Business Intelligence and Big Data in Health Care
In the health care, there are new opportunities to predict and react more promptly to critical clinical events, allowing better care for patients and more effective cost management. Researchers at the University of Buffalo, New York, are using Big Data analytics to improve the quality of life of multiple sclerosis patients, while the University of Ontario Institute of Technology (UOIT) is using IBM Big Data technology to capture and analyze real-time data from medical monitors, alerting hospital staff of potential health problems before patients manifest clinical signs of infection or other issues (IBM, 2013)-
By revealing the genetic origin of illnesses, such as mutations related to cancer, the Human Genome Project, which was completed in 2003, is one project that realizes the promise of Big Data. Consequently, researchers are now embarking on two major efforts, the Human Brain Project and the US BRAIN Initiative, in a quest to construct a supercomputer simulation of the brain’s inner workings, in addition to mapping the activity of about 100 billion neurons in the hope of unlocking answers to Alzheimer’s and Parkinson’s diseases (Michael 8c Miller, 2013)-
Business Intelligence and Big Data in Human Resources Management
The benefits of BI&BD solutions can also be seen in human resources management (Nocker, & Sena, 2019). They enable creating a holistic picture of the employment of staff in a given organization and facilitate the design of employee retention schemes, improving their efficiency and reducing costs.
Big Data tools are suitable for effective monitoring and analyzing of the labor market as well as identifying demand for new competences and skills among employees. Employers, employees, institutions dealing with broadly understood education (high schools, universities, etc.), labor market institutions, and investors are increasingly interested in such research. This issue is becoming increasingly relevant as the number of job seekers online increases.
The most common BI&BD applications in human resources management include the following:
■ Human resources analyses and reports: An integrated overview of data on staff employment becomes possible, especially various analyses regarding their migration and achievements and job separation. Such data may be combined with other information flowing from the labor market, e.g., employment criteria set in a given industry, market demand for specific professional groups.
■ Allocation of workforce: Multidimensional analyses are used to determine the employment level of staff (e.g., in sales departments) in specific regions where demand for specific products and services is expected to increase.
■ Human resources management portal: It is understood as an integrated database of employees, in which information about their competences, salaries, achievements, professional promotions, etc. is stored.
■ Training and career path planning: Precise data on the skills obtained by individual employees can be stored in data warehouses. This is helpful when designing programs to improve their qualifications and planning their career paths (SAS Institute, 2013).
At the end of this chapter, it should be stressed that the use of BI&BD tools requires the organization to operate a specific set of competences combining technical skills with knowledge of information architecture and broad analytical skills. In connection with the aforementioned, there is a need to develop new professions based on classical bibliological and computer science knowledge, extended by highly specialized IT and analytical competences. An example would be professions, such as Big Data analyst, Big Data scientist, or Big Data architect.
References
Alt, R., & Puschmann, T. (2004). Successful practices in customer relationship management.
Proceedings of the 37th Hawaii International Conference on System Science.
Bean, R. (2017). How companies say they’re using big data. Harvard Business Review, April.
Retrieved from https://hbr.org/2017/04/how-companies-say-theyre-using-big-data Big Data in insurance (2020). 21 Big Data insurance companies to know. Retrieved from https://builtin.com/big-data/big-data-insurance.
Buttle, F. (2009). Customer relationship management. Oxford: Butterworth-Heinemann. Chaudhary, S. (2004). Management factors for strategic BI success. In: M.S. Raisinghani (Eds.), Business Intelligence in digital economy. Opportunities, limitations and risks (po. 191-206). Hershey: IGI Global.
Davenport, T. H., Harris, J. G., & Morison, R. (2010). Analytics at work: Smarter decisions, better results. Cambridge: Harvard Business Press.
Deutsche Bank (2014). Big data. How it can become a differentiator. Passion to perform. Deutsche Bank. Global Transaction Banking. Retrieved from https://cib.db.com/ docs_new/GTB_Big_Data_Whitepaper_(DB0324) _v2.pdf.
Fatima, Z. (2018). Big Data and the Logistics Industry. Retrieved from https://www .bbntimes.com/society/big-data-and-the-logistics-industry).
Fjermestad, J., & Romano, N. Jr. (2003). Electronic customer relationship management: Revisiting the general principles of usability and resistance - an integrative implementation framework. Business Process Management Journal, 9(5), 572—591.
Greenberg, P. (2010). CRM at the speed of light. Social CRM Strategies, Tools, and Techniques for Engaging Your Customer. New York: McGraw-Hill.
Greentech Media Inc., SAS. Retrieved from http://www.sas.com/content/dam/SAS/en_us/ doc/whitepaperl/gtmresearch-high-performance-analytics-smart-grid-106115.pdf. 2012.
Gronroos, C. (2000). Service management and marketing — A customer relationship management approach. New York: John Wiley & Sons Ltd.
Gummesson, E. (2002). Relationship marketing in the new economy. Journal of Relationship Marketing, /(1), 37-55.
Halligan, B., & Shah, D. (2010). Inbound marketing get found using google, social media, and blogs. New Jersey: John Wiley & Sons Ltd.
Hassani, H., Huang, X., & Silva, E. (2018). Digitalisation and Big Data mining in banking. Big Data in Cognitive Computing, 2(3) 18; doi:10.3390/bdcc2030018.
Hawking, P, Foster, S., & Stein, A. (2008). Tire adoption and Use of business intelligence solutions in Australia. International Journal of Intelligent Systems Technologies and Applications, 4(1), 327-340.
IBM (2013). Analytics: Tire real-world use of big data in healthcare and life sciences. How innovative healthcare and life sciences organizations extract value from uncertain data. IBM Global Business Services. Business Analytics and Optimization. Executive Report. Oxford: Said Business School at the University of Oxford. Copyright IBM Corporation 2013. Somers, NY: IBMGlobal services. Retrieved from https://www. ibm.com/downloads/cas/KX8N32ZQ.
