research paper on attendance management system

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College Smart Classroom Attendance Management System Based on Internet of Things

Mingtao zhao.

Architectural Engineering School, Qingdao Huanghai University, Qingdao, Shandong 266427, China

Associated Data

The data used to support the findings of this study are available from the corresponding author upon request.

Since entering the information age, educational informatization reform has become the inevitable trend of the development of colleges and universities. The traditional education management methods, especially the classroom attendance methods, not only need to rely on a large number of manpower for data collection and analysis but also cannot dynamically monitor students' attendance and low efficiency. The development of Internet of things technology provides technical support for the informatization reform of education management in colleges and universities and makes the classroom attendance management in colleges and universities have a new development direction. In this study, a college smart classroom attendance management system based on RFID technology and face recognition technology is constructed under the architecture of the Internet of things, and the corresponding simulation experiments are carried out. The experimental results show that the smart classroom attendance management system based on RFID technology can accurately identify the absence and substitution of students and has the advantages of fast response and low cost. However, its recognition is easily affected by obstructions, which requires students to place identification cards uniformly. The smart classroom attendance management system based on face recognition technology can accurately record and identify the situation of students entering and leaving the classroom and identify the situations of being late and leaving early, absenteeism, and substitute classes. The experimental results are basically consistent with the sample results, and the error rate is low. However, the system is easily affected by environmental light, students' sitting posture, expression, and other factors, so it cannot be recognized. Generally speaking, both can meet the needs of classroom attendance in colleges and universities and have high accuracy and efficiency.

1. Introduction

The development of information technology and Internet of things technology has promoted the pace of educational informatization reform. The informatization development of educational management has also become one of the focuses of attention and research in colleges and universities. The development of college education management methods and mechanisms needs to measure the effectiveness and rationality of daily teaching through real and scientific data, in which student classroom attendance is an important part of college education management [ 1 ]. Classroom attendance includes student attendance and teacher attendance. It can not only reflect students' learning behavior but also reflect the authenticity and effectiveness of teachers' teaching classroom and provide important basic data information for teaching reform. The collection of classroom attendance data is closely related to the classroom attendance technology and methods. The traditional classroom attendance method is mainly manual attendance. Such attendance methods not only can dynamically grasp the attendance status of students, but also is prone to errors and omissions. It also needs to repeatedly test the attendance information, which consumes a lot of manpower and materials [ 2 ]. At the same time, manual attendance records are generally recorded, sorted, and kept by teachers, so it is not easy for students to understand their attendance. Therefore, the traditional classroom attendance method can meet neither the needs of students and teachers nor the requirements of the development of information technology in colleges and universities [ 3 ].

The development of Internet of things technology provides a new development direction for colleges and universities to build information-based classroom attendance system and mechanism. Many colleges and universities are also constantly trying new technologies in the process of building smart classrooms. Therefore, this study proposes the research on a college smart classroom attendance management system based on the Internet of things, constructs the classroom attendance management system based on RFID technology and face recognition technology, and carries out the corresponding simulation test. This study is mainly divided into three parts: the first part expounds on the development of classroom attendance technology in colleges and universities; the second part is to build a classroom attendance management system based on RFID technology and face recognition technology under the framework of the Internet of things; the third part is the test and result analysis of classroom attendance management system based on RFID technology and face recognition technology.

Under the architecture of Internet of things, this study constructs an attendance management system of intelligent classroom in colleges and universities based on RFID technology and face recognition technology and carries out the corresponding simulation experiments. Research and innovation contributions are as follows: (1) The intelligent classroom attendance management system based on RFID technology can accurately identify the absence and substitution of students. It has the advantages of fast response and low cost. (2) The intelligent classroom attendance management system based on face recognition technology can accurately record and identify the situation of students entering and leaving the classroom and identify the situation of being late and leaving early, absenteeism, and substitute classes. The experimental results are basically consistent with the sample results, and the error rate is low. The research provides technical support for the development of Internet of things technology and the reform of educational management informatization in colleges and universities and makes the classroom attendance management in colleges and universities have a new development direction.

2. Development of Classroom Attendance Technology in Colleges and Universities

The development of college attendance technology is closely related to the development of information technology and Internet technology. Before the wide application of information technology and Internet technology, classroom attendance was mainly based on traditional manual attendance; that is, the arrival of paper signs was used to record the attendance of students and teachers in class, and then the relevant personnel carried out data statistics and analysis after class [ 4 ]. Teachers will also conduct random roll calls and multiple roll calls in class according to the actual situation to monitor students' classroom attendance. To a certain extent, this attendance method restricts the occurrence of students' behaviors such as being late, leaving early, and absenteeism and improves the management of students. In addition, the teaching method of colleges and universities is different from that of middle schools. There is no fixed class. Manual attendance can help teachers know and understand students, shorten the distance between students and teachers, and improve the communication between teachers and students [ 5 ]. However, with the expansion of the scale of colleges and universities and the increase of the number of students, the way of manual attendance will take up a lot of time, and the accuracy and efficiency of attendance will be reduced. At the same time, the way of manual attendance cannot realize the timely feedback and dynamic tracking of relevant data, and the head teacher cannot timely understand and master the situation of students' classroom attendance [ 6 ].

