Business Analytics focuses on data drove decisions. It tries to draw insights from the data and uses these insights to address business problems. This reflective essay will focus on learning from the first five modules of the Business data analytics course, learning with peers, and how it helps in personal and professional development.
The module first enabled me to understand the complex terminologies used in business analytics and how business analytics evolved over some time. This module helped me to bring out the differences between information, analytics, and intelligence aspects of the business. In this information age which started approximately during the 1970s digitalization helped to access and control the information. During the 1800s Frederick W.Taylor introduced the first formalized system of business analytics in the United States with a scientific management system. Information on data, data analytics, and business intelligence is the core driver of business analytics. Hindle and Vidgen (2018) state business analytics helps the business managers in decision making and helps to generate business value. This module helps to understand the descriptive, predictive, and prescriptive way of analyzing data. In a highly competitive market making a proper business, the decision is very important to retain a leading position in the market. Some examples of companies using analytics to drawn business intelligence are coca-cola for customer retention and acquisition, the use of big data analytics by Netflix for recommendation to its users, etc. According to Sahay and Amar (2016) business analytics, data analytics, and advanced analytics are the broad area of business intelligence. The module helps us to understand the types of data and how different types of data can be effectively used in deriving insights. In highly competitive industries role of big data analytics in drawing business, a decision is need of the hour. Findings from reviewing EY websites showed that databases of both proprietary and public are used to identify the risk score of potential business relationships.
The second module was all about decision making with data. According to Thomas, Jeanne, and Robert (2010), 40 % of business decisions made by managers are not based on facts but on the manager's gut. In case when data or facts are not available and when decisions are complicated decision based on individual gut feeling is invariable. Compare to other business processes decision making requires systematic review and checking error of data incorporated in decision making. Data along with analytics helps to make improved decisions. Shah et al (2012)
states that good data won't guarantee good decisions. The most common forms of obstacles in better decision making are relying on findings align to prior beliefs, intuitive reasoning, and judgemental decisions, etc.
Module three helped in understanding how different is data analysis in comparison to business research. There exists a complementary relationship between data analysis and business research. I found data analysis is the process of collecting and analyzing the data to generate business decisions. Business research involves providing decisions and focuses on reasoning by reducing errors. Understanding the relationship between data analysis and business research is critical in making decisions relating to the missions, goals, strategies, and tactics of the organization. Business research has a value to the extent it helps the managers in making a proper decision by making use of new information to achieve the goals of the business. By reading Business Research Methods, the identified research challenges are ill-defined management problems, ethical research process issues, Purpose based data analytics, hidden agenda of managers, legitimacy of research, and inexperienced researcher. The case study on Chipotle Mexican Grill helped me in understanding how business performance improved by addressing two fundamental problems highlighted by business research. In this case study, there is a mention of using a traceability program and quantified platform which offers real-time data and helps in understanding consumer behavior.
Module four focused on data visualization. How it helps in digging data and helps to draw insights. Data visualization helps in presenting the data in good visuals and helps in easy understanding of the business performance. Many charts can be created which allows viewers to explore data and draw insights quickly and clearly. Visualization is effective when we design it looking into the right audience and the right reasons. Some of the most commonly used charts in visualizing data are bar charts, line charts, and pie charts. Wakeling et al (2015)considered different visualizations and found out that the accuracy of interpreting tabular data is low, simple charts are better and quicky interpreted compared to advanced and unfamiliar graphs. In present-day business, peoples are using storytelling concepts by making use of visualizations.
Data visualization was further explained in module 5. Visualizationallow people to analyze and communicate data directly. The display can be designed which supports more accuracy and avoid extreme biases. Szafir and Danielle (2018) stated that 3d marks, truncated axes, and other design choices create visualizations that are difficult to read and also stated that avoiding known bad practices leads to more accurate and meaningful data communications. Visualizations are not effective always. It can be used effectively for exploratory data analysis but it also has limitations. They are still an effective tool for identifying the pattern. Coordination between data science and other science is needed to move towards visual solutions.
The course helped me to understand data and how it can be used to derive insights for making business decisions. Through the case study provided in the course, I understand the use of data analytics in addressing real-life problems faced by the industries. Data may always not derive a workable solution but it helps us in knowing historical trends and how to use these trends in future decision making. The course also highlighted the role of big data analytics in a highly competitive industry. I also came to know-how making a bad decision will affect the growth of the industry. This course also helped me in understanding the importance of visualization in presenting business problems. I found out that the adoption of business analytics is very important in today's world where businesses often face huge and volatile data.
Hindle, G.A., & Vidgen, R. (2018): Developing a business analytics methodology: A case studyin the food bank sector. European Journal of Operational Research, 268(3), 836-851. https://doi.org/10.1016/j.ejor.2017.06.031
Sahay, A. (2018): Business Analytics: A Data-Driven Decision Making Approach for Business, Volume 1, Business Expert Press, New York, NY, Chapter 2
Shah, Shvetank, Horne, Andrew, Capellá, Jaime (2012): Good data won’t guarantee good decisions
Szafir Danielle August. (2018):The Good, The Bad, And The Biased: Five Ways Visualisations Can Mislead (And How To Fix Them)https://interactions.acm.org/archive/view/july-august-2018/the-good-the-bad-and-the-biased.
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