Explore Data Sources


Explore Data Sources

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MSOL 5106-3 Understanding Data

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The chart above demonstrates various data sources that could help solve the organizational problem or unequal racial distribution in student enrollment at Southern Crescent Technical College (SCTC). Typically, the four data sources that would help address the organization’s concern include SCTC students’ enrollment database, web services, industry reports, and customer service reviews and inquiries. The SCTC student enrollment database will be a crucial data source because it will provide information to identify the existence and extent of the unequal racial distribution in student enrollment at Southern Crescent Technical College (SCTC) over the years. It will indicate that since 2012, the college has only focused on admitting students from the three major racial groups, resulting in unequal ethnic distribution.

The web services will also provide information on the student enrollment at SCTC to acknowledge the unequal racial distribution in this institution’s student enrollment issues. For instance, the site  https://www.usnews.com/education/community-colleges/southern-crescent-technical-college-CC08047  contains information about the institution’s lower retention rate of 61% from 2012-2019, which comprise a red flag of the existence of the problem. Many students consider leaving a college that does not embrace or portray equal racial distribution. The industry reports on racial distribution in student enrollment in education would provide information about other similar colleges on racial distribution in student enrollment for comparison and objective decision-making (Mitroff & Sharpe, 2017). Customer service reviews and inquiries would also comprise an essential data source to understand the community and students’ perspectives to develop an appropriate solution.

Besides, the above sources of data are both qualitative and quantitative. The qualitative data source includes customer service reviews and inquiries (Akter et al., 2019). This source is qualitative since it contains non-numerical data related to the organizational concern. Typically, it includes the community and students’ views on the racial distribution in student enrollment. Student enrollment database, web services, and industry reports comprise the quantitative sources. This is because they contain numerical information related to the problem. For instance, the percentages of racial distribution of students enrolled in the institution since 2012.

Electronic and paper are the two primary data formats. Student enrollment database, web services, and industry reports take the electronic format, and customer service reviews and inquiries take the paper format. The data were created or generated by specific individuals and entities. For instance, with the staff’s help, the college administration generated the student enrollment database. This database comprised student enrollment elements like gender and race, thus essential for addressing the organizational problem. Specific individuals and institutions generated the data in the web services. For instance, the information on this site ( https://datausa.io/profile/university/southern-crescent-technical-college ) was generated by Data USA. The U.S. News generated the data on https://www.usnews.com/education/community-colleges/southern-crescent-technical-college-CC08047

The staff and external researcher generated the data in the customer service reviews and inquiries by asking students and community members about their perspectives on unequal racial distribution in student enrollment at SCTC using interviews and questionnaires. Data validity and reliability are important aspects in handling data tasks such as analyzing data or presenting information to stakeholders (Wang & Byrd, 2017). If the data is not accurate from the beginning, the results would also be inaccurate, leading to inappropriate decision-making based on assumptions. Intezari and Gressel (2017) stated that validating and determining the data’s reliability is vital to mitigate the defects in the decision-making. Therefore, it is crucial to establish the data validity and reliability to ensure the organizational problem is addressed adequately and objectively.

To confirm the data validity, specific aspects such as originality, attribution, and legibility must be assessed. Valid data should be original, accurate, complete, and traceable (Akter et al., 2019). It should also be from known and recorded sources. In this case, the web services data are valid because they are original, legit, and attributable. Reliability can be confirmed by looking at who conducted the research, data collection methods, or the sample size. The criterion of assessing who conducted the research fits the organization. Fundamentally, SCTC and other government agencies researched to generate the data on the web services sources, thus demonstrating reliability.

The data’s age can be identified by looking at the period when it was researched and published. For instance, data researched and published this year can show that its age is only a few months. By the time the data gets to the organizational leader, different individuals like hired researchers, staff members, and the institution’s data analysis team would have gone through it. The data has not been manipulated.

Finally, the data will help in solving the organizational problem by serving as a guideline towards reaching a practical solution (Ghasemaghaei, 2019). For instance, the data will demonstrate which programs have the highest unequal racial distribution in student enrollment at SCTC, informing the starting point institution. In other words, the college would ensure equality in the racial distribution in student enrollment by prioritizing students from marginalized racial groups to enhance equality.


Akter, S., Bandara, R., Hani, U., Wamba, S. F., Foropon, C., & Papadopoulos, T. (2019). Analytics-based decision-making for service systems: A qualitative study and agenda for future research. International Journal of Information Management48, 85-95.

Ghasemaghaei, M. (2019). Does data analytics use improve firm decision-making quality? The role of knowledge sharing and data analytics competency. Decision Support Systems120, 14-24.

Intezari, A., & Gressel, S. (2017). Information and reformation in KM systems: big data and strategic decision-making. Journal of Knowledge Management.

Mitroff, S. R., & Sharpe, B. (2017). Using big data to solve real problems through academic and industry partnerships. Current Opinion in Behavioral Sciences, 18, 91–96. https://doi- org.proxy1.ncu.edu/10.1016/j.cobeha.2017.09.013

Wang, Y., & Byrd, T. A. (2017). Business analytics-enabled decision-making effectiveness through knowledge absorptive capacity in health care. Journal of Knowledge Management.

Last Updated on April 10, 2021

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