Leveraging Historical Data for Predictive Decision-Making- Case Study and Challenges

Leveraging Historical Data for Predictive Decision-Making- Case Study and Challenges

Cardello Lighting and Electric retailer is an independently owned company and has been operational for over 70 years. It supplies genuine commercial and residential electrical supplies to empower the surrounding community. The firm has a wide array of indispensable data that will be of great relevance in this context. The data is typically centred on electrical accessories, including ceiling lights, lamps, and home cinemas, among other electrical portfolios (CLES, 2021). Its grain data is expansive and features inventories taken each week for specific products, detailed transactions for clients as processed by the sales scanner, weekly average profits and losses, a record of daily expenses, and a list of out-of-stock items. The grain data further incorporates the recurring invoices made and received by the company. The firm makes major and minor decisions as per its strategic mission and vision. The decisions pertain to its brand, HR practices, and financial resolutions.

Analyzing the historical data will help the organization to make more informed decisions henceforth. The analysis will detect past trends and their potential to manifest in the future. It further presents the strengths and weaknesses of the company as a strategic way of achieving the best outcomes onwards. An example of a decision that will be made with the help of insights from historical data is on which regions to venture into based on buying habits. Besides, the data will help determine the most productive items in the market and maximize them while working on the least performing ones. However, countless issues hinder effective predictions. For instance, errors are unavoidable, and they are more likely to constrain accuracy (Brown, Kaiser & Allison, 2018). In addition, the information might be inconsistent, have low quality, ambiguous or duplicate. However, past data is an essential reference point in predicting future trends and making critical decisions.

References

Brown, A. W., Kaiser, K. A., & Allison, D. B. (2018). Issues with data and analyses: Errors,

underlying themes and potential solutions. Proceedings of the National Academy of Sciences, 115(11), 2563-2570.

Cardello Lighting and Electric Supply (CLES). (2021). “Say Hello to Cardello.” Retrieved on

12 Nov 2021 from: https://cardellolighting.com/

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This course teaches us how to prepare, summarize, and describe data. When we look at data over time, it often becomes possible to predict future data by looking at past data.

Leveraging Historical Data for Predictive Decision-Making- Case Study and Challenges

You should briefly describe an organization with at least two years of historical data, the type and grain of the available data, the important decisions that your organization makes, and how analyzing the historical data might help this organization to make more informed decisions going forward. Give at least one example of a decision that your organization needs to make that might be aided by insights from the historical data. What issues might you encounter that would keep the predictions from being 100% accurate? Be as specific as possible.

For example, suppose you own a flower shop, and you have sales data for the last two years. The grain of this data is daily sales totals by type of arrangement and customer zip code. Other examples of grain might be individual customer sales transactions or monthly sales. How might you use this information to help you make better business decisions over the next 12 months?

You may use the flower shop in your discussion post, but you are encouraged to come up with your own example from your own experience, observation, research, or imagination.