BUS/721 v8 Data Table Worksheet

Identify 3-5 data sources necessary to execute the Leadership Optimization Business Plan, due in Week 8. Then, complete the prompts in each Data Source section below.

Ensure each response is thorough and complete supported with rationale edited carefully for grammar, punctuation, and spelling errors formatted according
to course-level APA guidelines (where applicable)

cited correctly (where applicable)

Review this list of guidelines for each prompt.

Prompt

Guideline

Overview

Briefly describe the data source you will use. Be sure to explain how you will obtain the data.

Examples:

survey

test instrument

database

experiment

observations

person-to-person transactions

Note: Include the formal name of the data source, if applicable.

Data Measurement

Categorize each data source as nominal (categorical numbers), ordinal (ex: Likert scale), interval (numerical ranges), or ratio (continuous numbers). Justify your response.

Then, describe how each data point will be measured.

Ex: If you used a survey as a data source, what kind of data will be extracted?

People

Describe the person/people/organization who own the data.

Note: This may include entities who own copyrights on a survey or test instrument.

Include relevant details, opportunities, or challenges from the list below:

Data security

Data privacy

Data richness

Data consistency

Data currency

Data validity

Data relevancy

Processes

Describe how the data will be captured or extracted. Include relevant details, opportunities, or challenges from the list below:

Data security

Data privacy

Data richness

Data consistency

Data currency

Data validity

Data relevancy

Systems

Explain the role of BI and ERP systems in the data collection, analysis, and storage. Include relevant details, opportunities, or challenges from the list below:

Data security

Data privacy

Data richness

Data consistency

Data currency

Data validity

Data relevancy

Data Source 1
Leadership Optimization Business Plan

In order to execute the Leadership Optimization Business Plan, the business may need to gather data from various sources. Some potential data sources could include:

i. Employee performance metrics: When optimizing leadership, it is essential to evaluate the performance of employees (Brungardt et al., 2006). Gathering individual and team performance data can help identify areas for improvement and opportunities for growth.

ii. Employee engagement surveys: Employee engagement is a essential factor in leadership optimization. Surveys can help assess employees’ engagement and identify potential improvement areas.

iii. Customer feedback: Customer feedback can provide valuable insights into how the organization is performing and where there may be areas for improvement (Brungardt et al., 2006). Gathering customer satisfaction and feedback data can help leaders make informed decisions about optimizing their operations.

iv. Financial data: Financial data can help leaders understand how their organization performs financially and identify areas for cost savings or revenue growth.

Industry benchmarks: Understanding how the organization compares to others within the same industry (Brungardt et al., 2006). Gathering data on industry benchmarks can help leaders detect areas where they may need to improve to remain competitive.

Overview

The data sources employed for the Leadership Optimization Business Plan are:

Employee engagement survey: This design measures employee engagement levels, job satisfaction, and overall organizational culture (Brungardt et al., 2006). The survey will be administered online to all employees, and the responses will be collected and analyzed anonymously.

Performance metrics database: This database contains performance metrics for each employee, including sales figures, customer satisfaction scores, and productivity levels (Brungardt et al., 2006)the data extraction from the company’s existing performance management system.

Leadership competency test: The designed test assesses the leadership competencies of the company’s managers and executives. The test will take place through an online service, and the results will be collected and analyzed anonymously.

Organizational climate Observations: The administration of observations by trained observers assessing the overall organizational climate, including communication patterns, leadership styles, and employee engagement levels (Brungardt et al., 2006). The observations will take place over several weeks, and the data will be recorded and analyzed.

Data Measurement

Their type of measurement categorizes the categorization of the data sources:

Employee engagement survey: This survey will yield ordinal data. The survey will use a 5-point Likert scale to measure employee engagement levels, job satisfaction, and organizational culture. The responses will be analyzed to identify trends and areas for improvement (Brungardt et al., 2006).

Performance metrics database: This database will yield interval or ratio data, depending on the measured metrics (Brungardt et al., 2006). For example, sales figures would be ratio data, while customer satisfaction scores might be interval data. The data points will be measured using established performance metrics and recorded in the database.

Leadership competency test: This test will yield ordinal data. The test will use a range of multiple-choice and short-answer questions to assess the leadership competencies of the company’s managers and executives (Brungardt et al., 2006). The responses will be analyzed to identify strengths and weaknesses in leadership skills.

Organizational climate observations: These observations will yield ordinal data. Trained observers will use a standardized observation checklist to record their observations of the organizational climate (Brungardt et al., 2006). The responses will be analyzed to identify areas where the organizational climate can be improved.

People

Depending on the source, the person/people/organization owning the data will vary. Here are some examples:

Surveys: The person/organization who conducted the survey will own the data. This data may include academic researchers, consulting firms, or the organization. Depending on the survey design and distribution, the data may be subject to copyright or intellectual property laws. Challenges related to data ownership and management may include ensuring data security and privacy, mainly if the survey includes sensitive information (Brungardt et al., 2006). Additionally, ensuring data validity and relevancy will be crucial to ensure the survey results are reliable and valuable for the Leadership Optimization Business Plan.

Databases: The organization itself will own the data collected in its databases. The data may be subject to legal and regulatory requirements, such as GDPR or HIPAA, depending on the nature of the data (Brungardt et al., 2006). Challenges related to data ownership and management may include ensuring data consistency and currency, primarily if the data utilization is for financial or performance reporting. Additionally, ensuring data security and privacy will be crucial to protect sensitive financial or employee data.

