1.5 AN INTRODUCTION TO THE SOAR ANALYTICS MODEL

Question 1

What are the key skills associated with having an analytics mindset

Answer 1

Individuals with an analytics mindset should be able to:

• Ask the right questions.
• Extract, transform, and load relevant data
• Apply appropriate business analytics techniques.
• Interpret and share the results with stakeholders

Question 2

What is the SOAR analytics model, and how is it used in the text?

Answer 2

The SOAR analytics model is a framework used throughout the text to guide the process of working with data. It consists of the following steps:

Specify the question: Formulate a business question that can be answered with data (covered in Chapter 1).

Obtain the data: Gather relevant data and understand their characteristics to determine their suitability for answering the question (covered in Chapters 2-3).

Analyze the data: Employ data manipulation and statistical techniques to analyze the data (covered in Chapters 4-5)

Report the results: Effectively communicate the findings to decision-makers and other interested parties (covered in Chapter 6).

Each step of the SOAR analytics model corresponds to a characteristic of the analytics mindset and serves as a framework for working through the data analysis process.

Question 3

Exibit 1.4: The Recursive SOAR Analyitics Model

Answer 3

Exhibit 1.4 illustrates the cyclical nature of the SOAR analytics model, indicating that it is an iterative process.

After completing all the steps of the model, both the analyst and the decision-maker often gain more knowledge and are better equipped to ask deeper and more refined questions.

The model is recursive, with each answer leading to more questions and vice versa.

Question 4

What is the difference between obtaining the data and analyzing the data, the second and third steps in the SOAR analytics model?

Answer 4

In the SOAR analytics model, “obtaining the data” (the second step) involves the process of collecting and gathering relevant data from various sources, while “analyzing the data” (the third step) refers to the examination and interpretation of the collected data to derive insights and draw conclusions.

Obtaining data focuses on data acquisition, while analyzing data focuses on uncovering patterns, trends, and information within the acquired data.

Question 5

How do the EY analytics mindset and the SOAR analytics model sync up with each other?

Answer 5

The EY analytics mindset and the SOAR analytics model align by emphasizing a holistic approach to data analysis.

The EY analytics mindset promotes a culture of data-driven decision-making and continuous learning, which complements the recursive nature of the SOAR model, where analysis leads to new questions and insights, fostering an environment of ongoing improvement and refinement in decision-making through analytics.

Question 6

What is the first component of the SOAR analytics model?

Answer 6

The first component of the SOAR analytics model is “SPECIFY THE QUESTION,” where management and analysts define the questions they need to answer for informed decision-making

Question 7

Why is it important to carefully specify the question in the SOAR analytics model?

Answer 7

Carefully specifying the question is crucial as it ensures the gathering of appropriate data and performing the right type of analysis, increasing the likelihood of making informed decisions.

Question 8

Can you provide examples of questions that decision-makers have asked in the context of the SOAR analytics model?

Answer 8

McDonald’s: How is the demographic profile changing for typical restaurant customers?

University of Arkansas: Will purchasing more efficient copiers yield long-term savings?

Michael’s: What’s the shipping time difference between sourcing products from Mexico and Indonesia?

Walmart: How will government-imposed tariffs affect product sourcing to the United States?

Netflix: How does comprehensive health insurance for employees impact the break-even sales level?

Costco: Which smartphone is the most profitable in Florida, and is it different from California’s most profitable smartphone?

Question 9

How does narrowing the scope of a question benefit the analytics process?

Answer 9

Narrowing the scope of a question allows for a more focused analysis, making it easier to find the required data, perform the analysis, and provide a meaningful answer.

Question 10

Why is it essential for business analysts to ask the right questions?

Answer 10

Business analysts play a critical role in improving an organization’s analytics by asking the right questions, as data won’t yield insights unless the right questions are posed.

Question 11

What considerations do data analysts make when obtaining data in the SOAR analytics model?

Answer 11

Data analysts consider factors such as the types of data needed, data availability, data sources, accessibility, data quality, errors, missing data, usability, biases, ethical acquisition, privacy protection, and data relevance and reliability

Question 12

Why is it important to consider the types of data needed when obtaining data?

Answer 12

Considering the types of data needed ensures that the collected data are relevant to answering the specific question at hand.

Question 13

Why is data quality and the absence of errors important when obtaining data?