Januszewski, A. (2008). Funkcjonalnosc informatycznych systemow zarzadzania. Systemy Business Intelligence. Tom 2. Warszawa: Wydawnictwo Naukowe PWN.
Kadayam S. (2007). Business Intelligence from Unstructured Data. Real-Time Marketing Intelligence for Agile Enterprises. Intelliseek, www.intelliseek.com.
Kostojohn, S., Johnson, M., & Paulen, B. (2011). CRM fundamentals. New York: Apress.
Kracklauer, A., Mills, D., & Seifert, D. (2004). Collaborative customer relationship management: taking CRM to the next level. Berlin Heidelberg: Springer-Verlag.
Leeds, D. (2012). High-performance analytics for the smart grid,'White Paper, 2012,
Levitt, T. (1983). After the sale is over. Harvard Business Review, 63(5), 87-93.
Linoff, G. S., & Berry, M. J. A. (2002). Mining the web: Transforming customer data into customer value. New York: Wiley.
Mayer-Schonberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Boston: Houghton Mifflin Harcourt.
McKenn, R. (1991). Relationship marketing: Successful strategies for the age of the customer. Cambridge: MA Perseus Books.
Michael, K., & Miller, K. W. (2013). Big data: New opportunities and new challenges. IEEE Computer, 46(6). Retrieved from http://works.bepress.com/kmichael/344/.
Minna, R., & Aino, H. (2005). Customer knowledge management competence: Towards a theoretical framework. Proceedings of the 38th Hawaii International Conference on System Sciences.
Nocker, M., & Sena, V. (2019). Big data and human resources management: The rise of talent analytics. Social Science,8, 273; doi:10.3390/socsci8100273.
Nykamp, M. (2001). The customer differential: The complete guide to implementing customer relationship management. New York: Amacom.
Olszak, С. M. (2016). Toward better understanding and use of business intelligence in organizations. Information Systems Management, 33(2), 105—123.
Olszak, С. M. (2006). Building and using business intelligence systems in a contemporary organization. Katowice: Publishing House of University of Economics in Katowice.
Olszak С. M., & Bartus, T. (2013). Multi-agent framework for social customer relationship management systems. Issues in Informing Science and Information Technology, Informing Science Institute, 10, 367-387.
Olszak, С. M., & Ziemba, E. (2006). Business intelligence systems in the holistic infrastructure development supporting decision-making in organizations. Interdisciplinary Journal of Information, Knowledge and Management, 1, 47-58.
Pamula, A. (2013). Zaangazowanie odbiorcow z grupy gospodarstw domowych w zarzt{dzaniu popytem na energie. Lodz: Wydawnictwo Uniwersytetu Lodzkiego.
Payne, A., & Frow, P. (2005). A strategic framework for customer relationship management. Journal of Marketing, 69(4), 167—176.
Peppers, D., & Rogers, M. (2011). Managing customer relationships: A strategic framework. New Jersey: John Wiley & Sons Ltd.
Roscoe, D. (2001). The customer knowledge journey. Journal of Database Marketing, 5(4), 314-318.
SAS (2013). Big Data & Utility'’ Analytics for Smart Grid, The Soft Grid 2013-2020, SAS Research Excerpt, White Paper, Greentech Media Inc, Gtmresearch. Retrieved from http://www.sas.com/content/dam/SAS/en_us/doc/analystreport/soft-grid-2013- 2020-big-data-utility-analyticssmart-grid.pdf.
SAS (2020). SAS Telecommunication Solution. Retrieved from https://www.sas.com/en_us/ industry/communications.html.
Schaeffer, C. (2014).Big Data + The Internet of Tilings = Big Manufacturing Opportunity. Retrieved from http://www.crmsearch.com/internetofthings.php.
Schaff, C. &Harris, G. (2012). 7 Secrets to Social media Business Success. Retrieved from http:// www.prnewsonline.com/Assets/File/digitalpr_presentations2012/Clinton_Schaff. pdf.
Shani, D., & Chalasani, S. (1992). Exploiting niches using relationship marketing. The Journal of Consumer Marketing, 9(3), 33-42.
Shanmugasundaram, S. (2010). Customer relationship management: Modern trends and perspectives. PHI Learning Pvt. Ltd.
Tas, Z. (2018). Big data in the shipping industry. Retrieved from https://www.morethan- shipping.com/big-data-in-the-shipping-industry.
Thuraisingham B. (2003). Web data mining and applications in business intelligence and counterterrorism.
Tiwana A. (2003). Przewodnik po zarzcfdzaniu wiedzt}. E-biznes i zastosowania CRM. Warszawa: Placet.
Tuzhilin, A. (2012). Customer relationship management and web mining: The next frontier. Data Ming Knowledge Discovery, 24(3), 584-612.
Vercellis, C. (2009). Business intelligence. Chichester: Wiley.
Vriens, M., & Kidd, P. (2014). The big data shift: What every marketer needs to know about advanced analytics. Marketing Insights, November/December, 23-29.
Wilde, S. (2011). Improving customer relationship through knowledge application. Berlin Heidenberg: Springer-Verlag.
Wixom, B.H., & Watson H.J. (2010). The Bl-based organization. International Journal of Business Intelligence Research, /(1), 13—28.
Xu, Z., Frankowick, G.L., & Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 65(5), 1562-1566.
Xu, Y., Yen, D., Lin, B., & Chou, D. (2002). Adopting customer relationship management technology. Industrial Management dr Data Systems, 102(8), 442-452.
Zulicki, R. (2017). Potecjal big data w badaniach spolecznych. Studia Socjologiczne, 3(226), 175-207.