With the development and application of computer technology, classroom attendance also began to enter the information age. Electronic communication technology and computer technology provide technical support for the informatization of classroom attendance. Some scholars propose to combine campus magnetic card or IC card with computer technology to collect and store campus card and student information through the attendance equipment that can be read, so as to be applied to the attendance system [ 7 ]. Other scholars have combined RFID technology to design a system that can carry out attendance and recording simply, quickly, and automatically, which improves the accuracy, efficiency, and timeliness of attendance technology [ 8 ]. As campus cards and other cards are forged, fraudulently used, and embezzled, which reduces the accuracy and authenticity of attendance data, someone developed biometric attendance technology based on computer technology and biological science and technology [ 9 ]. Biometric attendance technology is composed of computer technology, optics, acoustics, biosensors, statistical principles, and other discipline technologies. It mainly verifies personal identity through the inherent behavior and physiological characteristics of human body. The common ones are fingerprint recognition, iris recognition, and so on [ 10 , 11 ].

With the development of mobile Internet technology and equipment, the smart campus and smart classroom have become important development models of colleges and universities and put forward higher requirements for attendance technology and methods of colleges and universities [ 12 ]. On the basis of big data, Internet of things, and other information technologies, college classroom attendance combines intelligent mobile devices to build a new generation of classroom attendance system [ 13 ]. Some scholars have built an intelligent classroom attendance system with Internet of things technology as the core and verified its good system stability through experiments [ 14 ]. On the basis of mobile Internet, other scholars have realized the active and random data collection of classroom attendance information by using intelligent mobile terminal equipment, bluetooth, QR code, app, etc., which can feed back and track the information data in real time [ 15 ]. However, such attendance methods must be used in an environment that can connect to the network, and it is also unable to identify the substitute class. Therefore, some scholars proposed to integrate radio frequency identification technology into the classroom attendance system and constructed RFID-based automatic identification campus attendance technology, which can carry out real-time senseless data collection [ 16 ]. Other scholars have built a classroom attendance system based on face recognition technology, which improves the uniqueness and exclusivity of attendance and reduces the number of clock outs while ensuring the accuracy of classroom attendance [ 17 ]. However, face recognition technology needs to build a corresponding database. As in the early stage of the development of this technology, the technology has certain limitations, high management cost, and easy to be affected by environmental factors [ 18 ]. With the deepening of education informatization reform in colleges and universities, colleges and universities are constantly innovating and practicing the classroom attendance system according to their own actual situation and needs and are also constantly adjusting and improving the education management mechanism, so as to provide more convenient, fast, and reliable information services for teachers and students [ 19 ].

3. College Smart Classroom Attendance Management System Based on Internet of Things

The classroom attendance management system is based on Internet of things technology. The Zige system is convenient for teachers to collect and manage students' attendance information and improve the accuracy of Zige's 3G sensor network, which not only saves students' time and attendance but also improves students' attendance and management. With the continuous development of Internet, Internet of things, and information technology, colleges and universities have been exploring and studying efficient and convenient smart classroom attendance methods that can analyze classroom attendance data in the practice of building smart classroom, so as to provide multidimensional and diversified decision support for the development of smart classroom in colleges and universities [ 20 ]. At the same time, we also need to take into account the actual needs of smart classroom in colleges and universities, the software and hardware conditions required by attendance methods, technical feasibility, and economic feasibility. Therefore, the classroom attendance management system based on RFID technology and the classroom attendance management system based on face recognition technology have become the choice of many colleges and universities. As the core content of face recognition, feature extraction and comparison recognition are to use computer technology to locate the location of biological key points. After geometric and optical corrections, the feature key points that can be compared are extracted and compared with the facial texture code of the face database. Finally, the automatic processing technology of individual identification is carried out.

3.1. Construction of College Classroom Attendance Management System Based on RFID Technology

The traditional classroom attendance card swiping method in colleges and universities can neither control the number of students in the classroom and the situation of students being late and leaving early in real time nor identify problems such as one person with multiple cards. The classroom attendance management system based on RFID technology can realize the way of real-time and efficient roll call and information attendance and master the attendance of the classroom in real time. As shown in Figure 1 , it is the overall block diagram of the classroom attendance management system based on RFID technology.

