Mortenson faced. Theyconclude that analytics is outpacing management

Mortenson and Robinson are of the view that analyticsdemonstrate the diagnostic paradigm, differentiated by a largevolume of data that is heterogenous and is complemented by arrayof tools for processing and visualizing of data17. Challenge andvalue created by data analytics for prediction should be faced. Theyconclude that analytics is outpacing management science.The power of big data is usually related to predictive analytics thatuses statistical knowledge to forecast future events based on theassumption that what has occurred in the past may have influenceon future events. The common predictive techniques that are oftenused by data scientists are: regression modelling, decision tree,Bayesian statistics, neural network, Support Vector Machine(SVM) and nearest neighbor algorithms4. After acquiring the rawdata from the various sources, cleaning, integration, and other stepsare followed to make it ready for further analyses using appropriatepredictive techniques.Fig 2. A research Framework for value creationTowards the conceptualization of a big data &predictive analytics:(BDPA) capability Wamba defined BDPA as a higher-orderorganizational capability which relies on bundling of strategicresources. In a previous study, Akhter examined the effect thatresources and BDPA capability have on organizationalperformance. Despite the increasing research on BDPA, empiricalstudies on BDPA conceptualization are limited conditions underwhich the organization is functioning23.The effective exploitation of this organizational capability may leadto the achievement of sustained competitive advantage.Capabilities are created by the combination of resources, includinghuman resources and technical and managerial skills. We definehuman resources as a function of the employees’ experience,knowledge, business acumen, problem-solving abilities, leadershipqualities and relationships with others Skills (i.e. technical skillsand managerial skills) required to build BDPA capability,organizational learning and data driven decision making culturemay be the source of sustainable competitive advantage18.7. FindingsIn fact, from the previous studies in predictive analytics we can seethe benefits of its application such as the reduce and theprevention of risk, it allows to manage the resources, reduce time,allow to make better decisions, and save cost11, 23. While, inthe other hands many of the researches done about thePA which in majority focused on the creation anddevelopment of new models to enhance the use and results of PAapplication faced some challenges such asgetting real, sufficient and clean data to be able to test their modelsand discover its effectiveness. In addition, some models didnot get the desired results due to the wrong choice of algorithmsand variables. Moreover, the models applied must be dynamicand the PA must be integrated with other organizations systems toget better results and benefits23.8. ConclusionThe paper dives into investigation of previous research to designand outline a holistic but comprehensive characterization ofpredictive analytics in business. It examines roots, progress, needand issue that are related current state of the art PA. This paperconcludes that by finding 3 kinds of analytics dimension. Authorsuse a synthesizing approach to build a business analyticsframework, which accounts for six classes being complementary.one contribution of PA along with attendant dimension is toproduce a unifying foundation for building and key factorsuggested are commitment and awareness to organizations’ visionthat understand and support PA in terms of business.In sum, this paper takes into consideration and take an initialstep to build a foundation for the investigation of analyticalorganization.Infact, PA are system that have been in other industries as all forfirst time some get desired result some don’t.The authors, in this paper, try dive into knowledge archives tohighlight prediction analytics application in the business anddepicts its enormous benefits as well as to show PA ecosystem inthe business12.Must reads:1. H. Chen, R. H. Chiang, and V. C. Storey, “Businessintelligence and analytics: From big data to big impact.,”MIS Q., vol. 36, no. 4, 2012.2. M. A. Waller and S. E. Fawcett, “Data science, predictiveanalytics, and big data: a revolution that will transformsupply chain design and management,” J. Bus. Logist.,vol. 34, no. 2, pp. 77–84, 2013.3. D. A. Miles, “Market Research and Predictive Analytics:Using Analytics to Measure Customer and MarketingBehavior in Business Ventures,” in Analytics,Innovation, and Excellence-Driven EnterpriseSustainability, Palgrave Macmillan, New York, 2017

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