Business intelligence : from conventional to cognitive

Technological systems enhance organizations since 1958 and are the ground basis of a strong managerial operation in today´s business competition. Based on a literature review that identifies past, present and future applications of technology from business intelligence to artificial intelligence. Th...

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Autor Principal: Ramírez Linares, Andrés Felipe
Otros Autores: Gómez-Cruz, Nelson Alfonso
Formato: Trabajo de grado (Bachelor Thesis)
Lenguaje:Español (Spanish)
Publicado: Universidad del Rosario 2019
Materias:
Acceso en línea:http://repository.urosario.edu.co/handle/10336/19040
id ir-10336-19040
recordtype dspace
institution EdocUR - Universidad del Rosario
collection DSpace
language Español (Spanish)
topic Business intelligence
Analytics
Cognitive
Internet of things
Machine learning
Artificial intelligence
Conocimiento
Inteligencia artificial
Internet de las cosas
Aprendizaje automático (Inteligencia artificial)
Business Intelligence
Analytics
Cognitive
Internet of things
Machine learning
Artificial intelligence
spellingShingle Business intelligence
Analytics
Cognitive
Internet of things
Machine learning
Artificial intelligence
Conocimiento
Inteligencia artificial
Internet de las cosas
Aprendizaje automático (Inteligencia artificial)
Business Intelligence
Analytics
Cognitive
Internet of things
Machine learning
Artificial intelligence
Ramírez Linares, Andrés Felipe
Business intelligence : from conventional to cognitive
description Technological systems enhance organizations since 1958 and are the ground basis of a strong managerial operation in today´s business competition. Based on a literature review that identifies past, present and future applications of technology from business intelligence to artificial intelligence. This article offers an understanding of which technological advances are applied in organizations to adapt and survive within an ever-changing environment in business world today. Business intelligence´s definition and key divisions are described to carry on a wide explanation due to its scope. Based in a state-of-the-art literature revision and going through several definitions, BI it is analyzed as a process and as technological aid. From key divisions in its application such as: reporting, analysis, monitoring and prediction to its extensions based on time frames in operational and strategic bids. BI is the starting point to excel why having a decision support making tool is key to hedge the risk from failure to be an outstanding tool to increase profits. How can systems create for themselves prediction modules that optimize and later adapt to future scenarios based on historic data and how its adaptivity is key. Therefore, new technologies are emerging at a neck breaking speed. Hence, this article explains and help to understand their scope and importance within the world we live in and why companies must innovate and cope with them when building their industry to new horizons. Internet of things, machine learning and artificial intelligence are the new emerging and disruptive technologies that are being implemented in all industries creating new trends and challenges to manage.
author2 Gómez-Cruz, Nelson Alfonso
author_facet Gómez-Cruz, Nelson Alfonso
Ramírez Linares, Andrés Felipe
format Trabajo de grado (Bachelor Thesis)
author Ramírez Linares, Andrés Felipe
author_sort Ramírez Linares, Andrés Felipe
title Business intelligence : from conventional to cognitive
title_short Business intelligence : from conventional to cognitive
title_full Business intelligence : from conventional to cognitive
title_fullStr Business intelligence : from conventional to cognitive
title_full_unstemmed Business intelligence : from conventional to cognitive
title_sort business intelligence : from conventional to cognitive
publisher Universidad del Rosario
publishDate 2019
url http://repository.urosario.edu.co/handle/10336/19040
_version_ 1645142228857782272