Person-to-person transactions: The organization will own the data collected through person-to-person transactions, such as interviews, focus groups, or observations. Challenges related to data ownership and management may include ensuring data privacy and validity, especially if the data is subjective or based on personal opinions (Brungardt et al., 2006). Data richness and relevancy will be crucial to capture a comprehensive picture of employee behavior and leadership practices.

Observations: The organization will own the data collected through observations of employee behavior and work processes (Brungardt et al., 2006). Challenges related to data ownership and management include ensuring data consistency and validity, mainly if different observers collect the data. Additionally, ensuring data currency and relevancy will be crucial to capture a timely and accurate snapshot of employee behavior and work processes.

Experiment: The organization will own the data collected through experiments, such as A/B testing of leadership strategies. Challenges related to data ownership and management may include ensuring data validity and relevancy, especially if the experiment involves a small sample size or limited duration (Brungardt et al., 2006). Ensuring data security and privacy will be crucial to protecting sensitive employee or customer data involved in the experiment.

Processes

The capturing or extracting data processes will vary depending on the data source. Here are some examples:

Surveys: Data attainment through a survey instrument, administered online, in person, or through the mail, and the data may be entered into a database or analyzed using a survey software tool. Opportunities for data richness may include open-ended questions, while challenges related to data consistency may include differences in interpretation or response bias (Peng et al., 2019). The survey instrument may include consent forms or anonymized response options to ensure data security and privacy for the intended users. Data validity and relevancy will ensure that the survey results are reliable and valuable for the Leadership Optimization Business Plan:

Databases: Data will be extracted from the organization’s databases using a query or data extraction tool. Opportunities for data richness include using multiple data sources or the ability to query large datasets. Challenges related to data consistency may include differences in data formats or data quality. Data security and privacy will be crucial to protecting sensitive financial or employee data (Peng et al., 2019). Additionally, ensuring data currency and relevancy will be crucial to capture a timely and accurate snapshot of employee behavior and leadership practices.

Person-to-person transactions: Data captured through interviews, focus groups, or observations of employee behavior. Opportunities for data richness may include the ability to probe for more detailed responses or to observe nonverbal behavior (Peng et al., 2019). Challenges related to data consistency may include differences in interpretation or observer bias. Data privacy and validity will be crucial to protecting the confidentiality of the interviewees or focus group participants.

Observations: Data will be captured by observing employee behavior and work processes. Opportunities for data richness may include capturing real-time data or observing subtle changes in behavior (Peng et al., 2019). Challenges related to data consistency may include differences in interpretation or observer bias. Data privacy and validity will be crucial to protecting the confidentiality of the employees observed.

Experiment: Data captured through an experiment, such as A/B testing of leadership strategies. Opportunities for data richness include controlling confounding variables or collecting multiple data types. Challenges related to data validity may include the need to design the experiment carefully to ensure that the results are reliable and relevant to the Leadership Optimization Business Plan (Peng et al., 2019). Ensuring data security and privacy will be crucial to protecting sensitive employee or customer data involved in the experiment. Additionally, ensuring data currency and relevancy will be crucial to capture a timely and accurate snapshot of the effectiveness of the tested leadership strategies.

Systems

BI (Business Intelligence) and ERP (Enterprise Resource Planning) systems play an essential role in data collection, analysis, and storage (Peng et al., 2019). Here are some details, opportunities, and challenges related to data security, privacy, richness, consistency, currency, validity, and relevancy:

Data collection: BI systems afford powerful tools for data analysis, such as data visualization, dashboards, and predictive analytics. These tools can help identify patterns, trends, and insights from the data. The opportunities for data richness and relevancy are high as these tools can help generate actionable insights relevant to the business needs. However, ensuring data validity and consistency can be challenging as different data sources may have different levels of accuracy or completeness (Peng et al., 2019). Data security and privacy are critical as these insights can contain sensitive business data.

Data analysis: BI systems provide powerful tools for data analysis, such as data visualization, dashboards, and predictive analytics. These tools can help identify patterns, trends, and insights from the data. The opportunities for data richness and relevancy are high as these tools can help generate actionable insights that are relevant to the business needs. However, ensuring data validity and consistency can be a challenge as different data sources may have different levels of accuracy or completeness (Peng et al., 2019). Ensuring data security and privacy is critical as these insights can contain sensitive business data.

Data storage: BI and ERP systems provide centralized data storage and management, which can help ensure data consistency and currency. The opportunities for data security and privacy are high as these systems provide robust security and access control mechanisms to protect sensitive business data (Peng et al., 2019). However, ensuring data validity and relevancy can be challenging as different data sources may have different levels of relevance or timeliness.

References

Brungardt, C., Greenleaf, J., Brungardt, C., & Arensdorf, J. (2006). Majoring in leadership: A review of undergraduate leadership degree programs. Journal of Leadership Education, 5(1), 4-25.

Peng, H., Li, J., Gong, Q., Song, Y., Ning, Y., Lai, K., & Yu, P. S. (2019). Fine-grained event categorization with heterogeneous graph convolutional networks. arXiv preprint arXiv:1906.04580.