AnalyticsCase Evaluation and Response
AnalyticsCase Evaluation and Response
Inthe contemporary business world, data analytics has become part ofthe main factors to consider when a business wishes to build itsanalytical capacity and make any decisions(Harris,Craig & Egan, 2010).With the current levels of technological advancements, a lot ofvaluable information is derived from the available data sets.According to Davenport,Harris, Jones, Lemon, Norton & McCallister, (2007), healthinsurer IFA and Shopsence formed an engaging partnership. However,the connection threatens to interfere with the client’s tolerancefor sharing private data. The insurance company manager has beenproposing for the incorporation of the customer analysis to makeconclusions about consumer behavior based on the available evidence.If the clients realized that Shopsence was selling their informationto the insurance company, they might decide to stop buying theirgoods, and it would result in great losses for the enterprise.Recently, the institution hired me as an analytics consultant. I metthe CEO, Donna Greer, and we agreed that I would providerecommendations regarding how the organization can apply analytics togain some competitive advantage.
Ifthe access and the use of client data is to be useful for thecompany, then the organization must create methods to satisfy thecustomer`s concerns about privacy(Provost & Fawcett, 2013).This tactic would ensure that unpleasant use of data is prevented,and the clients would be informed about the purpose of theirinformation. The institution must observe transparency. The customersmust be notified on the mode of compensation for agreeing to the useof their purchase information. The risk of client backlash is higherfor Shopsence than for IFA (Davenport,Harris, Jones, Lemon, Norton & McCallister, 2007).The institution has to consider if the market strategy is valid andwhether the company can legally use it. They also need to considerwhether the marketing strategy has any effects on the institution andthe range of options in place for them.
Accordingto Bazerman& Chugh (2006), many executives have gained their success becausethey focused on particular information. Businessanalytics is a statistical method of exploring the corporate datawith an emphasis on quantitative analysis. Most of the companieswhose decision mechanisms are data-driven use this method to aid themin making some informed decisions. Business analytics assists thecompanies to arrive at helpful insights in their efforts to automateand optimize business activities (Veletsianos,2010).In data-driven mechanisms, the companies treat their corporate dataas an asset which they can readily use and gain some competitiveadvantage against other competitors. For business analytics to besuccessful, the data used should be of high quality and the personnelresponsible for analyzing the data should be well skilled with thenecessary technology and business knowledge. One method of analyzingthe importance of the available information is via predicting theactions of the competitors and analyzing the code that guides theiractivities (Bazerma& Chugh, 2006).Theanalyst should also understand the organization`s commitment to databased decision mechanisms adequately.
Businessanalytics may be used to assist the company to find new patterns andrelationships in the market, explain the behavior among the clients,and conduct experiments on some previous decisions made (Gupta,2016).Business analytics also help the institution to make some futureprojections. With this program, the company can adequately performits various tasks and be able to acquire the resultant advantages andeventually quell the competition.
Accordingto Harris,Craig & Egan (2010),if the company is in its initial stages of applying analytics toenhance the business value, it should first evaluate the analysts italready has, and investigate the functions of the variousdepartments. Then the organization determines the appropriate data touse in the analysis procedure. The data is derived from varioussources. It may be extracted from one or different business systems,cleansing or integration of information into a single structure, forinstance, a data mart. The analysis process applies some variousmethods such as spreadsheets and sophisticated data mining andprediction modeling software(Veletsianos, 2010).Typically, the analysis is conducted on small samples of evidence.
Asthe data patterns and relationships continue to be uncovered, newquestions arise, and the analysis process continues until thebusiness objective is realized (Koch& Rao, 2014).Use of predictive models involves arranging data records and using itto maximize and perfect the real-time decisions that are appliedwithin the organization. Business analytics also assists in makingprior decisions to emergency occurrences and implementing anautomated decision-making process to respond effectively in time.Business analytics is applied to answer such questions as whathappened and why it happened, is there a possibility of it occurringagain, what would happen if some variables changed, and any extrainferences that can be derived from the data.
TheShopsence institution should consider adopting the data analysisapproach to determine the client behavior. Having recognized theincreased popularity in the use of the business analytics, theEnterprise Intelligence Application developers have incorporated someanalytical functionality in their products (Gupta,2016). Though there are considerable analytical activities within thefirms, analytics is only in its initial stages as a competitiveresource for the organizations (Harris,Craig & Egan, 2010).Irrespective of the industrial occupation or geographical location,many business people believe that analytics will grow in importancein the coming few years. Focusing more on larger sets of data andanalytics provides positive outcomes from both the trial projects andthe anecdotal evidence against the competitors(Ma, Chen & Wei, 2014).Sound data can yield some good decisions for the senior management ifthe information is adequately recorded, analyzed, communicated andimplemented. An institution like Shopsence can develop and grow if itfocuses on the analytics and ultimately it will gain some competitiveadvantage against its competitors.
Theincorporation of data in decision-making is driven by such factors aseconomic necessities (Koch& Rao, 2014).Indeed, desperate situations demand drastic solutions. Many companiesbegin the analytics process with dabbling and specializing in one ormore applications and eventually, they gain momentum for additionalinitiatives. In market-place related areas, analytics is used toidentify favorable methods applicable to increase the volume ofsales. Facts and data are the primary driving force for the manybusiness activities such as investments (Provost& Fawcett, 2013).Analytics assist the companies in expanding their product lines inthe required proportions and becoming more realistic in forecastingthe offers that are likely to work. Analytics also help theinstitutions to determine the most favorable time for thesesuggestions to work.
Theorganization should overcome various limitations so that the dataanalytics can play a more vital role. The company needs to redirectits hiring practices in the analytics field to focus on datascientist hybrids. The institution should consider hiring talentedindividuals with either computer science or analytics backgrounds andabsorb them in the business teams that will utilize their skills andinsights (Ma,Chen & Wei, 2014).The organization may also work with the various administrators in theinstitutions of higher learning to develop a data program that willbe useful to the next generation of data analysts.
Bazerman,M. H., & Chugh, D. (2006). Decisions without blinders. HarvardBusiness Review, 84(1),88.
Davenport,T. H., Harris, J. G., Jones, G. L., Lemon, K. N., Norton, D., &McCallister, M. B. (2007). The dark side of customeranalytics. Harvardbusiness review, 85(5),37.
Gupta,A. (2016). Advancesin Healthcare Informatics and Analytics.Springer International Publishing.
Harris,J., Craig, E., & Egan, H. (2010). How successful organizationsstrategically manage their analytic talent. Strategy& Leadership, 38(3),15-22.
Koch,F. & Rao, C. (2014). Towards Massively Personal Education throughPerformance Evaluation Analytics. IJIET, 4(4),297-301. http://dx.doi.org/10.7763/ijiet.2014.v4.417
Ma,Y., Chen, G., & Wei, Q. (2014). A novel business analyticsapproach and case study – fuzzy associative classifier based oninformation gain and rule-covering. JournalOf Management Analytics, 1(1),1-19. http://dx.doi.org/10.1080/23270012.2014.889915
Provost,F. & Fawcett, T. (2013). Datascience for business.Sebastopol, CA: O`Reilly Media.
Veletsianos,G. (2010). Emergingtechnologies in distance education.Edmonton: AU Press.