Big data is everywhere, and various businesses around the world are driven by big data. While some businesses rely on big data for organizational decision making, this does not mean that the implications and applications of big data are properly used to ensure optimal effectiveness for the organization
For this scenario, you have been appointed as a business analyst for Big D Incorporated, charged with providing authoritative recommendations to the Board of Directors. As the business analyst, the recommendations that you provide will be based upon data calculated from statistically appropriate formulas. Be reminded that you are not the company’s statistician yet. However, as the business analyst, you are therefore responsible for interpreting statistical data and making the appropriate recommendations.
Big D Incorporated was offered a series of business opportunities, and it is your job as the business analyst to provide expert insight and justification for recommendations regarding these potential prospects.
Big D Incorporated has a business opportunity to provide two different types of information to a new client. As the business analyst, you are tasked to assess the financial feasibility of this opportunity. The new client is a retailer and looking to expand its product offerings. However, the client is requesting Big D Incorporated to assist in the decision-making process.
Prepare a presentation that addresses the following:
Explain the difference between nominal and ordinal data.
List 3 qualitative attributes of outdoor sporting goods that the client may want to ask consumers. Make sure 1 of the qualitative attributes is nominal.
For each ordinal attribute, assign names for the endpoints of a 5-point rating scale.
Explain the difference between interval and ratio data.
List 2 quantitative attributes of outdoor sporting goods that market researchers might want to measure.
Explain the difference between a population and a sample.
Full Answer Section
Big D Incorporated has been offered a business opportunity to provide two different types of information to a new client, a retailer looking to expand its product offerings. The client is requesting assistance in the decision-making process.
As the business analyst, I have assessed the financial feasibility of this opportunity and recommend that Big D Incorporated accept the contract. This presentation will explain the data analysis and statistical methods used to make this recommendation.
Data Types
Before proceeding with the analysis, it is important to understand the difference between the different types of data.
Nominal data: Nominal data is categorical data that has no inherent order. For example, the color of a shirt is nominal data.
Ordinal data: Ordinal data is categorical data that has a natural order. For example, the rating of a product on a scale of 1 to 5 is ordinal data.
Interval data: Interval data is numerical data that has a constant interval between each value. For example, temperature is interval data.
Ratio data: Ratio data is numerical data that has a true zero point. For example, height is ratio data.
Qualitative Attributes of Outdoor Sporting Goods
Here are three qualitative attributes of outdoor sporting goods that the client may want to ask consumers:
- Brand: This is a nominal attribute, as there is no inherent order to different brands.
- Quality: This is an ordinal attribute, as consumers can rank the quality of different products on a scale from 1 to 5.
- Durability: This is an ordinal attribute, as consumers can rank the durability of different products on a scale from 1 to 5.
Rating Scale for Ordinal Attributes
Here are some example names for the endpoints of a 5-point rating scale for the ordinal attributes listed above:
Quality:
- Very poor
- Poor
- Fair
- Good
- Very good
Durability:
- Very poor
- Poor
- Fair
- Good
- Very good
Quantitative Attributes of Outdoor Sporting Goods
Here are two quantitative attributes of outdoor sporting goods that market researchers might want to measure:
- Price: This is interval data, as there is a constant interval between each dollar value.
- Weight: This is ratio data, as there is a true zero point (no weight).
Population and Sample
The population of interest is all outdoor sporting goods consumers. However, it is impractical to survey the entire population. Therefore, a sample of the population will be selected.
A sample is a representative subset of the population. It is important to select a sample that is representative of the population so that the results of the survey can be generalized to the entire population.
Conclusion
I recommend that Big D Incorporated accept the contract to provide two different types of information to the new client. The first type of information is qualitative data on brand, quality, and durability. The second type of information is quantitative data on price and weight.
The data collected from this survey will help the client to make informed decisions about expanding its product offerings. For example, the client may want to focus on products from brands that consumers perceive to be high quality and durable. The client may also want to focus on products that are priced competitively and have a weight that is appropriate for the target market.
Statistical Methods
The statistical methods that will be used to analyze the data collected from this survey include:
- Descriptive statistics: Descriptive statistics will be used to summarize the data and provide insights into the characteristics of the sample. For example, descriptive statistics can be used to calculate the mean, median, and mode of each variable.
- Inferential statistics: Inferential statistics will be used to draw conclusions about the population based on the data collected from the sample. For example, inferential statistics can be used to test hypotheses about the differences between groups or the relationships between variables.
Financial Feasibility
The financial feasibility of this opportunity has been assessed by considering the following factors:
- Cost of data collection: The cost of data collection will include the cost of developing the survey, recruiting respondents, and collecting the data.
- Revenue from the contract: The revenue from the contract will depend on the scope of work and the pricing structure agreed upon with the client.
Based on this analysis, I believe that this opportunity is financially feasible. The revenue from the contract is expected to exceed the cost of data collection.
Recommendation
I recommend that Big D Incorporated accept the contract to provide