Management in organizations and firms of all sizes is required to make essential decisions that determine an entity’s future on a daily basis. Data-driven decision-making entails collecting data on essential performance indicators and analyzing patterns and trends to enhance insight into a situation for decision-making purposes (Provost & Fawcett, 2013). The use of data-driven decision-making enhances businesses’ ability to take advantage of available opportunities, forecast the future, and optimize operations (Zikmund et al., 2013). There are various methods of obtaining data for use in analysis, including surveys, historical records, or data mining (Provost & Fawcett, 2013). Businesses ought to broaden data-driven decision-making to seize the advantages that come with big data analytics.
The available data consists of historical financial performance indicators. The data may be used to compare expenses and revenue and display the business’s actual performance against the target performance.
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Figure 1
Comparing Revenue and Cost of Goods Sold per Month
A critical purpose of a business is to generate profit for the shareholders. Visualizations are essential tools for providing an overview of a business’s success in realizing the main purpose. Figure, 1 analyzes the trends and patterns of total revenue and costs of goods sold. The cost of goods sold in the first five months and a half exceeded the total revenue generated, meaning that the business experienced losses. However, the total revenue rose above the cost of goods sold in the sixth month while costs of goods continue to decline gradually before becoming constant in the last three months. The significant components of the cost of goods sold are overhead, labor, and materials. The graph shows that the five months recorded higher overhead and labor and material costs hence the high cost of goods sold. The business should focus on identifying the causes of high overhead, labor, and material costs for purposes of optimizing operations and profits.
Figure 2
Actual Monthly Profit against the Target Profit
A business may also compare the actual performance against the target performance using graphs. Figure, 2 compares the actual monthly profits against the target profit of 25% of the cost of goods sold. The actual business profits remained significantly below the target until the sixth month when the actual profit was slightly below 25% of the costs of goods sold. After the sixth month, actual profit improved above the profit goal and became constant after the tenth month. The business may compare the business strategies used in the various months to establish the most effective. The strategies used between and fifth and sixth month contributed to significant increase growth in the profits. Figure 2 also makes it easy to establish the years when the company underperformed hence allowing for timely action.
There are multiple statistical methods that can be utilized for data analysis. However, some complex models and visualizations would be only useful to an audience with prior knowledge of statistics (Zikmund et al., 2013). The line graphs used in the analysis are easy to explicate at a glance. The analysis indicates that the expenses exceeded the revenue in the first six months. The strategies applied after the fifth month were effective in reducing the expenses and improving the profits. The company attained the profit goals after the sixth month.
References
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data , 51-59.
Zikmund, W. G., Carr, J. C., & Griffin, M. (2013). Business Research Methods . Cengage Learning.
