Harnessing the Power of Data - A Guide to Digital Intelligence in Business - Part One

Data Intelligence at its core is the intersection of data with business analytics and their profound impact on modern enterprises. The synthesis and integration of modern advanced analytics techniques enables organizations to extract valuable insights from vast amounts of data, optimize decision-making processes, and gain a competitive edge in today’s data-driven economy. In today’s digitally interconnected world, organizations are generating vast amounts of data at an unprecedented rate. This data holds valuable insights that can drive strategic decision-making, enhance operational efficiency, and improve customer experiences.

However, extracting meaningful insights from big data requires advanced analytical techniques and tools. This is where business analytics plays a pivotal role. Business analytics involves the exploration, analysis, and interpretation of data to gain actionable insights and support decision-making processes. It encompasses descriptive analytics, which focuses on summarizing historical data; predictive analytics, which uses statistical modelling and machine learning to forecast future outcomes; and prescriptive analytics, which provides recommendation to optimize decision-making. Business analytics empowers organizations and gain and competitive advantage.

Business Analytics - Foundations and Methodologies

Business analytics refers to the practice of using data, statistical analysis, quantitative methods, and predictive models to extract insights, drive informed decision-making, and improve business performance. It involves the systematic exploration and interpretation of data to uncover patterns, relationships, and trends that can guide strategic and operational decision-making processes. Business analytics encompasses a range of techniques and methodologies, including data mining, statistical analysis, data visualisation, predictive modelling, and optimization.

The primary goal of business analytics is to transform raw data into meaningful information that can be used to gain and competitive advantage, identify opportunities, mitigate risks, enhance operational efficiency, and improve overall organisational performance. By leveraging data-driven insights, organizations can make data-informed decisions, optimize processes, and align their strategies with market demands and customer preferences. The scope of business analytics is vast and encompasses various domains in functional areas within organizations. Some of the key areas where business analytics is applied include -

Marketing Analytics

Marketing analytics focuses on analysing customer behaviour, preferences, and market trends to optimize marketing strategies and improve customer acquisition, retention, and satisfaction. It involves analysing customer segmentation, pricing optimization, campaign effectiveness, customer lifetime value, and market forecasting.

Operations Analytics

Operations analytics involves analysing and operational processes, supply chains, and production systems to enhance efficiency, reduce costs, and improve quality. It includes optimisation of inventory management, demand forecasting, production planning, and logistics optimization.

Financial Analytics

Financial analytics focuses on analysing financial data and market trends to make informed investment decisions, maange risk, and optimise financial performance. It includes financial forecasting, portfolio optimization, credit risk assessment, fraud detection, and compliance monitoring.

Human Resources Analytics

Human resources analytics involves leveraging data to optimise workforce management, talent acquisition, performance evaluation, and employee engagement. It includes analysing employee demographics, skills, gaps, attrition patterns, and performance metrics to support effective hr decision-making.

Risk Analytics

Risk analytics aims to identify, assess, and mitigate potential risks and vulnerabilities in business operations. It involves analysing historical data, market trends, and external factors to predict and manage risks related to fraud, cybersecurity, regulatory compliance, and supply chain disruptions.

The scope of business analytics continues to evolve as technology advancements, such as artificial intelligence and machine learning, enable more sophisticated data analysis and prediction capabilities. This enables organizations to delve deeper into complex data sets, extract deeper insights, and generate more accurate predictions.

Descriptive, Predictive, and Prescriptive Analytics

Descriptive Analytics

Descriptive Analytics focuses on understanding and summarizing historical data to gain insights into past events and trends. It involves the examination of data to answer questions such as, ‘what happened?’ and ‘what is the current state of affairs?’ descriptive analytics helps organizations gain a comprehensive understanding of the business operations, customer behaviour, and market trends. Descriptive analytics techniques include data aggregation, data visualisation, and summary statistics. These techniques enable organizations to organise and present data in a meaningful way, facilitating easier interpretation and analysis. For instance, data visualisation tools like charts, graphs, and dashboards help stakeholders identify patterns, trends, and outliers within their data. Through descriptive analytics, organizations can uncover valuable information about their historical performance, customer preferences, and operational efficiency.

By leveraging descriptive analytics, organizations can make data-driven decisions based on a solid understanding of past events. For example, a retail company may analyse historical sales data to identify the best selling products, peak sales periods, and customer preferences. This information can then be used to optimize inventory management, marketing strategies, and product development efforts.