Overall block diagram of classroom attendance management system based on RFID technology.

RFID technology can realize indoor real-time positioning in a large range of coverage without contact and has the advantages of high visibility, convenience, and low cost. For indoor positioning based on RFID technology, the basic coordinates need to be selected according to the specific situation of the classroom, and the corresponding calculation is carried out through the coordinates. Considering that there will be a multipath effect in positioning through the distance between the electronic tag and the receiver, which will affect the accuracy of positioning coordinates, this study uses the strength of the relative received signal for indoor positioning, and its calculation is shown in the following formula:

In the formula, the distance between the electronic tag and the reader is expressed as d , the reference distance is expressed as d 0 , the environmental parameter is expressed as n , the signal strength at the distance d between the electronic tag and the reader is expressed as PL ( d ), and the signal strength at the reference distance is expressed as pl ( d 0 ).

The coordinate position of the label to be tested is set as ( x , y ) and the position of the reader-writer as ( X i , Y i ). When the position is known, the distance between the label to be tested and the i reader-writer is shown in the following formula:

If there is an intersection A in the effective area of two readers and writers, and there is also an intersection outside the area, then the A point is reasonable. The average value of all solutions is calculated to obtain the final solution of each label, as shown in the following formulas:

The label value obtained according to the formula is ( X , Y ), which is converted into the reference coordinates of the setting area to obtain the specific position of the label seat.

Friis path loss model realizes positioning through Frith transmission formula in a free propagation model, as shown in the following formula:

The model power received by the passive tag is expressed as P r , the transmission power of the reader is expressed as P t , the radiation radius of its corresponding reading and writing area is expressed as R , the antenna gain of the reader is expressed as G r , the antenna gain of the tag is expressed as G t , and the path loss is expressed as L path . The expression is shown as follows:

Combining formulas ( 5 ) and ( 6 )

The logarithm of formula ( 7 ) is obtained as follows:

Due to the amplitude fading caused by human interference or obstruction in the indoor environment, the reception power of passive tags is reduced, resulting in the problem of missing reading. Therefore, formula ( 8 ) is modified as follows:

The Gaussian white noise is expressed as X σ , its standard deviation is expressed as σ , and the actual received power of the tag is expressed as P r ′.

Each seat in the classroom represents the corresponding target area. In principle, each target area corresponds to only one electronic label. When multiple electronic labels appear on the same seat, students may brush on behalf of others. As shown in Figure 2 , it is the effect diagram of antiproxy brushing of college classroom attendance management system based on RFID technology.

Effect drawing of antiproxy brushing of college classroom attendance management system based on RFID technology.

The traditional attendance management is time consuming, laborious, and inefficient, and the information is not timely, which cannot meet the management requirements of modern colleges and universities. Therefore, it is necessary to adopt the student attendance management system based on the campus network to automatically collect student attendance information through RFID (radio frequency identification) technology. To avoid that teachers' roll call takes up classroom teaching time and improve teaching efficiency, the convenience of students' leave and the efficiency of management approval is improved through online leave and approval. Students, teachers, and teaching management departments share attendance information through the campus network to increase the transparency of information and improve the management quality of management departments. The attendance management system will automatically calculate the number and position of electronic tags according to the RFID antenna in the classroom, so as to obtain the precise position of each electronic tag in the classroom and identify normal seats, empty seats, and proxy brushing seats with multiple cards. However, if students put the card in their schoolbag or pocket, it may affect the accuracy of measurement. In actual use, students will be required to place the card in a certain position on the desktop to reduce missing reading.

3.2. Construction of College Classroom Attendance Management System Based on Face Recognition Technology

With the development of Internet of things technology, face recognition technology is widely used in the smart campus of colleges and universities. Face recognition technology combined with wireless network technology can realize classroom attendance check-in, detect students' late and early leave, absenteeism, substitute class, etc., and effectively and accurately record students' access to the classroom. The face recognition technology in this study is based on the Harr feature to detect the face; that is, the integral graph method is used to calculate many matrix features contained in the detection window. Let the sum of the luminance values of the rectangular area composed of the image starting point and point i ( x , y ) be expressed as s ( x , y ), and it will be saved in the memory as an integral value. When the vertical and horizontal coordinates of the image to be calculated exceed the sum of the lighting degrees of all pixels of point i ( x , y ), it can be calculated as long as it traverses all points of the original image once, as shown in the following formula:

In order to reduce the interference and recognition influence on the classroom attendance system and improve the recognition efficiency and accuracy, it is necessary to preprocess the image. By calculating the weighted average of the red specific gravity, green specific gravity, and blue specific gravity of the color image, the transformation of the image gray scale is completed. The calculation formula is shown as follows:

Then, the image is histogram equalized and its new pixel value is calculated, as shown in the following formula:

where k =0,1,2,…, L − 1, the total number of image pixels is expressed as n , the number of pixels of the current gray level is expressed as n j , and the total number of possible gray levels of the image is expressed as L .