spelling ir-10336-190402019-09-19T12:37:54Z Business intelligence : from conventional to cognitive Ramírez Linares, Andrés Felipe Gómez-Cruz, Nelson Alfonso Business intelligence Analytics Cognitive Internet of things Machine learning Artificial intelligence Conocimiento Inteligencia artificial Internet de las cosas Aprendizaje automático (Inteligencia artificial) Business Intelligence Analytics Cognitive Internet of things Machine learning Artificial intelligence Technological systems enhance organizations since 1958 and are the ground basis of a strong managerial operation in today´s business competition. Based on a literature review that identifies past, present and future applications of technology from business intelligence to artificial intelligence. This article offers an understanding of which technological advances are applied in organizations to adapt and survive within an ever-changing environment in business world today. Business intelligence´s definition and key divisions are described to carry on a wide explanation due to its scope. Based in a state-of-the-art literature revision and going through several definitions, BI it is analyzed as a process and as technological aid. From key divisions in its application such as: reporting, analysis, monitoring and prediction to its extensions based on time frames in operational and strategic bids. BI is the starting point to excel why having a decision support making tool is key to hedge the risk from failure to be an outstanding tool to increase profits. How can systems create for themselves prediction modules that optimize and later adapt to future scenarios based on historic data and how its adaptivity is key. Therefore, new technologies are emerging at a neck breaking speed. Hence, this article explains and help to understand their scope and importance within the world we live in and why companies must innovate and cope with them when building their industry to new horizons. Internet of things, machine learning and artificial intelligence are the new emerging and disruptive technologies that are being implemented in all industries creating new trends and challenges to manage. Technological systems enhance organizations since 1958 and are the ground basis of a strong managerial operation in today´s business competition. Based on a literature review that identifies past, present and future applications of technology from business intelligence to artificial intelligence. This article offers an understanding of which technological advances are applied in organizations to adapt and survive within an ever-changing environment in business world today. Business intelligence´s definition and key divisions are described to carry on a wide explanation due to its scope. Based in a state-of-the-art literature revision and going through several definitions, BI it is analyzed as a process and as technological aid. From key divisions in its application such as: reporting, analysis, monitoring and prediction to its extensions based on time frames in operational and strategic bids. BI is the starting point to excel why having a decision support making tool is key to hedge the risk from failure to be an outstanding tool to increase profits. How can systems create for themselves prediction modules that optimize and later adapt to future scenarios based on historic data and how its adaptivity is key. Therefore, new technologies are emerging at a neck breaking speed. Hence, this article explains and help to understand their scope and importance within the world we live in and why companies must innovate and cope with them when building their industry to new horizons. Internet of things, machine learning and artificial intelligence are the new emerging and disruptive technologies that are being implemented in all industries creating new trends and challenges to manage. 2019-02-07 2019-02-11T20:41:43Z info:eu-repo/semantics/bachelorThesis info:eu-repo/semantics/acceptedVersion http://repository.urosario.edu.co/handle/10336/19040 spa Atribución-NoComercial-SinDerivadas 2.5 Colombia http://creativecommons.org/licenses/by-nc-nd/2.5/co/ info:eu-repo/semantics/openAccess application/pdf Universidad del Rosario Administrador de negocios internacionales Facultad de administración instname:Universidad del Rosario reponame:Repositorio Institucional EdocUR Abdelkerim Rezgui, R. B. (2016). Un système d’évaluation de l’impact des décisions pour la Business Intelligence Adaptative. Ingeniere des systems d`information , 21, 103-124. AI, P. (2018). Obtenido de https://www.partnershiponai.org/about/ Alphaydin, E. (2010). Introduction to machine learning. London: The MIT press. Anderson, S. L. (2008). Asimov’s ‘‘three laws of robotics’’ and machine. AI & Soc, 