Data Analytics 5q1

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DataAnalytics

5q1

Organizationsface a myriad of challenges as they embrace analytic competition.Human talent stands out as the biggest hurdle in the process. Theskills, competencies, and abilities of employees affect the outcomesof activities in an enterprise. The capacities are critical for theinnovation of the desired processes that give institutions acompetitive advantage. According to Tschakert and Kozlowski(2016), human skills are primary inputs in a business structure thatadopts analytics. Workers need to develop the right skills to providepredictive and perspective analytics. For example, Apple has opened afirm in India specifically for skill hunting and development in theAsian region.

Harriset al. (2010) provide four criteria that organizations can exploit tostrengthen analytic capabilities. They include identifying the righttalents to support business processes, sourcing locations for topskills, identifying the necessary baselines for the desiredcompetencies and manipulating the talents to suit the emergingchanges. Although acquiring the right expertise is an uphill taskfor some organizations, making the employees flexible is alsochallenging. A firm’s internal challenge, therefore, is to ensurethat the available competencies support the varying levels oftechnology in highly competitive environments.

5q2

Thenumerous data that organizations acquire demand effective utilizationstrategies. According to Chen et al. (2012), a multiple-approach toinformation harnessing is a recommendable technique since companiescan find the right applications for specific findings. Data miningand regression analysis are some of the most effective tools thatnovice enterprises can exploit to gain a competitive advantage(Provost &amp Fawcett, 2013).Regression helps in determining the relationship between variousinternal undertakings that are within the management’s control andthose that are beyond its influence (Roig-Tierno et al., 2016). Forexample, Sur La Table Inc. has embraced regression by analyzing theconsumers’ variables including their lifetime purchases, averageorder, preferred products, and feedback. The idea has helped themanagement to develop a mailing list for specific clients.

Accordingto Du et al. (2015), organizations use regression for cost estimatesand to determine the possible outcomes of uncertain scenarios. Inaddition, businesses that are new to analytics can benefit fromextracting information from various sources(Bifet &amp et Réseaux, 2015).As they mature in their operations, they can introduce complexanalytic tools that will provide additional results (Talia, 2013).The adoption of analytic methods, therefore, should be a systematicprocess from the basic to more sophisticated techniques.

References

Bifet,A., &amp et Réseaux, D. I. (2015). Real-Time Big Data StreamAnalytics. In 2ndAnnual International Symposium on Information Management and Big Data(p. 13).

Du,J., &amp El-Gafy, M. (2015). Human computation enabledorganizational learning in the face of deep uncertainty: example ofconceptual estimating.

Harris,J., Craig, E., &amp Egan, H. (2010). How successful organizationsstrategically manage their analytic talent. Strategy&amp Leadership,38(3),15-22.

NorbertTschakert, C. P. A., &amp Stephen Kozlowski, C. P. A. (2016). Thenext frontier in data analytics. Journalof Accountancy,222(2),58.

Provost,F., &amp Fawcett, T. (2013). DataScience for Business: What you need to know about data mining anddata-analytic thinking. California: O`Reilly Media.

Talia,D. (2013). Toward cloud-based big-data analytics. IEEEComputer Science,98-101.

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