Theoretical Concepts in Business Analytics

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Introduction

Making informed choices in the workplace is of great importance since strong data analysis and decision-making skills enable teams to maximize the chances of success. In organizational contexts, those involved in making important decisions should be aware of different methods helping to analyze data sets and make accurate predictions. This essay is aimed at analyzing some theoretical concepts in business analytics and discussing their applications in data analysis and decision-making.

Probability

The concept of probability can be justly listed among the terms having numerous applications in decision-making in everyday and professional problems. Based on class activities, the probability is basically the chance that a specific event will happen, and decision-makers should reassess probabilities when new information becomes available (Albright & Winston, 2017). The notion of probability and its main rules (the rule of complements and the addition rule) are relevant to workplace decision-making and applied to the selection of investment opportunities (Sen, 2020). For instance, using the concept of probability, investment analysts keep track of the chances of financial success and failure associated with each available investment opportunity. Markov chains, which can be used to analyze the competitive landscape and fluctuations in brand preferences, are another popular application of probability in business analytics and strategic decision-making (Trinh, 2018). Probability assessments remain critical when it comes to workplace decisions since an understanding of this concept allows looking into the future of specific businesses and avoiding preventable strategic mistakes.

Uncertainty

In the workplace, a good understanding of uncertainty contributes to the ability to design strategic decisions that bring financial and reputational gains without being overly risky. One common misconception about uncertainty involves the inability to reflect the difference between risks and uncertainty in decision-making. As opposed to risks that exist in situations with predictable outcomes, uncertainty is much harder to quantify and assess. Those involved in decision-making are to deal with uncertainty when analyzing business data to search for relationships and trends (Albright & Winston, 2017). In business decisions, probability rules can be applied to assess uncertainty about whether companies will meet the set deadlines or derive additional profits given their current market position and suppliers’ work (Albright & Winston, 2017). Simple uncertainty assessment methods find multiple applications in business and daily life. Individuals deal with uncertainties and poorly controllable factors in many instances, ranging from the establishment of new partnerships to the need to make decisions about international contracts despite exchange rate uncertainty.

Distribution

In workplace decisions, it is also essential to understand the concept of distribution and distinguish between its types. A data distribution refers to a large number of all possible values for a specific variable and the frequency of these values’ occurrence (Albright & Winston, 2017). Many variables in real-life business situations are normally distributed or can be brought into normal distribution by means of specific transformations (Albright & Winston, 2017). An understanding of distribution, especially the normal distribution, facilitates data analysis and decision-making related to different aspects of business, such as customer relations, issues in quality control, internal operations, and so on (Albright & Winston, 2017). For instance, knowing the normal distribution of demand for products and services offered by their businesses, including seasonal fluctuations, financial and strategic decision-makers can make better procurement decisions.

Apart from other possible applications, the concept of distribution is relevant to the workplace since it can be used to establish just hiring and pre-employment testing policies. For instance, if the company implements a standardized exam to facilitate applicant selection, it will be helpful to evaluate the distribution of applicants’ test results to establish fair pass and fail criteria (Albright & Winston, 2017). Having assessed the distribution of results, it is possible to discover that the current pass/fail criteria need to be improved. If selected without due analysis, these criteria can make it too easy or too challenging for an average applicant with the required qualifications to enter the second stage of the interview process. It may result in spending extra time on conducting individual job interviews with unsuitable candidates or rejecting too many applicants. The use of statistical concepts can help businesses to find the golden mean approach to applicant evaluation.

