BUS612 Study Guide
Unit 2: Data Analysis
2a. Identify, define, and differentiate data analysis methods and models
- What are the differences between the four analytical models to frame tactical and strategic questions?
- What analytics domains can be deployed in an organization?
There are four primary stages of analytics development, plus cognitive analytics, for five total 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.
The four primary stages for tactical and strategic analytical model managers may use to frame analytics are:
- Descriptive analytics: what happened?
- Decision / diagnostic analytics: why did it happen?
- Predictive analytics: what will happen?
- Prescriptive analytics: how can we make it happen?
As the organization progresses from one stage to another, the benefits of the analysis increase while the implementation complexity increases. The focus also shifts from what happened in the past to what we can do to impact the future. Stage 5, cognitive analytics, includes artificial intelligence analytics.
Analytics can influence nearly every domain in an organization, including finance, marketing, talent, customers, risk management, transportation, and sales.
Here are some industry examples of questions that can be answered at each analytics stage.
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. In contrast, organizations in the advanced implementation phases can usually perform the more complex and future-focused stages.
To review, see The Stages of Analytics Development.
2b. Evaluate, classify, and summarize data analysis findings (facts and insights)
- What are the differences between quantitative and qualitative data?
- How and when should 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 various 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 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.
To review, see The Difference between Qualitative and Quantitative and Qualitative and Quantitative Research.
2c. Apply data-driven knowledge, skills, and abilities to real-world datasets
- How are data-driven decisions used in everyday life?
More and more, our personal and professional daily decisions are based on data we have accessed from several sources. Businesses have been tapping into this resource for years to determine everything from who will most likely buy a product to who they should hire. A culture of data helps organizations make more informed and more accurate decisions to better utilize their resources.
Sociologists have used qualitative research methods to conduct research and obtain data to explain, predict or control an aspect of social reality. These research methods are increasingly used in the business world to examine and explain consumer behavior and other social interactions that may impact a business.
Before preparing any data analysis, you must "know" your data. This involves understanding the distribution of the data elements to help you determine what you need to analyze further. A single-variable data profile would yield a sample distribution, such as in the table below. In the profile example, the story isn't that the North region has the most sales but that the North region has more sales than the other three regions combined.
| Region | Sales |
|---|---|
| North | 60% |
| South | 22% |
| East | 13% |
| West | 5% |
| Region | Product A | Product B | Product C | Product D |
|---|---|---|---|---|
| North | 125,000 | 375,000 | 1,000,000 | 500,000 |
| South | 90,000 | 175,000 | 75,000 | 100,000 |
| East | 30,000 | 80,000 | 100,000 | 50,000 |
| West | 40,000 | 30,000 | 10,000 | 20,000 |
To review, see Research Design and Data-Driven Decisions.
Unit 2 Vocabulary
This vocabulary list includes the terms you will need to know to successfully complete the final exam.
- cognitive analytics
- decision / diagnostic analytics
- descriptive analytics
- frequency distribution
- predictive analytics
- prescriptive analytics
- qualitative data
- quantitative data