## BUS607 Study Guide

### Unit 4: Types of Data

#### 4a. Distinguish between different types of quantitative and qualitative data

• How are quantitative and qualitative data different?
• How and when can you use quantitative versus qualitative data?

Data is at the heart of DDDM. Numerous types of data and sources must be included in a robust DDDM initiative. Every type of data must be extracted from a source, transformed into a standard format acceptable for the data warehouse, and then made available for analysis. Once data is prepared, analytics are deployed to create both hindsight and foresight analytics. You will need to understand these types of analytics and how they relate to successful DDDM initiatives.

Quantitative data is based on counting or measuring the attributes of a population. They are always numbers that specify weight, height, length, population, etc. Quantitative data can be discrete, resulting from counting with only certain numerical values, or continuous, resulting from measuring with a variety of values.

Qualitative data is based on categories or descriptions of a population. They are usually words or letters, such as color, street name, automobile name, etc. Qualitative data includes the color of hair, year in college, month, etc.

Quantitative Data Qualitative Data
Definition Quantitative data are the result of counting or measuring attributes of a population. Qualitative data are the result of categorizing or describing attributes of a population.
Data that you will see Quantitative data are always numbers. Qualitative data are generally described by words or letters.
Examples Amount of money you have
Height
Weight
Number of people living in your town
Number of students who take statistics
Hair color
Blood type
Ethnic group
The car a person drives
The street a person lives on

Quantitative and qualitative data can both be used to summarize frequency distributions. Since quantitative data are always numeric, it is more often utilized for descriptive (summary) statistics than qualitative data. Quantitative data can also be used to inform a broader understanding of a population through inferential statistics.

#### 4b. Analyze the role of big data

• What are the benefits of utilizing big data in new product development?
• How is big data different from other types of data or data processing?

Big Data refers to data that is so large, fast, or complex that it's difficult or impossible to process using traditional methods. It contains extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Technology has turned the average customer into a robust source of a variety of data. The magnitude, rapidity, and richness of the data generated have had a profound effect on new product development (NPD). Three NPD phases, in particular, can be affected by big data, including:

1. Idea and concept generation, through the collection of large amounts of customer feedback and suggestions
2. Design and engineering, through market research input from customers on their needs, wants, and opinions
3. Testing and launch, through timely feedback on new and improved product designs and applications

To review, see Big Data in New Product Development.

#### 4c. Compare different types of analytics and their role in building a data-driven decision-making enterprise

• What are the five stages of analytics development, and what question can be answered at each stage?
• How do the stages of analytics development integrate into the DDDM Change Model?

There are four primary stages of analytics development plus cognitive analytics for a total of five stages. A different question can be answered at each stage, with the early stages delivering hindsight and the later stages delivering foresight. The value of the results improves as an organization moves along the spectrum along with the complexity of implementation.

There are five stages of analytics development are:

1. Descriptive analytics
2. Decision / diagnostic analytics
3. Predictive analytics
4. Prescriptive analytics
5. Cognitive analytics

At each stage of analytics, certain questions can be answered.

These stages of analytics development can be implemented as an organization proceeds along the continuums of the DDDM Change Model. The less complex and past-focused stages can usually be implemented in the early phases, while the more complex and future-focused stages can usually be performed by organizations in the advanced phases of implementation.

To review, see Big Data Analytics and Examples of Analytical Development.

#### Unit 4 Vocabulary

This vocabulary list includes the terms that you will need to know to successfully complete the final exam.

• analytics development
• descriptive statistics
• frequency distributions
• inferential statistics
• qualitative data
• quantitative data