Predictive Analytics

Predictive analytics aims to forecast future outcomes based on historical data patterns and statistical modelling. It leverages advanced techniques and algorithms to identify relationships between variables and make predictions about future events. Predictive analytics answer questions like, ‘what is likely to happen?’ and ‘what is the probability of a specific outcome occurring?’. Predictive analytics utilises machine learning algorithms, statistical modelling, and data mining techniques to analyse historical data and identify patterns or trends. These patterns are then used to build predictive models that can be applied to new data to generate predictions or forecasts. The accuracy of predictive methods improves with the availability of more data and the refinement of algorithms. Organizations can use predictive analytics to optimize decision-making, mitigate risks, and seize opportunities. For example, a bank can employ predictive analytics to assess the creditworthiness of loan applicants. By analysing historical data, such as credit scores, income levels, and payment histories, the bank can build a predictive model that estimated the likelihood of loan default. This information enables the bank to make informed decisions regarding loan approvals, interest rates, and risk mitigation strategies.

Prescriptive Analytics

Prescriptive analytics goes beyond descriptive and predictive analytics by providing recommendations and prescribing actions to optimize outcomes. It leverages historical and real-time data, predictive models, optimization algorithms, and business rules to identify the best course of action to achieve desired goals. Prescriptive analytics answers the question, ‘what should be done to achieve the best outcome?’. Prescriptive analytics considers various constraints, objectives, and scenarios to provide decision-makers actionable insights. It takes into account the potential impact of different decisions and recommends the optimal actions to maximise desired outcomes or minimise risks. Prescriptive analytics techniques include optimization models, simulation, and decision analysis. Organizations can use prescriptive analytics to optimize resource allocation, strategic planning, and operational efficiency. For example, a logistics company can utilise prescriptive analytics to determine the most efficient routes for deliveries, considering factors such as traffic conditions, delivery deadlines, and vehicle capacities. By prescribing optimal routes, the company can reduce fuel costs, improve customer satisfaction, and streamline its operations.

In the next part of this blog series we will look at the key methodologies like data mining, statistical analysis, and data visualisation.

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Agrawal, R., & Srikant, R. (1994). Fast Algorithms for Mining Association Rules in Large Databases Proceedings of the 20th International Conference on Very Large Data Bases, Aliahmadi, A., Nozari, H., & Ghahremani-Nahr, J. (2022a). AIoT-based Sustainable Smart Supply Chain Framework. International Journal of Innovation in Management, Economics and Social Sciences, 2(2), 28-38. https://doi.org/10.52547/ijimes.2.2.28

Aliahmadi, A., Nozari, H., & Ghahremani-Nahr, J. (2022b). Big Data IoT-based Agile-Lean Logistic in Pharmaceutical Industries. International Journal of Innovation in Management, Economics and Social Sciences, 2(3), 70-81. https://doi.org/10.52547/ijimes.2.3.70

Alnoukari, M. (2022). From Business Intelligence to Big Data: The Power of Analytics. In I. R. Management Association (Ed.), Research Anthology on Big Data Analytics, Architectures, and Applications (pp. 823-841). IGI Global. https://doi.org/10.4018/978-1-6684-3662-2.ch038

Barocas, S., & Selbst, A. D. (2016). Big Data's Disparate Impact. California Law Review, 104(3), 671-732. http://www.jstor.org/stable/24758720

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82-115. https://doi.org/https://doi.org/10.1016/j.inffus.2019.12.012

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. https://books.google.com/books?id=qWPwnQEACAAJ

Bojarski, M., Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., & Zieba, K. (2016). End to End Learning for Self-Driving Cars. https://doi.org/https://doi.org/10.48550/arXiv.1604.07316

Bostock, M., Ogievetsky, V., & Heer, J. (2011). D3 Data-Driven Documents. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2301-2309. https://doi.org/10.1109/TVCG.2011.185

Brose, M. S., Flood, M. D., Krishna, D., & Nichols, B. (2014). Handbook of Financial Data and Risk Information I: Principles and Context (M. S. Brose, M. D. Flood, D. Krishna, & B. Nichols, Eds. Vol. 1). Cambridge University Press. https://doi.org/DOI: 10.1017/CBO9780511997723

Bruun, E. P. G., & Duka, A. (2018). Artificial Intelligence, Jobs and the Future of Work: Racing with the Machines. Basic Income Studies, 13(2). https://doi.org/doi:10.1515/bis-2018-0018

Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review,, 95(1), 56-66. https://hbr.org/2017/07/the-business-of-artificial-intelligence

Bughin, J., Chui, M., & Manyika, J. (2018). Notes from the AI frontier: Insights from hundreds of use cases. M. G. Institute. Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183-186. https://doi.org/10.1126/science.aal4230

Campbell, C., Sands, S., Ferraro, C., Tsao, H.-Y., & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business Horizons, 63(2), 227-243. https://doi.org/https://doi.org/10.1016/j.bushor.2019.12.002

Cavoukian, A. (2018). Privacy by design: The 7 foundational principles. https://iapp.org/media/pdf/knowledge_center/Privacy-by-Design-The-7-Fundamental-Principles.pdf

Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188. https://doi.org/10.2307/41703503

Chen, I. Y., Johansson, F. D., & Sontag, D. A. (2018). Why Is My Classifier Discriminatory? Neural Information Processing Systems, Chen, P.-T., Lin, C.-L., & Wu, W.-N. (2020). Big data management in healthcare: Adoption challenges and implications. International Journal of Information Management, 53, 102078. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2020.102078

Chen, X., Kundu, K., Zhu, Y., Berneshawi, A., Ma, H., Fidler, S., & Urtasun, R. (2015). 3D object proposals for accurate object class detection Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, Montreal, Canada. Cohen, M. C. (2018). Big Data and Service Operations. Production and Operations Management, 27(9), 1709-1723. https://doi.org/https://doi.org/10.1111/poms.12832

Corbett-Davies, S., & Goel, S. (2018). The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning. ArXiv, abs/1808.00023. Davenport, T. H., & Harris, J. G. (2007). Competing on analytics. Harvard Business Review, 85(1), 98-107. https://hbr.org/2006/01/competing-on-analytics

Davenport, T. H., & Patil, D. J. (2012). Data Scientist: The Sexiest Job of the 21st Century Harvard Business Review, 90(10), 70-76. https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century Delen, D., & Ram, S. (2018). Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), 2-12. https://doi.org/10.1080/2573234X.2018.1507324

Demajo, L., Vella, V., & Dingli, A. (2020). Explainable AI for Interpretable Credit Scoring. https://doi.org/10.5121/csit.2020.101516

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. North American Chapter of the Association for Computational Linguistics, Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Fosso Wamba, S., Giannakis, M., & Foropon, C. (2019). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210, 120-136. https://doi.org/https://doi.org/10.1016/j.ijpe.2019.01.023

European-Union. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). . L. Official Journal of the European Union. http://data.europa.eu/eli/reg/2016/679/oj

Fan, W., & Geerts, F. (2012). Foundations of Data Quality Management. https://doi.org/10.1007/978-3-031-01892-3

Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. https://books.google.com/books?id=3C7QAAAAMAAJ

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2021). An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. In L. Floridi (Ed.), Ethics, Governance, and Policies in Artificial Intelligence (pp. 19-39). Springer International Publishing. https://doi.org/10.1007/978-3-030-81907-1_3

Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. https://doi.org/https://doi.org/10.1016/j.ijpe.2014.12.031

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280. https://doi.org/https://doi.org/10.1016/j.techfore.2016.08.019

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2014.10.007

Gauss, C. F., & Gottingensis, S. R. S. (1821). Theoria combinationis observationum: erroribus minimis obnoxiae. https://books.google.com/books?id=4BrCswEACAAJ

Ghahremani nahr, J., Nozari, H., & Sadeghi, M. E. (2021a). Artificial intelligence and Machine Learning for Real- world problems (A survey). International Journal of Innovation in Engineering, 1(3), 38-47. https://doi.org/10.59615/ijie.1.3.38

Ghahremani Nahr, J., Nozari, H., & Sadeghi, M. E. (2021b). Green supply chain based on artificial intelligence of things (AIoT). International Journal of Innovation in Management, Economics and Social Sciences, 1(2), 56-63. https://doi.org/10.52547/ijimes.1.2.56

Goodman, B., & Flaxman, S. (2017). European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”. AI Magazine, 38(3), 50-57. https://doi.org/https://doi.org/10.1609/aimag.v38i3.2741

hajiaghajani, A. (2023). Concepts and applications of data mining and analysis of social networks. Journal of Data Analytics, 2(1), 1-8. https://doi.org/10.59615/jda.2.1.1