Because the gray scale of LBP operator has good robustness and is not affected by lighting conditions, it has a fast computing speed and can analyze images in a complex real-time environment. Therefore, the university classroom attendance management system based on face recognition technology in this study is based on the LBP method for image feature extraction. LBP is initially set in the 3 × 3 pixel field, and the gray values of nine pixels in the field are extracted. The central pixel is selected as the threshold and the gray value is compared with the other eight adjacent pixels. When the central pixel is lower than the gray value of the adjacent pixel, the adjacent pixel is recorded as 1; otherwise, it is 0. The calculation formula of LBP value of the central pixel of the neighborhood is shown as follows:

In the formula, the central pixel of the neighborhood is expressed as ( x c , y c ), its pixel value is expressed as i c , other pixel values in the field are expressed as i p , and the symbolic function is expressed as s .

In order to enable LBP to better extract texture features from large-scale images, the LBP operator is improved to a circular LBP operator; that is, it is assumed that it contains eight sampling points in the 5 × 5 neighborhood, and the coordinate value calculation formulas of each sampling point are shown as follows:

where the center point of the neighborhood is expressed as ( x c , y c ) and a sampling point is expressed as ( x p , y p ).

When the university smart classroom attendance management system performs face recognition through LBPH algorithm, it needs to initialize parameters first and then LBP coding; that is, ( x , y ) n is set as the corresponding pixel offset coordinate in the n field, and its calculation formula is shown as follows:

The n field gray value of all pixel coordinates is calculated through bilinear difference, and the coded value is calculated according to the following formula:

The calculation formula of LBP coding value of all pixels contained in each submodule is shown as follows:

The histogram is calculated. The calculation formula of the width and height of each grid in the image is shown as follows:

The similarity between two histograms is calculated by the card method, as shown in the following formula:

4. Test and Results of College Smart Classroom Attendance Management System Based on Internet of Things

4.1. test and results of college classroom attendance management system based on rfid technology.

The traditional manual attendance check-in or manual attendance check-in is widely used in colleges and universities. This method occupies a lot of time for teachers and is inefficient. RFID technology is used to collect student data, C/S architecture is used, and c# programming language is used to develop a card reader attendance management system. The student information is stored into the card reader database so that teachers can easily grasp the students' class situation. The system can greatly reduce the time spent on attendance, reduce the burden of teachers, and effectively improve the attendance rate of students. Before testing the college classroom attendance management system based on RFID technology, we need to understand the influence relationship between the number of readers and positioning performance. The simulation results are shown in Figure 3 . As can be seen from the CDF distribution curve in the figure, when the number of readers is three, half of the probability of positioning error is not higher than 3.1 m; when the number of readers increases to four, half of the probability of positioning error is not higher than 2.35 m, and 80% of the probability is not higher than 3.2 m; when the number of readers increases to five, half of the probability of positioning error is no more than 2.2 m, and 80% of the probability is no more than 2.8 m.

The influence of the number of readers in the system on the positioning accuracy.

Figure 4 shows the relationship between the number of readers and positioning accuracy in the Friis loss model. It can be seen from the data in the figure that when the number of readers is three, the probability of positioning error not exceeding 1.13 m is 50%, and the probability of not exceeding 1.2 m is 80%; after adding a reader, the probability of positioning error not higher than 0.89 m is 50%, and the probability of not higher than 0.94 m is 80%; when increasing to five readers, the probability of positioning error not higher than 0.84 m is half, and the probability of lower than 0.91 m is 80%.

Effect of the number of readers in Friis loss model on positioning accuracy.

To sum up, when the number of readers increases from three to four, the error decreases greatly, and the accuracy of system positioning is significantly improved. When it is increased from four to five, the decline of system error decreases, and the impact on accuracy is very small. Therefore, the increase of the number of readers will improve the positioning accuracy of the system, and the accuracy will increase with the increase of the number of readers. However, when the number of readers increases to a certain number, its further increase has little impact on the positioning accuracy of the system. Considering the actual situation, the number of readers is four, which is relatively appropriate.