477–493. Apex. (November de 2018). Obtenido de https://www.apex.com/four-main-types-bi/ Atzori, L., Iera, A., & Morabito, G. (14 de May de 2010). The Internet of Things: A survey. Computer Networks, 2787–2805. Banafa, A. (14 de March de 2017). Three Major Challenges Facing IoT. Obtenido de https://iot.ieee.org/newsletter/march-2017/three-major-challenges-facing-iot.html Bostrom, N. (2003). Ethical Issues in Advanced Artificial Intelligence. Oxford: Oxford University. Brown, J., Cuzzocrea, A., Kresta, M., Kristjanson, K., Leung, C., & Tebinka, T. ( 2018). A machine learning tool for supporting advanced knowledge discovery from chess game data. 16th IEEE International Conference on Machine Learning and Applications, (págs. 649-654). Cancun. Cambridge, D. (18 de November de 2018). https://dictionary.cambridge.org. Obtenido de https://dictionary.cambridge.org/dictionary/english/data Cambridge. (2018). Obtenido de https://dictionary.cambridge.org/dictionary/english-spanish/artificial Cambridge. (2018). Obtenido de https://dictionary.cambridge.org/dictionary/english-spanish/intelligence Campbell, M., Jr, A. H., & Feng-hsiung, b. (2002). Deep Blue. Artificial Intelligence, 134, 57-83. Obtenido de https://doi.org/10.1016/S0004-3702(01)00129-1 Chen, Y., Argentinis, E., & Weber, G. (2016). IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research. Clinical Therapeutics, 38, 688-701. Obtenido de https://doi.org/10.1016/j.clinthera.2015.12.001 Chiang, R. H., Chen, H.-c., & Storey, V. C. (2010). Business Intelligence Research. Minnesota: MIS Quarterly CNN. (2018 de January de 2016). Why Elon Musk is worried about artificial intelligence. Obtenido de https://www.youtube.com/watch?v=US95slMMQis Daffodil. (30 de July de 2017). 9 Applications of Machine Learning from Day-to-Day Life. Obtenido de https://medium.com/app-affairs/9-applications-of-machine-learning-from-day-to-day-life-112a47a429d0 DataRobot. (2018). Unsupervised Machine Learning. Obtenido de https://www.datarobot.com/wiki/unsupervised-machine-learning/ Devi, S., & Kalia, D. A. (2015). Study of Data Cleaning & Comparison of Data Cleaning Tools. International Journal of Computer Science and Mobile Computing, 4(3), 360-370. Devi, S., & Kalia, D. A. (2015). Study of Data Cleaning & Comparison of Data Cleaning Tools. International Journal of Computer Science and Mobile Computing, 4(3), 360-370. Dietrich, D., Heller, B., & Yang, B. (2015). Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. Indianapolis, United States of America: John Wiley & Sons, Inc. Enciclopedia. (2018). Artificial Intelligence. Obtenido de https://www.encyclopedia.com/science-and-technology/computers-and-electrical-engineering/computers-and-computing/artificial-intelligence Erb, B. (2016). Artificial Intelligence & Theory of Mind. Gandhi, N., & Armstrong, L. J. (2016). A review of the application of data mining techniques for decision making in agriculture. (IEEE, Ed.) 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), 2. Gartner. (2 de April de 2012). Obtenido de https://www.gartner.com/newsroom/id/1971516 Gartner. (2018). IT Glossary. Obtenido de https://www.gartner.com/it-glossary/big-data/ Gartner. (3 de February de 2016). Obtenido de https://www.gartner.com/newsroom/id/3198917 Geotab. (25 de May de 2018). 6 Steps for Data Cleaning and Why it Matters. Obtenido de https://www.geotab.com/blog/data-cleaning/ Giorgio, P., Marzin, K., Lee, S., & Vonderhaar, M. (2018). Internet of Things (IoT): Bringing IoT to Sports Analytics, Player Safety, and Fan. Deloitte Development LLC. GN, C. K. (31 de August de 2018). Artificial Intelligence: Definition, Types, Examples, Technologies. Obtenido de https://medium.com/@chethankumargn/artificial-intelligence-definition-types-examples-technologies-962ea75c7b9b Golfarelli, M., Rizzi, S., & Cella, I. (2004). Beyond Data Warehousing: What’s Next in Business Intelligence? . Proceedings of the 7th ACM international, 1. Gorbea, P. S., & Madera, J. M. (Agosto de 2017). Diseño de un data warehouse para medir el desarrollo disciplinar en instituciones académicas. INVESTIGACIÓN BIBLIOTECOLÓGICA, 31(72), 161-181. Obtenido de http://rev-ib.unam.mx/ib/index.php/ib/article/view/57828 H.Witten, I., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. San Francisco: Morgan Kaufman Publishers. Hassan, M., El Desouky, A., Elghamrawy, S., & Sarhan, A. (2019). A Hybrid Real-time remote monitoring framework with NB-WOA algorithm for patients with chronic diseases. Future Generation Computer Systems, 77-95. Hazen, B. T., Boone, C. A., Ezell, J. D., & AllisonJones-Farmer, L. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80. Heltzel, P. (12 de February de 2018). www.cio.com. Obtenido de https://www.cio.com/article/3254744/emerging-technology/technologies-that-will-disrupt-business.html Hintzed, A. (14 de November de 2016). Understanding the Four Types of Artificial Intelligence. Obtenido de http://www.govtech.com/computing/Understanding-the-Four-Types-of-Artificial-Intelligence.html Hitachi. (26 de Junio de 2014). What is Business Intelligence (BI). Toronto, Canada. Obtenido de https://www.youtube.com/watch?v=hDJdkcdG1iA Hougland, B. (17 de December de 2014). www.tedx.com. Obtenido de https://www.youtube.com/watch?v=_AlcRoqS65E HUGH J. WATSON, B. H.-L. (December de 2009). REAL-TIME BUSINESS INTELLIGENCE: BEST PRACTICES AT CONTINENTAL AIRLINES. EDPACS: The EDP Audit, Control, and Security, 2-17. IBM. (2018). Shifting toward Enterprise-grade AI: Resolving data and skills gaps to realize value. Armonk, NY: IBM Institute for Business Value. IBM. (3 de September de 2015). How It Works: Internet of Things. Obtenido de https://www.youtube.com/watch?v=QSIPNhOiMoE Jones, M., Sidorova, A., & Isk, O. (23 de December de 2012). Business intelligence success: The roles of BI capabilities and decision enviroments. 13-14. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, and prospects. American Association for the Advancement of Science, 249(6245). Kato, S., Ando, M., Kondo, T., Yoshida, Y., Honda, H., & Maruyama, S. (May de 2018). Lifestyle intervention using Internet of Things (IoT) for the elderly: A study protocol for a randomized control trial (the BEST-LIFE study). Nagoya Journal of Med Sci., 175-182. Kopetz, H. (2011). Real-Time Systems: Design Principles for Distributed Embedded Applications. Boston: Springer. Kuhn, M., & Jhonson, K. (2016). Applied Predictive Modeling (Vol. 5). New York: Springer. Lahrmann, G., Marx, F., Winter, R., & Wortmann, F. (2011). Business Intelligence Maturity: Development and Evaluation of a Theoretical Model. (U. o. Gallen, Ed.) 44, 2. Luhn, H. P. (1958). A Business Intelligence System . IMB Journal . Marr, B. (14 de February de 2018). www.forbes.com. Obtenido de https://www.forbes.com/sites/bernardmarr/2018/02/14/the-key-definitions-of-artificial-intelligence-ai-that-explain-its-importance/#e649af04f5d8 Marr, B. (23 de March de 2016). What Everyone Should Know About Cognitive Computing. Obtenido de https://www.forbes.com/sites/bernardmarr/2016/03/23/what-everyone-should-know-about-cognitive-computing/#332913b55088 Michalewicz, Z., Schmidt, M., Michalewicz, M., & Constantine, C. (2010). Adaptive Business Inteligence. Berlin - Heidelberg: Springer. Mostafa, H., Thurow, K., Habil, D. I., Stoll, R., & Habil, D. M. (2017). Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices. Healthc Inform Res, 4-15. Nilsson, N. J. (1996). Book review: Stuart Russell and Peter Norvig, Artijcial Intelligence: A Modem Approach. Artificial Intelligence, 369-380. Ning, H., & Wang, Z. (April de 2011). Future Internet of Things Architecture: Like Mankind Neural System or Social Organization Framework? IEEE COMMUNICATIONS LETTERS, 15(4), 2. Oracle. (2018). Oracle Big Data. Obtenido de https://www.oracle.com/big-data/guide/what-is-big-data.html Paul, F. (26 de November de 2018). Obtenido de https://www.networkworld.com/article/3322517/internet-of-things/a-critical-look-at-gartners-top-10-iot-trends.html PaulaGonzález, M., JesúsLorés, & AntoniGranollers. (2008). Enhancing usability testing through datamining techniques: A novel approach to detecting usability problem patterns for a context of use. Information and Software Technology, 547-568. Peart, A. (22 de June de 2017). www.artificial-solutions.com. Obtenido de https://www.artificial-solutions.com/blog/homage-to-john-mccarthy-the-father-of-artificial-intelligence Pirttimäki, V. (2 de June de 2007). Conceptual analysis of business intelligence. South African Journal Of Information and Management, 9, 2-5. Price, R. (23 de May de 2018). Months after a fatal crash, Uber lays off 300 workers as it pulls its self-driving car tests out of Arizona. Obtenido de Price, R. (23 de May de 2018). Months after a fatal crash, Uber lays off 300 workers as it pulls its self-driving