot example

    One of the typical applications of service robots is security work for office buildings, commercial facilities, etc. Robots equipped with IoT sensors and cameras autonomously guard the building and detect suspicious movements and abnormalities. Among these, security robots are particularly good at patrolling within facilities and monitoring (sentry) at specific locations. For example, one office building had six security guards guarding 20 floors. However, there is a case in which the introduction of four security robots automated patrolling and monitoring, cutting the number of security guards in half to three.

    Example of cleaning robot

    Cleaning robots are service robots that are increasingly being introduced in public facilities, commercial facilities, medical facilities, restaurants, etc. It can not only clean floors, walls, windows, etc., but also sterilize them. Since robots automatically run and clean the facility, there is less unevenness in quality, and it is also possible to clean and sterilize places that humans cannot reach or dangerous areas. Coupled with the renewed recognition of the importance of disinfection and sterilization due to the spread of the new coronavirus, the number of cases in which robots are responsible for cleaning and sterilization work is increasing.

     

    Utilization of robots in the office and future prospects

    Service robots are a technology that is attracting attention in various fields, but their current scope of use is extremely limited, such as cleaning, security, reception, and guidance. However, information and communication technology is developing rapidly, and as AI and IoT become more sophisticated, the scope of its use is expected to expand, including improving the efficiency of backyard operations and managing the performance of human resources.

    For example, service robots could be used to manage employee health to help maximize performance, or to support communication by translating and summarizing languages. In the future, it will become an intermediate platform that connects the office environment and online environment, and may become an indispensable solution for building a digital workplace.

     

    Summary

    Service robots are robots whose purpose is to improve the quality of life and services. The introduction of service robots reduces the work load on human resources, providing benefits such as “reducing personnel costs,” “improving customer satisfaction,” and “resolving human resource shortages.” It continues to attract a lot of attention as a technology that will be used in a variety of office environments.

     

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