As shown in Figure 5 , the research results of user waiting time are queried by the university classroom attendance management system based on RFID technology. It can be seen from the data in the figure that 76% of users are satisfied that the time required for access query is 6 s; when the access query time increased to 11 s, the proportion of satisfied users decreased to 63%; when the access query time increased to 23 s, 41% of users were satisfied; only 5% of users said they could accept 36 s of query time. During the simulation experiment, the amount of database information of the system is relatively small and the number of users participating in the simulation test is limited. Therefore, the average query time is maintained at 5 s, which can basically meet the needs of all users. However, in a practical application, the number of users and database information will continue to increase, which will also increase the burden on the system operation. In case of slow operation, it is necessary to further optimize the system.

University classroom attendance management system based on RFID technology queries the research results of user waiting time.

As shown in Figure 6 , it is the relationship between the university classroom attendance management system based on RFID technology, response time, and server request. It can be seen from the figure that the increase in the number of login users will prolong the system response time. When the number of users increases to 100, the system response time is 30 times that of 20 users. The number of requests that the server can handle per unit time decreases with the increase of the number of users. When the number increases to 100, the number of requests processed by the server decreases by 14 compared with the initial number.

RFID-based campus attendance management system's user number and response time relationship and server request relationship diagram.

4.2. Test and Results of College Classroom Attendance Management System Based on Face Recognition Technology

The classroom attendance test results of college classroom attendance management system based on face recognition technology are shown in Figures ​ Figures7 7 and ​ and8; 8 ; 1 in the figure indicates that the recognition of the student in the classroom is successful, and 0 indicates that the recognition is unsuccessful. It can be seen that the university classroom attendance management system based on face recognition technology can realize independent classroom attendance and identify the access of students in the classroom, such as leaving early, absenteeism, and substitute classes. Because the face recognition technology will be affected by the lighting conditions of the surrounding environment, students' sitting posture, expression, and other factors, although some students attend class on time, the system still shows that the recognition fails. On the whole, the test results of college classroom attendance management system based on face recognition technology are basically consistent with the sample results and can meet the expected requirements.

Test results of college classroom attendance management system based on face recognition technology.

Comparison of attendance score and sample score in system experiment.

To sum up, both the university classroom attendance management system based on face recognition technology and the university classroom attendance management system based on RFID technology can basically meet the needs of classroom attendance with high accuracy. However, due to the limitations of technology and environment, both of them have some disadvantages. The college classroom attendance management system based on RFID technology cannot meet the needs of college students of different majors to log in to the system at the same time, and the running time will slow down with the increase of the number. The university classroom attendance management system based on face recognition technology has certain requirements for the environment of classroom attendance. Therefore, both of them need to be further optimized and improved.

5. Conclusion

This study presents the research of college intelligent classroom attendance management system based on the framework of Internet of things. Under the framework of Internet of things, this study constructs a classroom attendance management system based on RFID technology and face recognition technology. The experimental results show that the intelligent classroom attendance management system based on RFID technology can accurately identify students' absence and substitution. It has the advantages of fast response speed and low cost. The intelligent classroom attendance management system based on face recognition technology can accurately record and identify the situation of students entering and leaving the classroom and identify the situation of being late and leaving early, absenteeism, and substitute classes. The experimental results are basically consistent with the sample results, and the error rate is low. This study provides technical support for the development of Internet of things technology and the informatization reform of educational management in colleges and universities and makes the classroom attendance management in colleges and universities have a new development direction. The experimental results are basically consistent with the sample results. However, it is easily affected by ambient light, students' sitting posture, and expression and cannot be recognized. These two kinds of college classroom attendance management systems can meet the basic needs of colleges and universities, but there are still some technical limitations, which need to be further optimized and debugged.

Data Availability

Conflicts of interest.

The authors declare that they have no conflicts of interest.

Raspberry Pi-Based Smart Attendance Management System with Improved Version of RFID Over IoT

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• S. Venkat Pavan Kumar 7 ,
• T. S. Arulananth 7 &
• S. V. S. Prasad 7  

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College attendance is important with respect to education, punctuality, and safety of the students. Nowadays, of digital electros, many systems were developed to track the attendance of the student with accurate. Many alternative models have been developed to secure students and autonomous attendance system. Here is a framework called automated RFID, IoT, and Raspberry Pi-based attendance system that is safe and secure system for attendance and student safety with server database of all students’ information. When the student shows the card to the reader module, it automatically detects and stores in Raspberry Pi and then sends to the server using the IoT module. Status of the work will be displayed in LCD module, and a buzzer will alert you if unauthorized card is detected. This method is very easy to track the student and monitor his/her attendance status anywhere in the world through Internet of things (IoT).