car tests out of Arizona. Obtenido de 33 https://www. Rahm, E., & Do, H. H. (2015). Data Cleaning: Problems and Current Approaches. University of Leipzig. Raona. (4 de September de 2018). Adiós a nuestros problemas gracias al Cognitive Computing. Obtenido de https://www.raona.com/adios-a-nuestros-problemas-gracias-al-cognitive-computing/ Rashed K. Salem, A. S. (14 de March de 2016). Fixing Rules for Data Cleaning based on Conditional. Future Computing and Informatics Journal, 11-15. Richard G. Vedder-, M. T. (1999). Ceo and Cio Perspectives on Competitive Intelligence. Communications of the ACM, 42(8), 108-116. Rubio, J. M., & Crawford, B. (2014). An approach towards the integration of Adaptive Business Intelligent and Constraint Programming . Pontificia universidad catolica del valparaiso, 2. SAP. (2018). Obtenido de https://www.sap.com/latinamerica/products/leonardo.html SAP. (26 de April de 2018). Obtenido de https://www.soapeople.com/blog/6-reasons-why-sap-leonardo-is-the-future-of-intelligent-erp Shah, J., & Mishra, B. (2016). Customized IoT enabled Wireless Sensing and Monitoring Platform. 3rd International Conference on Innovations in Automation and Mechatronics Engineering, (págs. 256 – 263 ). Gandhinaga: VLSI and Embedded Systems Research Group. Shollo, A., & Kautz, K. (2010). Towards an Understanding of Business Intelligence. Australasian Conference on Information Systems. Brisbane, Qeensland Sommer, P. (20 de November de 2017). Obtenido de https://www.ibm.com/blogs/nordic-msp/artificial-intelligence-machine-learning-cognitive-computing/ Soni, D. (22 de March de 2018). Supervised vs. Unsupervised Learning: Understanding the differences between the two main types of machine learning methods. Obtenido de https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d Sparks, O. (11 de Enero de 2017). www.Youtube.com. Obtenido de https://www.youtube.com/watch?v=f_uwKZIAeM0 Stefan Debortoli, M., Müller, D. O., & Brocke, P. D. (2014). Comparing Business Intelligence and Big Data Skills. 5. Su, X. (2018). Introduction to Big Data. Learning material is developed for course IINI3012 Big Data, 2. Surajit Chaudhuri, U. D. (Agosto de 2011). An Overview of business Intelligence Technology. Communications of the acm, 54(8), 88-98. Techopedia. (2018). Obtenido de https://www.techopedia.com/definition/13832/operational-business-intelligence-obi Techopedia. (30 de October de 2018). Obtenido de https://www.techopedia.com/definition/344/business-analytics-ba Techopedia. (December de 2018). www.techopedia.com/. Obtenido de https://www.techopedia.com/definition/3739/algorithm Tegmark, M. (2018). Obtenido de https://futureoflife.org/background/benefits-risks-of-artificial-intelligence/?cn-reloaded=1 Thelwell, R. (2018). www.matillion.com. Obtenido de https://www.matillion.com/insights/5-real-life-applications-of-data-mining-and-business-intelligence/ Thewell, R. (2018). /www.matillion.com. Obtenido de /www.matillion.com: https://www.matillion.com/insights/5-biggest-business-intelligence-challenges/ Triana, J. A., Hernández, C. A., Martínez, A. B., Lista, E. A., & Flórez, L. C. (2013). Business intelligence solution for managing educational resources and physical. AVANCES Investigación en Ingeniería, 10(1), 11. UJ, A. (14 de May de 2018). https://www.analyticsinsight.net. Obtenido de https://www.analyticsinsight.net/what-are-the-two-types-of-business-intelligence/ Viktor Mayer-Schönberger, K. C. (2014). Book Review. En K. C. Viktor Mayer-Schönberger-, Big Data: A Revolution That Will Transform How We Live, Work, and Think (Vol. 179, págs. 1143-1144). Oxford: American Journal of Epidemiology. Weldon, D. (12 de June de 2018). Obtenido de https://www.information-management.com/slideshow/10-predictions-on-advanced-analytics-and-business-intelligence-trends Wixom, B., & Watson, H. (2010). The BI-Based Organization. International Journal of Business Intelligence Research, 14. Wong, M.-H. C.-L. (4 de May de 2011). A review of business intelligence and its maturity models. African Journal of Business Management, 5, 3424-3428. Obtenido de http://www.academicjournals.org/AJBM Yang, S.-H. (2014). Internet of Things. In: Wireless Sensor Networks. Signals and Communication Technology. London: Springer. Zhao, Y., Yu, Y., Li, Y., Han, G., & Du, X. (2018). Machine learning based privacy-preserving fair data trading. Information Sciences, 459.
score 11,15118