DBA-831 Integrated Case Study

Introduction

Data is everywhere. There are considerable datasets in all fields which when well utilized and analyzed can yield valuable information that can be used in decision making such as in business. Today, data is being used in all major research areas such as in health and industries. Professionals such as statisticians are tasked with the collection of the data, its organization, analysis, and presentation and reporting (Osborne, 2013). However, how data is handled is crucial to ensure that useful information is obtained and not one that is misleading or violates ethical consideration(Osborne, 2013). Data sources suffer from potential inclusion of errors as well as missing values (Tang, 2014). As such, it can be argued that before data analysis is done, the data must be cleaned as data cleaning addresses these anomalies. This paper reviews the information given in a case study which includes narrative information and financial as well as supporting documentation. The data analysis conducted in the case study is also reviewed and using an approach in the review, the data is cleaned. To achieve the purpose of the paper, the questions presented in the case study, as well as the analysis of the data, are considered. This consideration helps in formulating the decisions based on the gathered information from the case study documentation.

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Current Environment

The case study under consideration is DBA-831 integrated case study which focuses on data analysis and data mining issues as well as the related ethical decisions. In the case study, Purple Cloud, a security company founded 15 years ago has witnessed continuous growth over the years. However, with the growth on the internet, one of the challenges the company has faced is keeping up with the ever-changing technology environment. As such, larger companies with proficient marketing budgets and technical staffs have often outmaneuvered Purple Cloud. In seeking means to accelerate product development, the founder, Tecnoti decides to adopt an acquisition strategy for companies that were promising in security technologies with the hope to increase market share and maximize new market opportunities. Purple Cloud recently acquired ABC Tech, an application design specialty. The founders of ABC Tech while basing their idea of increasing cybercrimes developed systems that would change data and personal security. While approaching the end-product, they marketed their product and were amazed by the growing sales each month which approached 250 million dollars in the first revenue year. However, the founders realized they had little knowledge about business and readily accepted an offer to sell their company to Purple Cloud.

The ABC Tech is unique and straightforward eliminating the need to acquire extensive infrastructures and add support staffs that are highly priced. However, since the technology must be implemented with precision and rapidly to ensure Purple Cloud is a market leader with unprecedented profits and revenue growth, the company must integrate ABC Techs staff and executives to help implement the technology. The challenge is that after the acquisition the expenses are out of margin while there are diminishing profit and income margin.

Business Issues Observed In "Sales Data Q1 Yr3,"

Based on the previous summary, several business concerns that can be addressed for the sales data provided. The revenue base and sale force indicate that there are five employees in the Houston office, 3 in Los Angeles and 2 in Chicago. The other issue is that the location with the highest sales in Houston perhaps because it has most employees. The other business issue that has been addressed is the sale of products from different industries, but the manufacturing industry has not been reported.

There are also some inconsistencies that can be identified in the data set provided. First, there is repeated data in the dataset provided. The first column which indicates the customer ID and the second column which is labeled "customer" has the same details about the customers' identity. This repetition is unnecessary as it gives the same information. For such an extended dataset, the repetition adds extra tasks to the researcher other than additional useful information. Secondly, when we look at the column labeled "customer city" and compare the same to the column labeled "customer state" they appear to be inconsistent for the fact that the city and the state mentioned are the same in both columns for some customers. Also, some entries seem to differ by small proportions implying that maybe the data in some cells have been duplicated. For example, the entry for customer 282 and customer 299 only seem to vary in the industry, city, and the salesperson but is identical for all other variables. It would possibly result from data entry errors. The other concern in the data presented is that there are missing values. For example, some of the data values in the survey column are not input. These values could either be missing from the data collection techniques or could have been omitted during the data entry stage. The data was cleaned by editing and imputation. The missing values were cross-checked and confirmed if they had been omitted intentionally or unintentionally.

While Marcoulides (2005) agreed that failing to clean data results to a range of problems, drawing false conclusions results from inclusion of errors, model missing specification, parameter estimation errors and incorrect analysis. Some of the ethical implications that may arise from data cleaning in this data set are that if the data contained some anomalies, they are probably likely to repeat. Again, if cleaning involves removing some columns such as the customer's city, the data may never correctly estimate the real value of sales made in a particular region. It would be hard to identify some variable measures such as the salesperson and their purchases in a specific area. According to data mining may result to three kinds of ethical issues which are suitability and validity of the techniques used in data mining, confidentiality, and privacy obligations during data mining, and the general aims of the data mining application employed. As a rule, any data cleaning or mining process must ensure it measures whether there are ethical issues in data cleaning actions. As indicated in the case study values alongside ethics are fundamental to the success of a business and its suitability.

The actions taken to clean the data are editing which ensures duplicated data is identified and unnecessary data eliminated. Secondly, validation is used to provide that the missing values are determined using a rule thumbing the dataset to avoid analyzing data with outliers which may lead to wrong decision making. Based on the Sales Data Q1 Yr3 I would recommend to the CEO that the data variables in the system should be ensured that they are updated from time to time to ensure that the chance of encountering missing values is reduced. Again, the inclusion of double entry or unnecessary information such as the inclusion of the ID and the customer's column is avoided. Otherwise, it only contributes to a large data set that does not yield useful information.

In addition, since the system that has been acquired from ABC Tech are unique in the market, and that the former founders of ABC Tech had no prior business experience, Purple Cloud should consider the application of newly developed or the use of revised theories that apply to the present business opportunity. At this point, the company should establish the existing opportunities and the challenges that the firm may face in the competitive market so that they may device modern theories and practices in their business. These theories are expected to guide the business operation and therefore increase its market share, increasing its revenue and the profit margin. I would suggest to the CEO, Purple Cloud, to use systems such as SWOT analysis in understanding both the external and internal environment of the business.

This research study is useful in practice as it helps in critically thinking and developing and putting together business ideas based on theories and practices in making a valid decision. Future research that could be conducted with respect to this research is the process of data mining which would ensure that some variable or the inclusion of the same do not lead to any ethical issues. Again, what methods could increase the profitability, market share of the firm into the future considering the business environment is dynamic.

References

Marcoulides, G. A. (2005). Discovering Knowledge in Data: an Introduction to Data Mining. Journal of the American Statistical Association, 100(472), 1465-1465. doi:10.1198/jasa.2005.s61

Osborne, J. (2013). Best Practices in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data. doi:10.4135/9781452269948

Tang, N. (2014). Big Data Cleaning. Lecture Notes in Computer Science, 13-24. doi:10.1007/978-3-319-11116-2_2