EmergingTrends in Business Analytics
Businessanalytics has intensified over the last few years providing userswith better decision-making tools from data derived fromtransactional systems. Various emerging trends in analytics continueto make the applications user-friendly. First, the need to simplifythe representation of complex analysis has become a primaryconsideration. For the tools to be effective and take care of theneeds of a wide audience, the large and complex data sets should beeasy to read(Kambatla et al.,2014). Most of the analytic tools have adopted advanced visualizationcapabilities.
Anothermajor trend is the transition of analytics into the mobile devices toenhance collaboration between workers in different cadres. Forexample, the use of mobile devices to perform roles has been dominantfor the sales people while lacking in the top management. Accordingto Dawsonet al. (2014), the executive has adopted the technology in handheldunits. Also, analytics are continually being exploited byorganizations to gain a competitive advantage. Analytic competitorsuse the applications as a strategic weapon to study the market trendsand increase their revenue(Dawsonet al., 2014). For example in aviation and manufacturing industries,there is an intense use of analytics as the companies study marketpatterns to remain competitive.
Inmost studies, researchers engage a group that is taken as a truerepresentation of the target population. The size of the sampleaffects the test and the outcomes of a study in various ways. First,the quantity of data collected in research is dependent on the sizeof the sample(Israel, 2012).This also affects the choice of the appropriate method to analyze thedata and arrive at the most conclusive interpretation. For example,in an inquiry to understand the security implication of allowingstudents to carry guns to college in a given state with about 1000participants, researchers would find it necessary to employstatistical tools to determine the relationship between thevariables. Secondly, the sample size also affects the precision andstatistical significance of a study(Israel, 2012).For example, research can only be generalized and become applicablein different settings if it includes a relatively adequate number ofparticipants.
Beforecollecting data, it is instrumental for researchers to plan thesample size. In doing so, they can determine the resources requiredto extract the required information(Israel, 2012).Also, it makes it easy to determine whether the targeted number ofparticipants is the actual representation of the total population.This is imperative if the researchers intend to generalize theirstudy.
Dawson,S., Gašević, D., Siemens, G., & Joksimovic, S. (2014, March).Current state and future trends: A citation network analysis of thelearning analytics field. In Proceedingsof the Fourth International Conference on Learning Analytics AndKnowledge(pp. 231-240). ACM.
Israel,G. D. (2012). Sampling: Determining sample size. Retrieved,5(13),2013.
Kambatla,K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in bigdata analytics. Journalof Parallel and Distributed Computing,74(7),2561-2573.