Business Analytics Finance and Human Resource

Question: Discuss about theBusiness Analytics for Finance and Human Resource. Answer: Introduction Making a sound decision in an organization is the core to the business success. A decision needs to be evidence based judgment so that the business data can be used to understand the past, current business situations, and the future. That is, business analytics is one of the most utilized strategy in business to make data-driven decisions (Stubbs, 2011, p. 7). This scholar also stipulates that organizations have the ability to save quite a massive amount of data. These data can be used to understand the trends, the past business performance as well as project the future. Moreover, business future plans are designed using the industry statistics. Statistics approaches are used to mathematically represent data and using other relevant methodology to explore the business data. Therefore, business analytics are important in different areas like marketing, operations, finance, and human resource (Sharda et al., 2014). Provost Fawcett, (2013) impressively gave some of the fundamental basics that need to be understood before data analysis process. The scholar stipulates that data can be used, but gives a bad information if wrong statistical methods are used. Importantly, the business analysts should incorporate stringent controls to reduce statistical anomalies as a result of methodological rigor. That is, the analyst should ensure that data are validated, stored correctly and appropriately to avoid corruption. Importance of Business Analytics Rouse, (2010) states that business analytics is important as it helps the business to answer a couple of questions like; why a situation occurred? Can it happen again? What are the consequences of changing a variable say ? And also other important information derived from data that people never thought to ask. To answer these questions data explorations, need to be performed. That is, data mining, quantitative data analysis, predictive/regression modeling, and multivariate testing commonly referred as a test of hypothesis. (Stubbs, 2011, p. 4) States every business is unique. This uniqueness makes them competitive in their own right, and an organization should use this to create a competitive advantage through capitalizing on what makes them unique. This can ONLY be achieved through massive investment in their unique resources; their data (Stubbs, 2011, p. 4). Through statistical approach and data management business are able to secure their future. Definitions of Analytics Ecosystem There are some of the common business analytical terms that are used more often. In accordance with (Evans, Lindner, 2012) descriptive analytics give summaries of data and transforms the summaries into meaningful charts and reports. A good example of this is a company that sells five types of beverages in Australia, and the sales department came up with the following summary. Figure 1: Beverages sales in Australia This summary indicates that Cottees drink is the most popular among the Australian people. Also, the statistics suggest that Golden Circle is least sold and maybe there is a need to improve the sales through advertisement or other favorable sales promotion strategies. Predictive analytics revolve around the development of models using past records that can be used to predict the future outcomes of the firm (Waller Fawcett, 2013). In fact, (Patil and Davenport, 2012) showed how sexy or beautiful it is to use predictive analytics and big data to make an important decision that affects the future of the business. Take an example of a public organization that has been keeping the return of the company for the previous twenty years (from 1995). These data can be used to develop a regression model that can be used to predict the future values in a certain year with a particular level of certainty. Figure 2: Returns ('000,000) against Time The plot indicates that the fitted model can explain 89.7% sources of variation of returns. The plot also shows that there is a positive linear relationship between return and time. Prescriptive analytics is one of the powerful statistical approaches that organizations use to optimize the outcomes of the firm. The core process of prescriptive analytics is to identify the best alternatives to minimize or maximize some objectives (Evans, 2012, p. 5). A bank uses this strategy to determine the optimal amount of money in an ATM. This approach determines the number of commodities that need to be produced to maximize the profit, and the number of workers that are required to minimize expenditure. Also, this approach can be used by firms to determine the course of action in case of disaster or any other unforeseen situation. In conjunction with mathematical and statistical techniques, prescriptive analytics can be used to make a decision taking into account uncertainty nature of the data. Halo Blog, Descriptive, Predictive, and Prescriptive Analytics Explained, (2016, May) states that prescriptive analytics goes beyond the commonly used predictive and descriptive analy tics as it recommends one or various course of action to remedy or optimize the objective(s). However, this approach is complex, and most of the institutions have not adopted it in their decision-making process. Business performance management (BPM) is fundamental and can be considered as cornerstones of business success. As (Stubbs, 2011, p. 12) say it, the most important part of the firm analytics is identifying the insights which are valuable given the organizations strategy and technical objectives. Therefore, the organization requires proper skills to manage all the processes within the organization and adopt a data collection habit. This will help the firm to come up with strategies to build competitive