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Venkat Pavan Kumar, S., Arulananth, T.S., Prasad, S.V.S. (2023). Raspberry Pi-Based Smart Attendance Management System with Improved Version of RFID Over IoT. In: Mandal, J.K., Hinchey, M., Rao, K.S. (eds) Innovations in Signal Processing and Embedded Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-1669-4_40

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Today, everybody across the organization is hungry to know more about AI. What is it good for? Should I trust it? Will it take my job? Business leaders are investing in massive training programs, partnering with promising vendors and consultants, and collaborating with peers to identify ways to benefit from AI and avoid the risk of AI missteps. They are trying to understand how to manage AI responsibly and at scale.

Our book Data Is Everybody’s Business: The Fundamentals of Data Monetization describes how organizations make money using their data.[foot]Barbara H. Wixom, Cynthia M. Beath, and Leslie Owens, Data Is Everybody's Business: The Fundamentals of Data Monetization , (Cambridge: The MIT Press, 2023), https://mitpress.mit.edu/9780262048217/data-is-everybodys-business/ .[/foot] We wrote the book to clarify what data monetization is (the conversion of data into financial returns) and how to do it (by using data to improve work, wrap products and experiences, and sell informational solutions). AI technology’s role in this is to help data monetization project teams use data in ways that humans cannot, usually because of big complexity or scope or required speed. In our data monetization research, we have regularly seen leaders use AI effectively to realize extraordinary business goals. In this briefing, we explain how such leaders achieve big AI wins and maximize financial returns.

Using AI in Data Monetization

AI refers to the ability of machines to perform human-like cognitive tasks.[foot]See Hind Benbya, Thomas H. Davenport, and Stella Pachidi, “Special Issue Editorial: Artificial Intelligence in Organizations: Current State and Future Opportunities , ” MIS Quarterly Executive 19, no. 4 (December 2020), https://aisel.aisnet.org/misqe/vol19/iss4/4 .[/foot] Since 2019, MIT CISR researchers have been studying deployed data monetization initiatives that rely on machine learning and predictive algorithms, commonly referred to as predictive AI.[foot]This research draws on a Q1 to Q2 2019 asynchronous discussion about AI-related challenges with fifty-three data executives from the MIT CISR Data Research Advisory Board; more than one hundred structured interviews with AI professionals regarding fifty-two AI projects from Q3 2019 to Q2 2020; and ten AI project narratives published by MIT CISR between 2020 and 2023.[/foot] Such initiatives use large data repositories to recognize patterns across time, draw inferences, and predict outcomes and future trends. For example, the Australian Taxation Office (ATO) used machine learning, neural nets, and decision trees to understand citizen tax-filing behaviors and produce respectful nudges that helped citizens abide by Australia’s work-related expense policies. In 2018, the nudging resulted in AUD$113 million in changed claim amounts.[foot]I. A. Someh, B. H. Wixom, and R. W. Gregory, “The Australian Taxation Office: Creating Value with Advanced Analytics,” MIT CISR Working Paper No. 447, November 2020, https://cisr.mit.edu/publication/MIT_CISRwp447_ATOAdvancedAnalytics_SomehWixomGregory .[/foot]

In 2023, we began exploring data monetization initiatives that rely on generative AI.[foot]This research draws on two asynchronous generative AI discussions (Q3 2023, N=35; Q1 2024, N=34) regarding investments and capabilities and roles and skills, respectively, with data executives from the MIT CISR Data Research Advisory Board. It also draws on in-progress case studies with large organizations in the publishing, building materials, and equipment manufacturing industries.[/foot] This type of AI analyzes vast amounts of text or image data to discern patterns in them. Using these patterns, generative AI can create new text, software code, images, or videos, usually in response to user prompts. Organizations are now beginning to openly discuss data monetization initiative deployments that include generative AI technologies. For example, used vehicle retailer CarMax reported using OpenAI’s ChatGPT chatbot to help aggregate customer reviews and other car information from multiple data sets to create helpful, easy-to-read summaries about individual used cars for its online shoppers. At any point in time, CarMax has on average 50,000 cars on its website, so to produce such content without AI the company would require hundreds of content writers and years of time; using ChatGPT, the company’s content team can generate summaries in hours.[foot]Paula Rooney, “CarMax drives business value with GPT-3.5,” CIO , May 5, 2023, https://www.cio.com/article/475487/carmax-drives-business-value-with-gpt-3-5.html ; Hayete Gallot and Shamim Mohammad, “Taking the car-buying experience to the max with AI,” January 2, 2024, in Pivotal with Hayete Gallot, produced by Larj Media, podcast, MP3 audio, https://podcasts.apple.com/us/podcast/taking-the-car-buying-experience-to-the-max-with-ai/id1667013760?i=1000640365455 .[/foot]

Big advancements in machine learning, generative tools, and other AI technologies inspire big investments when leaders believe the technologies can help satisfy pent-up demand for solutions that previously seemed out of reach. However, there is a lot to learn about novel technologies before we can properly manage them. In this year’s MIT CISR research, we are studying predictive and generative AI from several angles. This briefing is the first in a series; in future briefings we will present management advice specific to machine learning and generative tools. For now, we present three principles supported by our data monetization research to guide business leaders when making AI investments of any kind: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects.