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer New York. https://books.google.com/books?id=tVIjmNS3Ob8C

He, K., Zhang, X., Ren, S., & Sun, J. (2016, 27-30 June 2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer New York. https://books.google.com/books?id=qcI_AAAAQBAJ

Jędrzejka, D. (2019). Robotic process automation and its impact on accounting. Zeszyty Teoretyczne Rachunkowości, 2019, 137-166. https://doi.org/10.5604/01.3001.0013.6061

John, A., & Latha, T. (2023). Stock market prediction based on deep hybrid RNN model and sentiment analysis. Automatika, 64(4), 981-995. https://doi.org/10.1080/00051144.2023.2217602

Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 70(1), 71-79. https://hbr.org/1992/01/the-balanced-scorecard-measures-that-drive-performance-2

Kavanagh, M. J., Thite, M., & Johnson, R. D. (2011). Human Resource Information Systems: Basics, Applications, and Future Directions: Basics, Applications, and Future Directions. SAGE Publications. https://books.google.com/books?id=yDe-rMyZSbkC

Kian, R. (2021). Provide a model for an e-commerce system with the impact of artificial intelligence. International Journal of Innovation in Management, Economics and Social Sciences, 1(3), 88-94. https://doi.org/10.52547/ijimes.1.3.88

Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 1746–1751. https://doi.org/10.3115/v1/D14-1181

Kober, J., Bagnell, J. A., & Peters, J. (2013). Reinforcement learning in robotics: A survey. The International Journal of Robotics Research, 32(11), 1238-1274. https://doi.org/10.1177/0278364913495721

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In F. Pereira, C. J. Burges, L. Bottou, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems (Vol. 25). Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), 700-710. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2016.04.013

Lavalle, S., Lesser, E., Shockley, R., Hopkins, M., & Kruschwitz, N. (2011). Big Data, Analytics and the Path From Insights to Value. MIT Sloan Management Review, 52, 21-32. https://sloanreview.mit.edu/article/big-data-analytics- and-the-path-from-insights-to-value/

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539

Lee, I., & Mangalaraj, G. (2022). Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions. Big Data and Cognitive Computing, 6(1), 17. https://www.mdpi.com/2504-2289/6/1/17

Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2015). End-to-End Training of Deep Visuomotor Policies. Journal of Machine Learning Research, 17(39), 1-40. http://jmlr.org/papers/v17/15-522.html

Li, L., Chi, T., Hao, T., & Yu, T. (2018). Customer demand analysis of the electronic commerce supply chain using Big Data. Annals of Operations Research, 268(1), 113-128. https://doi.org/10.1007/s10479-016-2342-x

Liebowitz, J. (2013). Business Analytics: An Introduction (1st ed.). CRC Press. https://doi.org/https://doi.org/10.1201/b16246

Lipton, Z. C. (2018). The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3), 31–57. https://doi.org/10.1145/3236386.3241340

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In L. M. L. Cam & J. Neyman (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. University of California Press. https://books.google.com/books?id=IC4Ku_7dBFUC

Maleki, R., & Sabet, E. (2022). Business Intelligence Analysis in Small and Medium Enterprises. International journal of Innovation in Marketing Elements, 2(1), 1-11. https://doi.org/10.59615/ijime.2.1.1

McCarthy, J., Minsky, M., Shannon, C. E., Rochester, N., & Dartmouth, C. (1955). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Merchán, D., Arora, J., Pachon, J., Konduri, K., Winkenbach, M., Parks, S., & Noszek, J. (2022). 2021 Amazon Last Mile Routing Research Challenge: Data Set. Transportation Science. https://doi.org/10.1287/trsc.2022.1173

Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Scientific Reports, 6(1), 26094. https://doi.org/10.1038/srep26094

Mirowski, P., Pascanu, R., Viola, F., Soyer, H., Ballard, A., Banino, A., Denil, M., Goroshin, R., Sifre, L., Kavukcuoglu, K., Kumaran, D., & Hadsell, R. (2016). Learning to Navigate in Complex Environments. https://doi.org/https://doi.org/10.48550/arXiv.1611.03673

Mitchell, T. M. (1997). Machine Learning. McGraw-Hill Education. https://books.google.com/books?id=xOGAngEACAAJ