Answer 13

Data quality and error-free data are crucial to ensure the accuracy and reliability of the analysis results.

Question 14

How can data bias be addressed when obtaining data?

Answer 14

Data analysts need to be aware of potential biases in the data and take steps to mitigate them to ensure fair and accurate analysis.

Question 15

What ethical considerations are relevant when obtaining data?

Answer 15

Data must be obtained ethically, and measures should be in place to protect people’s privacy and data rights during the collection process.

Question 16

What are the characteristics of data that analysts ideally aim to use?

Answer 16

Analysts aim to use data that are both relevant (closely connected to the question) and reliable (accurate, valid, and unbiased).

Question 17

What does data integrity refer to in the context of reliable data?

Answer 17

Data integrity encompasses the combined accuracy, validity, and consistency of data stored and used over time, ensuring their reliability.

Question 18

Why is it important for analysts to understand the advantages and disadvantages of using flawed data?

Answer 18

Analysts must understand the pros and cons of using imperfect data because not all data are both relevant and reliable, and they may need to make informed decisions when dealing with flawed data

Question 19

What types of data do business analysts ideally want to use?

Answer 19

Business analysts prefer using well-organized and structured data, like financial statements, but they also acknowledge the value of disorganized data, such as social media trends (e.g., Instagram or Twitter).

Question 20

How do companies acquire the data they need for analysis?

Answer 20

Companies can either own the data they need or acquire it from external sources, depending on the situation

Question 21

What factors should a company consider when deciding to acquire and use external data?

Answer 21

When deciding to acquire external data, a company should weigh the cost of use and access against the potential value provided by analyzing the data.

Question 22

Exibit 1.5: Types of Analytics

Answer 22

Question 23

What are the five types of analytics?

Answer 23

• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics
• Adaptive/Autonomous Analytics

Question 24

What is Descriptive Analytics?

Answer 24

Descriptive analytics focuses on answering the question, “What happened?” It involves summarizing historical data to understand past events and trends

Descriptive analytics answers questions like “What happened?” and “What is happening?”

Question 25

What is Diagnostic Analytics?

Answer 25

Diagnostic analytics addresses the question, “Why did it happen?” It aims to uncover the causes of past results and understand why they differ from expectations.

Diagnostic analytics addresses questions such as “Why did it happen?” and “What are the causes of past results?”

Question 26

What is Predictive Analytics?

Answer 26

Predictive analytics aims to answer, “Will it happen in the future?” It involves using data to make predictions about future events or outcomes, often using probability

Predictive analytics answers questions like “Will it happen in the future?” and “What is the probability something will happen?”

Question 27

What is Prescriptive Analytics?

Answer 27

Prescriptive analytics deals with the question, “What should we do, based on what we expect will happen?” It provides recommendations on how to optimize performance based on potential constraints.

Prescriptive analytics answers questions such as “What should we do, based on what we expect will happen?” and “How do we optimize our performance based on potential constraints?

Question 28

What is Adaptive/Autonomous Analytics?

Answer 28

Adaptive or Autonomous analytics involve continuously learning and improving using artificial intelligence (AI) and learning from past and current events.

Adaptive/Autonomous analytics focus on continuous learning using AI and learning from past and current events, enabling data-driven decision-making

Question 29

Exibit 1.6: Analytics Accendency Model

Answer 29

Question 30

What is the “rightsight” skill in the context of adaptive/autonomous analytics?

Answer 30

The “rightsight” skill in adaptive/autonomous analytics involves learning to ask the right question of your data, at the right time, in the right place, for the right object or event, and in the right context.

This skill ensures actionable information and knowledge can be delivered effectively to the right people in the right form at the right time.

Question 31

What are the three most popular analytics software tools used extensively in the business world?

Answer 31

The three most popular analytics software tools are Excel, Tableau, and Power BI.

These tools are commonly used by business analysts and are valuable for careers in business analysis, data science, decision-making, or anyone regularly addressing business questions

Question 32

Why is it important to report the results of an analysis in the SOAR analytics model?

Answer 32

Reporting the results is crucial to communicate findings to decision-makers.

Reports can take various forms, including written reports, statistical results, charts, and graphics.

Reports can be static or dynamic, with real-time updates delivered through dashboards, which provide a graphical summary of continuously tracked measures.