5-2 - Milestone 4
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School
Southern New Hampshire University *
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Course
210
Subject
Information Systems
Date
Dec 6, 2023
Type
Pages
3
Uploaded by snehshah98
The data processing plan for the Running for Glory (RfG) organization encompasses
several key steps. Initially, I will gather data from the various sources I have established.
Subsequently, I will input this data into a robust database system. Then, proceed to categorize the
data based on specific areas of significance. Following this, a data validation process will be
implemented to ensure the data's quality and accuracy. As the final step, I will generate reports to
assess whether the organizational changes implemented by RfG indicate readiness for expansion.
Throughout these processes, paramount importance will be placed on safeguarding the data's
security, preventing any unauthorized access. It's worth noting that, as of now, there are no
regulations imposing limitations on the utilization of the data collected from the specified
sources.
1.
Collect data
2.
Enter data into database
3.
Categorize findings
4.
Validation
To initiate the process, we will gather data from diverse sources, including the CRM
database, focus groups, expert panels, interviews, social media pages, questionnaires,
observations, case studies, sports magazines, and corporate records. Establishing a clear timeline
for each step is essential to ensure the project's timely completion. The comprehensive data
collection from these various sources provides us with the necessary information for analysis and
processing. Without this data, we would lack the foundation to make presentations and evaluate
the impact of the changes on RfG’s performance.
After gathering the data, our next step will involve inputting it into a database system.
This phase enables us to identify and address any duplicate entries while efficiently organizing
the data. By entering the data at this stage, we streamline the categorization process and reduce
the likelihood of errors, ensuring a smoother workflow.
Segmenting the data into specific areas of significance serves a dual purpose. It not only
facilitates the detection of data duplications but also provides a clear understanding of the data's
content, storage location, and its relative value within the dataset (Boldon, 2023). The data
requiring categorization encompasses customer demographics, including their city and state
information, as well as both current and historical sales data. Additionally, data gathered from
surveys, interviews, focus groups, etc., must be categorized based on respondents' opinions or
responses.
Following the categorization of data, the subsequent crucial step involves data validation
to ensure its reliability and accuracy. This step holds significant importance because it is
imperative to verify the correctness of the entered data, ensuring the generation of accurate
reports. Validation serves the dual purpose of guaranteeing data accuracy and identifying
potential errors in entries made from our various data sources. Once the validation process is
successfully concluded, we can proceed to generate reports for presentation to Running for
Glory.
References:
What is data classification? definition and benefits
. Boldon James. (2022, August 4).
https://www.boldonjames.com/data-classification/what-is-data-classification-
definition/#:~:text=Classifying%20data%20makes%20it%20possible,hoarding%20vast
Sekaran, U., & Bougie, R. (2019).
Research Methods For Business: A Skill Building
Approach
(8th ed.). Wiley Global Education
US.
https://mbsdirect.vitalsource.com/books/9781119561248
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