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The Importance of Business Analytics and Big Data for Organizational Performance

According to Chen, Chiang and Storey (2012), attention should be paid in both practitioner and academic literatures, so that organizational values can be enhanced through the use of business analytics and big data. Use of business analytics and its technologies not only help the organization to understand its marketing and business strategies but also helps to leverage the opportunities presented by domain specific analytic and abundant data. Davenport, Harris, and Morison (2010) mentioned that by the use of business analytic technologies leading organization can make more sound and rigorous decisions than lower performing companies. New era of business analytic technologies also help the organizations to make its future strategies by implementing in the daily business operations.

Different people from different sectors of the organization are needed to structure the managerial decisions from data. Mithas (2013) stated that generating insights from data is not only the target of this new technology but making meaningful insights is equally important for the welfare of the organization. Moreover that, transformation of these insights (intuitive and deep understanding of the situation) into value should be ensured by the managers, so that operations and strategic decisions will be more powerful in respect to organizational performance. According to Gillon et al. (2014), big data can impact organizational performance with better decisions and good insights. Implementation of business intelligence helps to understand the business problems and opportunities in a better way which can lead to discover new revenues and cost saving methods for the organization. Gillon et al. (2014) also argued that not necessarily good insights lead to good decisions but it can make poor decisions too. So creating better options and evaluation of those options can help to commit to a firm decision, by which organizations can make immense changes in this competitive business world.

Much consideration is presently being paid in both the academic as well as peer reviewed literatures to the showcase that organizations could craft through the use of big data and business analytics (Gillon et al, 2012; Mithas et al, 2013). Taken for example, the study of Chen et al (2012, p. 1166–1168) has explored that business analytics and similar technologies can lend a support towards the organization to ‘better comprehend its operation as well as markets’ and ‘control opportunities obtainable by ample data and domain-specific analytics’. In the same way, LaValle et al (2011, p. 22) have performed a study and according to their findings, highest performing institutions ‘take decisions on the basis of severe exploration of its existing information in compare to lower performing institutions’. They have also concluded that such a detailed inspection of their historical information become possible only through data analytics.

Generating Meaningful Insights for Organizational Performance

Despite the fact that there has been a list of research works that has engrossed on the capability of business analytics to produce better understandings and decisions, the emphasis on the impending of data driven decision making (3D) to capture value has been restricted. The implied postulation exploring that dissertation appears to be that if the eminence of decisions can be augmented through the practice of data driven decision making (3D), then the enquiry of how institutions can produce value from those decisions is an insignificant one. With regards to this fact, studies has shown two uncertainties connecting with altering decisions to value – the indecision of effectively executing decisions and the indecision accompanying with the accomplishment of premeditated actions. Thus, this is the prospective future of business analytics and resource distribution procedures in extenuating those indecisions.

Perhaps, the practice of business analytics can support to expand the eminence of decisions. Nevertheless, it is not vibrant if business analytics can be engaged to mend the recognition of decisions in any extent. Several studies has recommended that intuition- generation and resolution making processes accompanying with the practice of business analytics and every so often do not include important stakeholders from practical areas who will be accountable for executing those choices (Shanks and Sharma 2011; Shanks et al. 2010). Although cross-functional teams are frequently engaged to work with business analytics, main stakeholders who ‘hold the ownership of resources prerequisite to execute decisions are mostly not a member of those teams. Thus it can be concluded that there is a methodical arrangement, it would probably bring to light in cross-sectional investigation as an undesirable association between the practice of business analytics in decision-making and the positive executions of those choices.

Hence, from the above discussion two important aspects are highlighted for business organizations to take into consideration prior to employ data driven decision making process:

  • Better understanding of how decision-making do processes impact the efficacious application of choices after analyzing information through business analytics; and
  • Appropriate way of utilizing business analytics to progress the recognition of choices in the longer run;

Data-driven decision making (3D) is a tactic towards business control that values choices that can be endorsed with confirmable data. The attainment of the data-driven decision making is dependent upon the superiority of the data congregated and the efficiency of its examination and elucidation. 

In the first few years of computing, it typically took a professional with a robust background in technology to scrutinize data for evidence since it was essential for that professional to apprehend how databases as well as data warehouses controlled. In this context, it can be said that if a manager of any institution wanted to understand data at a granulated level, he or she had to take help from the information technology department (IT). Once the query raised by the manager, any person from the IT department would then generate the information and plan it to run on an intermittent basis. This clearly indicates a complex procedure and as a consequence, ad hoc reports, commonly termed as one-off reports, were dejected.

The Role of Cross-Functional Teams in Decision-Making

In the contemporary world, with the help of data driven decision making process managers can tailor dashboards to exhibit the data they need to see and run routine reports on the flutter. The variations in how data can be excavated and envisaged permits business administrators who have no knowledge or backgrounds in IT to be capable to toil with data analytics and make data-driven decisions.