Mohseni Kiasari, M., & Fartash, K. (2023). Prioritizing policy tools to support development of IoT technologies in Iran. International Journal of Innovation in Engineering, 3(2), 53-67. https://ijie.ir/index.php/ijie/article/view/121

Mortaji, S. T. H., Noori, S., & Bagherpour, M. (2021a). Adaptive Project Monitoring and Control with Variable Reviewing Intervals. International Journal of Innovation in Engineering, 1(4), 34-46. https://doi.org/10.59615/ijie.1.4.34

Mortaji, S. T. H., Noori, S., & Bagherpour, M. (2021b). Directed earned value management based on ordered fuzzy numbers. Journal of Intelligent & Fuzzy Systems, 40, 10183-10196. https://doi.org/10.3233/JIFS-201248

Mortaji, S. T. H., Noori, S., Noorossana, R., & Bagherpour, M. (2018). An ex ante control chart for project monitoring using earned duration management observations. Journal of Industrial Engineering International, 14(4), 793-806. https://doi.org/10.1007/s40092-017-0251-5

Mortaji, S. T. H., Noorossana, R., & Bagherpour, M. (2015). Project Completion Time and Cost Prediction Using Change Point Analysis. Journal of Management in Engineering, 31(5), 04014086. https://doi.org/doi:10.1061/(ASCE)ME.1943-5479.0000329

Moslemi, S. (2021). Examining the Dimensions of Big Data Privacy (Block Chain Solution for Privacy Protection). International Journal of Innovation in Management, Economics and Social Sciences, 1(4), 10-16. https://doi.org/10.52547/ijimes.1.4.10

Mousakhani, M., Saghafi, F., Hasanzadeh, M., & Sadeghi, M. E. (2020). Proposing Dynamic Model of Functional Interactions of IoT Technological Innovation System by Using System Dynamics and Fuzzy DEMATEL [Research]. Journal of Operational Research and Its Applications, 17(4), 1-21. http://jamlu.liau.ac.ir/article-1-1869-en.html

nazari, E., Edalati Khodabandeh, M., Dadashi, A., Aldaghi, T., Rasoulian, M., & Tabesh, H. (2022). Studying Students' Knowledge of the Benefits, Challenges, and Applications of Big Data Analytics in Healthcare. International Journal of Innovation in Engineering, 2(1), 40-57. https://doi.org/10.59615/ijie.2.1.40

Nilsson, N. J. (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann Publishers. https://books.google.com/books?id=GYOFSd6fETgC

Nozari, H., Ghahremani-Nahr, J., Fallah, M., & Szmelter-jarosz, A. (2022). Assessment of Cyber Risks in an IoT- based Supply Chain using a Fuzzy Decision-Making Method. International Journal of Innovation in Management, Economics and Social Sciences, 2(1), 52-64. https://doi.org/10.52547/ijimes.2.1.52

Nozari, H., Sadeghi, M. E., & Najafi, S. E. (2022). Quantitative Analysis of Implementation Challenges of IoT-Based Digital Supply Chain (Supply Chain 0/4). arXiv preprint arXiv:2206.12277.

Obaid, H. S. (2022). Review the challenges of using big data in the supply chain. Journal of Data Analytics, 1(1), 16-24. https://doi.org/10.59615/jda.1.1.16

Popat, R. R., & Chaudhary, J. (2018, 11-12 May 2018). A Survey on Credit Card Fraud Detection Using Machine Learning. 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), Power, D. (2002). Decision Support Systems: Concepts and Resources for Managers. Bloomsbury Academic. https://books.google.com/books?id=9NA6QMcte3cC

Prat, N. (2019). Augmented Analytics. Business & Information Systems Engineering, 61(3), 375-380. https://doi.org/10.1007/s12599-019-00589-0 Pumsirirat, A., & Yan, L. (2018). Credit Card Fraud Detection using Deep Learning based on Auto-Encoder and Restricted Boltzmann Machine. International Journal of Advanced Computer Science and Applications, 9(1). https://doi.org/http://dx.doi.org/10.14569/IJACSA.2018.090103

Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Elsevier Science. https://doi.org/https://doi.org/10.1016/C2009-0-27846-9

Rahmaty, M. (2023). Machine learning with big data to solve real-world problems. Journal of Data Analytics, 2(1), 9- 16. https://doi.org/10.59615/jda.2.1.9

Rajaraman, A., & Ullman, J. D. (2011). Mining of Massive Datasets. Cambridge University Press. https://doi.org/DOI: 10.1017/CBO9781139058452

Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., Patel, B. N., Yeom, K. W., Shpanskaya, K., Blankenberg, F. G., Seekins, J., Amrhein, T. J., Mong, D. A., Halabi, S. S., Zucker, E. J., . . . Lungren, M. P. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLOS Medicine, 15(11), e1002686. https://doi.org/10.1371/journal.pmed.1002686

Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review, 59(1). Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: towards real-time object detection with region proposal networks Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, Montreal, Canada. Roman, R., Lopez, J., & Mambo, M. (2018). Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems, 78, 680-698. https://doi.org/https://doi.org/10.1016/j.future.2016.11.009

Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. CreateSpace Independent Publishing Platform. https://books.google.com/books?id=PQI7vgAACAAJ

Sabet, E. (2021). Reviewing the Challenges of Big Data Use in Smart Industries. International Journal of Innovation in Management, Economics and Social Sciences, 1(4), 66-72. https://doi.org/10.52547/ijimes.1.4.66

Sadeghi, M. E. (2022). Big data based on IoT in the agriculture industry: developments, opportunities, and challenges ahead. Journal of Data Analytics, 1(1), 25-32. https://doi.org/10.59615/jda.1.1.25

Sadeghi, M. E., & Jafari, H. (2021). Investigating the dimensions, components and key indicators of supply chain management based on digital technologies. International Journal of Innovation in Management, Economics and Social Sciences, 1(3), 82-87. https://doi.org/10.52547/ijimes.1.3.82

Samadi-Parviznejad, P. (2022). The role of big data in digital transformation. Journal of Data Analytics, 1(1), 42-47. https://doi.org/10.59615/jda.1.1.42

Schroff, F., Kalenichenko, D., & Philbin, J. (2015, 7-12 June 2015). FaceNet: A unified embedding for face recognition and clustering. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Shahvaroughi Farahani, M., & Esfahani, A. (2022). Opportunities and Challenges of Applying Artificial Intelligence in the Financial Sectors and Startups during the Coronavirus Outbreak. International Journal of Innovation in Management, Economics and Social Sciences, 2(4), 33-55. https://doi.org/10.52547/ijimes.2.4.33

Shahvaroughi Farahani, M., Esfahani, A., Nejad Falatouri Moghaddam, M., & Ramezani, A. (2022). The Impact of Fintech and Artificial Intelligence on COVID 19 and Sustainable Development Goals. International Journal of Innovation in Management, Economics and Social Sciences, 2(3), 14-31. https://doi.org/10.52547/ijimes.2.3.14

Sharda, R., Turban, E., Delen, D., Aronson, J. E., Liang, T. P., & King, D. (2014). Business Intelligence and Analytics: Systems for Decision Support. Pearson. https://books.google.com/books?id=FLYDnwEACAAJ

Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. Souza, R. M., Nascimento, E. G. S., Miranda, U. A., Silva, W. J. D., & Lepikson, H. A. (2021). Deep learning for diagnosis and classification of faults in industrial rotating machinery. Computers & Industrial Engineering, 153, 107060. https://doi.org/https://doi.org/10.1016/j.cie.2020.107060

Student. (1908). The Probable Error of a Mean. Biometrika, 6(1), 1-25. https://doi.org/10.2307/2331554

Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks

Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning, second edition: An Introduction. MIT Press. https://books.google.com/books?id=sWV0DwAAQBAJ

Tufte, E. R. (1983). The Visual Display of Quantitative Information. Graphics Press. https://books.google.com/books?id=SqVpAAAAMAAJ

Vaswani, A., Shazeer, N. M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is All you Need. Neural Information Processing Systems, Vergara, J. R., & Estévez, P. A. (2014). A review of feature selection methods based on mutual information. Neural Computing and Applications, 24(1), 175-186. https://doi.org/10.1007/s00521-013-1368-0

Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From Multi-Channel Retailing to Omni-Channel Retailing: Introduction to the Special Issue on Multi-Channel Retailing. Journal of Retailing, 91(2), 174-181. https://doi.org/https://doi.org/10.1016/j.jretai.2015.02.005

World-Economic-Forum. (2020). The future of jobs report 2020. W. E. Forum. http://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf

Zarsky, T. Z. (2019). Privacy and Manipulation in the Digital Age. Theoretical Inquiries in Law, 20(1), 157-188. https://doi.org/doi:10.1515/til-2019-0006

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