Principle 1: Invest in Practices That Build Capabilities Required for AI

Succeeding with AI depends on having deep data science skills that help teams successfully build and validate effective models. In fact, organizations need deep data science skills even when the models they are using are embedded in tools and partner solutions, including to evaluate their risks; only then can their teams make informed decisions about how to incorporate AI effectively into work practices. We worry that some leaders view buying AI products from providers as an opportunity to use AI without deep data science skills; we do not advise this.

But deep data science skills are not enough. Leaders often hire new talent and offer AI literacy training without making adequate investments in building complementary skills that are just as important. Our research shows that an organization’s progress in AI is dependent on having not only an advanced data science capability, but on having equally advanced capabilities in data management, data platform, acceptable data use, and customer understanding.[foot]In the June 2022 MIT CISR research briefing, we described why and how organizations build the five advanced data monetization capabilities for AI. See B. H. Wixom, I. A. Someh, and C. M. Beath, “Building Advanced Data Monetization Capabilities for the AI-Powered Organization,” MIT CISR Research Briefing, Vol. XXII, No. 6, June 2022, https://cisr.mit.edu/publication/2022_0601_AdvancedAICapabilities_WixomSomehBeath .[/foot] Think about it. Without the ability to curate data (an advanced data management capability), teams cannot effectively incorporate a diverse set of features into their models. Without the ability to oversee the legality and ethics of partners’ data use (an advanced acceptable data use capability), teams cannot responsibly deploy AI solutions into production.

It’s no surprise that ATO’s AI journey evolved in conjunction with the organization’s Smarter Data Program, which ATO established to build world-class data analytics capabilities, and that CarMax emphasizes that its governance, talent, and other data investments have been core to its generative AI progress.

Capabilities come mainly from learning by doing, so they are shaped by new practices in the form of training programs, policies, processes, or tools. As organizations undertake more and more sophisticated practices, their capabilities get more robust. Do invest in AI training—but also invest in practices that will boost the organization’s ability to manage data (such as adopting a data cataloging tool), make data accessible cost effectively (such as adopting cloud policies), improve data governance (such as establishing an ethical oversight committee), and solidify your customer understanding (such as mapping customer journeys). In particular, adopt policies and processes that will improve your data governance, so that data is only used in AI initiatives in ways that are consonant with your organization's values and its regulatory environment.

Principle 2: Involve All Your People in Your AI Journey

Data monetization initiatives require a variety of stakeholders—people doing the work, developing products, and offering solutions—to inform project requirements and to ensure the adoption and confident use of new data tools and behaviors.[foot]Ida Someh, Barbara Wixom, Michael Davern, and Graeme Shanks, “Configuring Relationships between Analytics and Business Domain Groups for Knowledge Integration, ” Journal of the Association for Information Systems 24, no. 2 (2023): 592-618, https://cisr.mit.edu/publication/configuring-relationships-between-analytics-and-business-domain-groups-knowledge .[/foot] With AI, involving a variety of stakeholders in initiatives helps non-data scientists become knowledgeable about what AI can and cannot do, how long it takes to deliver certain kinds of functionality, and what AI solutions cost. This, in turn, helps organizations in building trustworthy models, an important AI capability we call AI explanation (AIX).[foot]Ida Someh, Barbara H. Wixom, Cynthia M. Beath, and Angela Zutavern, “Building an Artificial Intelligence Explanation Capability,” MIS Quarterly Executive 21, no. 2 (2022), https://cisr.mit.edu/publication/building-artificial-intelligence-explanation-capability .[/foot]

For example, at ATO, data scientists educated business colleagues on the mechanics and results of models they created. Business colleagues provided feedback on the logic used in the models and helped to fine-tune them, and this interaction helped everyone understand how the AI made decisions. The data scientists provided their model results to ATO auditors, who also served as a feedback loop to the data scientists for improving the model. The data scientists regularly reported on initiative progress to senior management, regulators, and other stakeholders, which ensured that the AI team was proactively creating positive benefits without neglecting negative external factors that might surface.

Given the consumerization of generative AI tools, we believe that pervasive worker involvement in ideating, building, refining, using, and testing AI models and tools will become even more crucial to deploying fruitful AI projects—and building trust that AI will do the right thing in the right way at the right time.