The above review of literature related to data driven decision has significant insinuations for managers in XYZ to employ business analytics to enhance the performance level. The impending worth that could be shaped and apprehended through the practice of business analytics is one of the vital impetuses for why XYZ is making considerable investments in those technologies. An alike stimulus has reinforced earlier investments in Business Intelligence taken for examples, Executive Information Systems (EIS), Customer Relationship Management Systems (CRMS) as well as Business Intelligence Systems (BIS) that can be well thought-out as pioneers of business analytics. Investigators are exploring the importance and earnings apprehended by organizations with utilizing these business analytics tools and thus it holds true for XYZ also. It has seen there are several perceptible paybacks taken for example, enhanced information flows, and imperceptible paybacks for instance better-quality of customer information, one-to-one business efficiency, consumer contentment, and consumer surplus that XYZ can get hold off (Mithas et al. 2006; Mithas et al. 2005).

Nevertheless, the ways from investments in those business analytic tools to monetary value are not apparent. In specific, it has to be considered that the impacts of investments on pointers of value making for instance stock returns are not unswerving; somewhat, those effects are arbitrated by their influence on aspects for example customer satisfaction (Fornell et al. 2009; Fornell et al. 2006). According to the study of Gillon et al. (2014); Mithas (2013), there are six pathways to gain competitive advantages through the practice of business analytics. The below mentioned figure explored all six pathways:

Figure: Business decision making

(Source: Gillon et al. 2014)

Taken all at once, the aforementioned discoveries suggest that the way in which XYZ can implement technologies has significant contribution towards its business culture. It cannot be forgotten that it has a significant comportment on its capability to generate and capture value. In specific, XYZ need to emphasize parenting to transmuting their decision-making processes if they are to get hold of the significance that is potential through the practice of business analytics.

This section of this study explored results found through rapid miner related to patient health data. The data set contains one predictive variable and 8 confounding variables. The predictive variable “genhealth” represents health rating of each patients and categorized under five categories such as poor, fair, good, very good and excellent.

The Prospective Future of Business Analytics and Resource Distribution Procedures

The aim is to identify, which confounding variables are contributing the most to determining whether a patient is healthy. To do so, the analyst has initially performed descriptive statistical exploration. The below mentioned figure explain the results found through rapid miner. In addition, the analyst has also mentioned histograms of each of these confounding variables. As per this , height, weight, age, wtdesire and gender are the key confounding variables. However, further analysis has been performed to identify the top five key confounding variables.    

The above mentioned correlation table represents the association among predictive and confounding variables. However, if the association between genhealth and all 8 confounding variables are taken into consideration, then it can be seen that though the “r” is positive, it is significantly low, in other words near to 0. Thus, the correlation table does not provide much more details about key variables that contribute most to determining whether a patient is healthy. Hence, further the analyst has performed chi square test.  

According to this output, it can be said that the top five confounding variables that which contribute most to determining whether a patient is healthy are gender, wtdesire, hlthplan, height and smoke 100.

In rapid miner, decision tree can be made through using two basic operator such as “set role” and “decision tree”. The decision tree helps the analyst to judge which variables are key two predict the desire results.

In rapid miner, there are three major aspects of preparing decision tree. The first step is to identify the input. Here, as shown in the below process, patient health data is considered as the input.

An operator “set role” is used here to define the label variable. Since, the aim is to identify factors which contribute most to determining whether a patient is healthy, “genhlth” is marked as label. 

Once, the role has been set, another operator, “decision tree” is linked with the model. As shown in the below mentioned figure, gain ratio with maximal depth 20 has been set for this. Gain ratio is an attributes that regulates the information gain for each attribute to authorize the extensiveness and consistency of the attribute values. At the same time, minimal gain is set as 0.05 to split decision tree and to get bigger tree.

Finally, the decision tree output is found. The below mentioned figures represents the decision tree and decision tree rules.

As per the decision tree, weight is considered the top key confounding variable. It is seen that if weight of any patient is less than or equals to 81.5, then the health condition is considered as poor. Now, if weight of any patient is more than 81.5, then age is considered to judge further. Thus, age is second key confounding variable. Similar to the above, wtdesire, exerany and gender are considered as next three confounding variables which contribute most to determining whether a patient is healthy.

Data-Driven Decision Making and the Value It Can Bring to Organizations

This section of the study explored death of list of people over a specified period of time, who died while climbing Mount Everest.

The below mentioned graph displays by Nationality, No_Deaths over time (in years). According to this graph, it can be said that people with nationality Nepal died most followed by India, United States, Japan and others. The recent death rate is high across nationalities.

This particular number of deaths by Location, Cause of Death, and Nationality. According to this, number of death is high in base camp. Nepalese are mostly died in basecamp because of avalanche and altitude sickness.

Finally, a view of the Deaths on Mt Everest in a Text Table or Graph view that displays by Expedition, Cause of Death and Nationality has been created as mentioned below. According to this, it can be said that adventure consultants are the major ones who died because of altitude sickness and avalanche. 

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