Thesuccessful adoption and exploitation of analytics in an organizationis dependent on various internal factors. First, the excellence ofthe system and information in an enterprise are key considerations.The data used in the various processes enhances the identification ofthe right technology. In addition, a system that is easy to apply andconsistent with the business operations improves the analyticalcapabilities of the employees(Kaisler et al., 2013).Secondly, top management support is critical in giving direction andencouraging the use of analytics in all cadres. According to Popovičet al. (2012), the leadership is well informed of the benefits ofanalytics. Therefore, managers are mandated to influence the rest ofthe workforce and provide the relevant hardware and softwareresources. An organization also needs to have computer literatepersonnel who have the capacity to use the analytic tools. The basictechniques can be developed gradually and customized to suit changingtrends (Kaisleret al., 2013).With these basic requirements, an organization can effectivelyenhance analytical capabilities and reap the projected benefits.
Thesize of a sample in a given study influences the outcomes and choiceof the statistical tests. The number of respondents determines theimmensity of data collected from the field. According to Noand Analytical Methods Committee (2015), manyparticipants result in numerous responses that have to be analyzedusing statistical tools. For example, in a study to determine theeffect of the family on political choices among college students inColorado, the researchers are likely to have a large sample, andconsequently big data. They may use specific tests including SPSS,ANOVA among others. To arrive at a representative sample, researchersuse various scientific formulas. The number of participants alsoaffects the outcomes. A sample is taken as a true representation ofthe population. For example, a research involving many respondents isconsidered to have reliable outcomes, and its results can be appliedin other similar settings.
Itis imperative to plan the sample before conducting research forvarious reasons. First, the team conducting the inquiry gets a vividpicture of the location of the identified respondents and schedules asystematic procedure instead of approaching them randomly. Secondly,it assists the personnel to develop a list of the required resourcesdepending on the number and literacy characteristics of therespondents(No and Analytical Methods Committee, 2015).Finally, planning enables the researchers to determine whether thesample represents all the attributes of the population under study.
Kaisler,S., Armour, F., Espinosa, J. A., & Money, W. (2013, January). Bigdata: issues and challenges moving forward. In SystemSciences (HICSS), 2013 46th Hawaii International Conference on(pp. 995-1004). IEEE.
No,A. M. C. T. B., & Analytical Methods Committee. (2015). Samplingtheory and sampling uncertainty. AnalyticalMethods,7(24),10085-10087.
Popovič,A., Hackney, R., Coelho, P. S., & Jaklič, J. (2012). Towardsbusiness intelligence systems success: Effects of maturity andculture on analytical decision making. DecisionSupport Systems,54(1),729-739.