Sampling

To find effective and easy-to-implement decisions when business problems arise, decision-makers should understand the peculiarities of sampling in statistics. There are different probabilistic (random selection) and non-probabilistic (selection based on specific criteria) approaches to sampling with their own advantages and limitations. The use of sampling techniques supports workplace decision-making to a great extent. For instance, quality control policies of manufacturing businesses can be based on probability sampling techniques and involve randomly selecting a specific number of items from a lot to look for defective products and make further decisions (Albright & Winston, 2017). Depending on the situation, diverse approaches to sampling are also applied to customer/employee surveys and customer satisfaction research. To understand employees’ attitudes to a planned innovation in a very large organization, it is possible to use purposive sampling and consider the experiences of particular employees that would be the most affected by this innovation. Random sampling techniques are used in customer satisfaction research and can, for instance, inform the implementation of package/product testing surveys or brand awareness surveys.

Statistical Inference

Statistical inference refers to the use of statistical research methods to draw conclusions about the sample or its specific subsets. In real-life situations involving the need for business and customer research, statistical inference can be applied to acquire an understanding of general trends affecting customers or trends in customer behavior without the need to survey every single client. For instance, a large mail-order business can study the time that an average customer needs to pay bills by randomly sampling a specific number of clients and studying their experiences (Albright & Winston, 2017). Also, being unable to choose between two different compensation schemes or organizational control methods, large employers can conduct experiments (Albright & Winston, 2017). After assigning two randomly selected groups of employees to the conditions in question, they can observe and examine the conditions’ effects on employee productivity, satisfaction, and other parameters of interest. In the scenarios described above, hypothesis testing and confidence interval estimations can be applied to make inferences about the larger population (all customers/all employees) without the need to study the experiences of any member in the group.

Regression Analysis

Regression analysis helps to single out the variables that have the most significant role in the issue of interest. Based on class activities, regression analysis is highly applicable to decision-making dilemmas, including the selection of the best business locations based on each option’s profitability, competitiveness, and physical properties (Albright & Winston, 2017). For example, La Quinta Motor Inn, a hotel chain, applied the regression equation to predict the profitability of potential locations and used the results to inform business expansion decisions (Albright & Winston, 2017). Simple linear regression (the presence/strength of the relationship between two variables) is among the tools that are commonly used for organizational decisions. Some of its workplace applications include making predictions related to real estate values, conducting salary projections, and studying clients’ behaviors and the influence of external and internal factors on companies’ profits (Bhargavi & Sheshasaayee, 2018). In addition, sales specialists apply linear regression to analyze the sales data in the previous reporting periods and create sales forecasts, and the tool allows studying links between advertisement strategies and sales (Bhargavi & Sheshasaayee, 2018). Therefore, the business applications of regression analysis are abundant today.

Time Series

A time series refers to a series of items of data that are represented in time order. In business analytics and decision-making, time series analysis is applied to keep track of trends that affect specific business metrics and extract the most relevant statistics to make particular decisions (Albright & Winston, 2017). The relevance of time series analysis to the workplace is explained by its ability to contribute to the accuracy of forecasts and judgments. Time series forecasting finds application in different fields of business and is used to calculate the market-clearing prices of electricity and plan the operation of electric power systems by forecasting electricity loads (Mahalakshmi et al., 2016). Other common applications of time series analysis include using the historical data related to stock markets’ operation to construct more successful portfolios and improve stock selection decisions (Mahalakshmi et al., 2016). By looking at time series data and the presence of seasonality, decision-makers gain valuable information. Understanding seasonal trends in sales revenues, businesses can make further inquiries into external factors causing these trends and plan new marketing promotion strategies or product assortment decisions to achieve stability in revenues.

Forecasting Methods

Forecasting techniques are numerous, and there are three main groups of methods with their own advantages. Extrapolation methods, also known as time series methods, support workplace decision-making by searching for specific patterns in historical data and extrapolating them into future events (Albright & Winston, 2017). Time-series forecasting methods are widely used to forecast electricity loads and make informed investment decisions (Mahalakshmi et al., 2016). Econometric or causal forecasting methods support business decision-making by predicting the variable of interest with the help of other explanatory variables and regression (Albright & Winston, 2017). In the workplace, such methods can be applied to use large-scale economic factors (interest rates, average income levels, etc.) to predict future trends in sales and make informed product stock decisions. Finally, there are judgmental or qualitative forecasting methods that are often used in the absence of historical data (Albright & Wilson, 2017). For instance, new businesses use forecast by analogy (Jun et al., 2017). Within its frame, decision-makers look at the sales data of products that share similarities with their products to predict demand patterns and issues.