advantage. Business Analytics Implementation Plan Businesses are eager to implement the business analytics plan to enjoy higher revenues and at the same time reduce the cost. There are a number of issues facing the implementation like; mapping process of the organizations objectives, understanding the data, availability of resources, budget, and planning. The article by BusinessVibes, (2014) urges that five steps can be used in implementing the business analytics plan. First, the organization needs to identify the business problem, which should use quantitative results to solve the issue at hand. For instance, when an organization wants to solve problems associated with production, market returns they can adopt business analytics approach to resolve the problems (BusinessVibes, 2014). This strategy involves monitoring, capturing, and analyzing the business operation performance and writing reports on the results. Thus, the starting point of the implementation is identifying the business problem. The second step involves determining appropriate metric and analysis technique that aligns with the organizations needs. The analysts also should select the best statistical tools like SPSS, Minitab, Stata among others to analyze data as well as creating visualizations. The third step is collecting the data, which should be of high quality and integrity. The data collected should be in a position to make a projection, help understands the past, and display vital information about the business. The fourth step is the data analysis which exclusively tries to draw an insight about the data. This can be illustrated in econometric modeling, trend analysis, data distribution or deviations, or regression modeling used to adopt strategic business decisions (BusinessVibes, 2014). The last step is the reporting of the results obtained. The recommendations are drawn from the results obtained, which help to propel the business returns forward. As a result, the organization achieves the objective s and at the same time gain competitive advantage. Despite, efforts of organizations in implementing the business analytics in the decision-making process, there are still some drawbacks facing the implementation process. The first problem is the cross-organization collaboration which focuses on incorporating the customers information into the system and then solving the problem as a whole (IÃ…Å ¸Ãƒâ€žÃ‚ ±k, 2013, p. 14). The key point is to address the issue of the customer so that they can be paid. The second challenge is business sponsors and how they are recruited and integrated into the system. Also, obtaining the right team to perform the organizations mandates to achieve the set objectives is another challenge. The data management is also another drawback that might hinder the development and implementation of the business analytics. This process requires a significant amount of data from most, if not all departments and this may be a challenge as some may be uncooperative, resulting in incomplete records or inaccurate inf ormation. The next issue that might face the implementation of the business analytics is the length of time required to collect vital data that can be used in the system. Therefore, it might take time before full integration of this scheme in the decision-making process. Conclusion Decision-making process using evidence-based strategies is imperative in a number of ways. That is, it can help business analysts understand why things are happening, predict what will happen next, and come up with the best possible solution(s). The business analytics have been seen having some characteristics like; they depend on data, utilizes different mathematical methods to transform, analyze and summarize raw data. The summaries can help me in the form of graphs, and tables. The business analytics are vital as they add value to the raw data, and essential information or knowledge can be drawn. Despite the challenges companies faces when adapting this technique, there has been a great achievement. A lot has been done to incorporate it into the decision-making process so that firms can get a sound judgment of the situations. References BusinessVibes. (2014). 5 Steps to Implementing Business Analytics for Small Business. Retrieved September 26, 2016, from https://www.business2community.com/small-business/5-steps-implementing-business-analytics-small-business-0924778#if8wdjixrcrctvmb.97 Descriptive, Predictive, and Prescriptive Analytics Explained. (2016, May). Retrieved September 26, 2016, from https://halobi.com/2016/07/descriptive-predictive-and-prescriptive-analytics-explained/ Evans, J.R., and Lindner, C.H., 2012. Business analytics: the next frontier for decision sciences. Decision Line, 43(2), pp.4-6. IÃ…Å ¸Ãƒâ€žÃ‚ ±k, ., Jones, M.C. and Sidorova, A., 2013. Business intelligence success: The roles of BI capabilities and decision environments. Information Management, 50(1), pp.13-23. Patil, T.H. and Davenport, D.J., 2012. Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review. Provost, F., and Fawcett, T., 2013. Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.". Roldan, A. (2010). Implementing Business Analytics. Retrieved September 26, 2016, from https://atomai.blogspot.co.ke/2010/05/implementing-business-analytics.html Rouse, M., 2010. What is business analytics (BA)? - Definition from WhatIs.com. Retrieved September 26, 2016, from https://searchbusinessanalytics.techtarget.com/definition/business-analytics-ba Sharda, R., Delen, D., Turban, E., Aronson, J. and Liang, T.P., 2014. Business Intelligence and Analytics: Systems for Decision Support-(Required). Prentice Hall. Stubbs, E., 2011. The value of business analytics: Identifying the path to profitability (Vol. 43). John Wiley Sons. Waller, M.A. and Fawcett, S.E., 2013. Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), pp.77-84.