Principle 3: Focus on Realizing Value From Your AI Projects

AI is costly—just add up your organization’s expenses in tools, talent, and training. AI needs to pay off, yet some organizations become distracted with endless experimentation. Others get caught up in finding the sweet spot of the technology, ignoring the sweet spot of their business model. For example, it is easy to become enamored of using generative AI to improve worker productivity, rolling out tools for employees to write better emails and capture what happened in meetings. But unless those activities materially impact how your organization makes money, there likely are better ways to spend your time and money.

Leaders with data monetization experience will make sure their AI projects realize value in the form of increased revenues or reduced expenses by backing initiatives that are clearly aligned with real challenges and opportunities. That is step one. In our research, the leaders that realize value from their data monetization initiatives measure and track their outcomes, especially their financial outcomes, and they hold someone accountable for achieving the desired financial returns. At CarMax, a cross-functional team owned the mission to provide better website information for used car shoppers, a mission important to the company’s sales goals. Starting with sales goals in mind, the team experimented with and then chose a generative AI solution that would enhance the shopper experience and increase sales.

Figure 1: Three Principles for Getting Value from AI Investments

The three principles are based on the following concepts from MIT CISR data research: 1. Data liquidity: the ease of data asset recombination and reuse 2. Data democracy: an organization that empowers employees in the access and use of data 3. Data monetization: the generation of financial returns from data assets

Managing AI Using a Data Monetization Mindset

AI has and always will play a big role in data monetization. It’s not a matter of whether to incorporate AI, but a matter of how to best use it. To figure this out, quantify the outcomes of some of your organization’s recent AI projects. How much money has the organization realized from them? If the answer disappoints, then make sure the AI technology value proposition is a fit for your organization’s most important goals. Then assign accountability for ensuring that AI technology is applied in use cases that impact your income statements. If the AI technology is not a fit for your organization, then don’t be distracted by media reports of the AI du jour.

Understanding your AI technology investments can be hard if your organization is using AI tools that are bundled in software you purchase or are built for you by a consultant. To set yourself up for success, ask your partners to be transparent with you about the quality of data they used to train their AI models and the data practices they relied on. Do their answers persuade you that their tools are trustworthy? Is it obvious that your partner is using data compliantly and is safeguarding the model from producing bad or undesired outcomes? If so, make sure this good news is shared with the people in your organization and those your organization serves. If not, rethink whether to break with your partner and find another way to incorporate the AI technology into your organization, such as by hiring people to build it in-house.

To paraphrase our book’s conclusion: When people actively engage in data monetization initiatives using AI , they learn, and they help their organization learn. Their engagement creates momentum that initiates a virtuous cycle in which people’s engagement leads to better data and more bottom-line value, which in turn leads to new ideas and more engagement, which further improves data and delivers more value, and so on. Imagine this happening across your organization as all people everywhere make it their business to find ways to use AI to monetize data.

This is why AI, like data, is everybody’s business.

© 2024 MIT Center for Information Systems Research, Wixom and Beath. MIT CISR Research Briefings are published monthly to update the center’s member organizations on current research projects.

Related Publications

Talking Points

Ai, like data, is everybody's business.

Working Paper: Vignette

The australian taxation office: creating value with advanced analytics.

Research Briefing

Building advanced data monetization capabilities for the ai-powered organization.

Building AI Explanation Capability for the AI-Powered Organization

What is Data Monetization?

About the researchers.

Barbara H. Wixom, Principal Research Scientist, MIT Center for Information Systems Research (CISR)

Cynthia M. Beath, Professor Emerita, University of Texas and Academic Research Fellow, MIT CISR

Mit center for information systems research (cisr).

Founded in 1974 and grounded in MIT's tradition of combining academic knowledge and practical purpose, MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

MIT CISR Associate Members

MIT CISR wishes to thank all of our associate members for their support and contributions.

MIT CISR's Mission Expand

MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We provide insights on how organizations effectively realize value from approaches such as digital business transformation, data monetization, business ecosystems, and the digital workplace. Founded in 1974 and grounded in MIT’s tradition of combining academic knowledge and practical purpose, we work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

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Virtual fence (VF) is the use of a global positioning system (GPS) to dictate where on the landscape livestock can graze without relying on traditional physical fence such as barbed wire. The recent acceleration in the development and adoption of VF technology for grazing management has been characterized by the evolution of divergent terminology. Different research and commercial entities have adopted terms and definitions independently. Some terms and definitions are inherently problematic, while others are more aligned, and the simple fact that differences exist contributes to confusion in communication among scientists, producers, land managers, manufacturers, government agencies, and the public. In this paper, we propose a standard terminology determined during a 2-d in-service workshop at the annual meeting of the Society of Rangeland Management in February 2023. Standard terminology will aid in efficient and effective communication among all entities and interested parties.

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