Optimization

Optimization is a broad topic in decision-making, and it refers to different approaches to determining the best possible decision and maximizing the chances of achieving the desired outcome. Interestingly, more than 80% of the 500 most profitable corporations in the U.S. report the use of optimization, which proves its importance in making business decisions (Albright & Wilson, 2017). Optimization techniques find reflection in multiple applications and models, including the models helping to make the best investment decisions. For example, the Markowitz portfolio optimization model (the mean-variance model) is used to evaluate the effectiveness of different investment options (Zanjirdar, 2020). Other popular applications of optimization techniques, for instance, linear programming, include solving employee scheduling problems (Albright & Wilson, 2017). For instance, a large organization can use linear programming methods to find the best schedule that implements all daily workforce requirements using the smallest possible number of full-time employees.

Decision Tree Modeling

A decision tree is a specific type of graph that depicts the payoffs and costs of specific options in a convenient way. Decision tree modeling facilitates the visualization of different elements of the problem and makes it easier to calculate the expected monetary value (EMV) of the potential outcomes (Albright & Wilson, 2017; Alomari et al., 2018). In the workplace, decision tree modeling is applied to solve problems of different complexity since such graphs can be drawn on paper or created with the help of specific Excel add-ins. Apart from solving one-stage decision-making problems, decision trees can find application in the BPM+ approach to process modeling to improve the structure of business process models (Alomari et al., 2018). Continuing on the real-world applications, simple decision trees with EMV calculations help businesses to compare the financial outcomes of launching a new product or abandoning this idea. In a similar way, decision tree modeling can be applied to compare the outcomes of different investment options, personnel development strategies, approaches to advertising, and other decisions affecting businesses.

Conclusion

To sum it up, all ten concepts discussed above facilitate the process of decision-making in business environments. The course elements’ current applications to workplace decision-making are great in number, whereas future applications are not abundant in the available research literature. The concepts are relevant to the workplace since they can be utilized to support strategy development, inform inventory and investment choices, establish effective internal policies, and maximize profits.

References

Albright, S. C, & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.) [eBook edition]. Cengage Learning.

Alomari, A., April, A., Monsalve, C., & Gawanmeh, A. (2018). Integrating a decision tree perspective at the operational level of BPM. Computer Systems Science and Engineering, 33(3), 219-227.

Bhargavi, K., & Sheshasaayee, A. (2018). Implementation of regression analysis using regression algorithms for decision making in business domains. In S. Smys, A. M. Iliyasu, R. Bestak, & F. Shi (Eds.), New trends in computational vision and bio-inspired computing: Selected works presented at the ICCVBIC 2018, Coimbatore, India (pp. 819-828). Springer.

Jun, S. P., Sung, T. E., & Park, H. W. (2017). Forecasting by analogy using the web search traffic. Technological Forecasting and Social Change, 115, 37-51. doi:10.1016/j.techfore.2016.09.014

Mahalakshmi, G., Sridevi, S., & Rajaram, S. (2016). A survey on forecasting of time series data. In 2016 international conference on computing technologies and intelligent data engineering (ICCTIDE’16) (pp. 1-8). Institute of Electrical and Electronic Engineers.

Sen, S. (2020). Investment decisions under uncertainty. Journal of Post Keynesian Economics, 43(2), 267-280.

Trinh, V. D. (2018). Analysis of brand switching behavior in motorbike hailing app with Markov Chain model. Global and Stochastic Analysis, 5(6), 93-101.

Zanjirdar, M. (2020). Overview of portfolio optimization models. Advances in Mathematical Finance and Applications, 5(4), 1-16.

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