Introduction
In today’s world, the popularity of business analytics is increasing; companies start to use its tools to guide their decision-making processes. This paper presents an overview of business analytics, evaluating its aspects and applicability. The report features opportunities and challenges business analytics presents. In addition, it addresses the features and purposes of the three Statistical Analysis System (SAS) software tools commonly used in the field.
Overview of the Field
Business analytics can be considered a relatively new field that is gaining popularity in academic and business circles. It can be seen as an intersection of operational research (OR), information systems, and artificial intelligence (AI) (Granville). This discipline studies the use of mathematical, statistical, and network science methods along with machine learning to enhance decision-making processes (Delen and Ram 2).
Business analytics is a subset of comprehensive analytics that uses its techniques and principles to develop solutions to complex business problems. Organizations can apply the findings and inventions in the field to predict possible outcomes of their choices, as well as analyze and optimize their performance. In addition, companies can use business analytics tools to forecast customers’ preferences, evaluate potential patient outcomes, and manage traffic networks.
The increased public attention to business analytics can be determined by several factors. First, in today’s world, businesses encounter global competition and are under pressure to make timely and effective decisions (Delen and Ram 3). In addition, customers become more demanding because they are exposed to a vast variety of products, which means that organizations should predict individuals’ needs and make business choices accordingly. Second, technological progress allows for higher affordability of software and access to a significant amount of data. Moreover, data processing tools have improved as well, which means that companies can analyze complex data in a short time.
Finally, successful organizations have shifted from intuition-driven decision-making to evidence-based ones (Delen and Ram 4). The new approach to management allows for making more effective decisions and avoid potential negative outcomes.
Opportunities and Challenges of Business Analytics
As mentioned above, business analytics allows for enhancing business decisions and predicting their consequences. The examples of opportunities presented by the field include developing models for customer relationships management, the utilization of sentiment analysis to obtain clients’ feedback on products, marketing campaign optimization, financial planning, and risk mitigation. In addition, business analytics can be used for product pricing, detection of fraud, actuarial estimation, and employee retention and management.
The primary challenge associated with business analytics is that some companies may not have enough resources to adopt necessary tools and use them to guide decision-making processes. For instance, it may be difficult for some organizations to transform from the traditional management style to a contemporary one and to implement changes in their management strategy. Companies may be challenged by the necessity to adopt technologies and invest in business analytics tools. Another potential problem is that the difficulty of justifying the return on investment (ROI) in business analytics tools.
The reason for it is that such an approach to decision-making may not provide an immediate return (Delen and Ram 4). It may be challenging for organizations to analyze when the value gained from the implementation of business analytics will outweigh the investment.
Main Purposes and Features of SAS Software Tools
SAS software tools commonly used in business analytics include Visual Data Mining and Machine Learning, Visual Analytics, and Econometrics. The first one combines visualization, data preparation, analytics, and model assessment tools in a single program (SAS® Visual Data Mining 1). Its purposes are to help companies to analyze complex data and develop predictive models, reduce latency between data and deployment, and work with unstructured information. The features of the program include a drag-and-drop interactive interface, automated code creation, the Model Studio tool, in-memory processing, workload and failover management, intelligent autotuning, and concurrent access to the same data by many users.
The purpose of the Visual Analytics tool is to assist organizations in exploring data, creating its visualizations, and examine existing trends. Its main features are self-service discovery and analysis (SAS® Visual Analytics 3). The tool offers network diagrams, auto-charting, key-value visualization, automated evaluations of variables, the creation of aggregated or derived data items, and path analysis.
Finally, Econometrics is a tool that offers economic techniques to simulate business processes. Its purpose is to help companies to solve complex finance-related problems, drive scientific decision-making, and perform count regressions (SAS® Econometrics 1). The key features of the program include linear, spatial autoregressive, and hidden Markov models, filtering and decoding of data, support of negative binomial and Poisson regressions, and estimation of stochastic frontier production.
Conclusion
Business analytics tools can be valuable for companies’ success as they can guide organizational decision-making and predict the outcomes of choices. Although some firms may encounter challenges during the implementation of business analytics technologies, such a step has significant benefits. The examples of software tools commonly used in the field are Visual Data Mining and Machine Learning, Visual Analytics, and Econometrics.
Works Cited
Delen, Dursun, and Sudha Ram. “Research Challenges and Opportunities in Business Analytics.” Journal of Business Analytics, vol. 1, no. 1, 2018, pp. 2-12.
Granville, Vincent. “Difference Between Machine Learning, Data Science, AI, Deep Learning, and Statistics.” Data Science Central. 2017.
SAS® Econometrics. 2018. Web.
SAS® Visual Analytics on SAS® Viya®. 2018.
SAS® Visual Data Mining and Machine Learning. 2019. Web.