Module 1

Define Data-Driven Decision Making

• refers to the practice of making decisions based on assumptions

• refers to the practice of making decisions based on gut feeling and intuition

• refers to the practice of making decisions based on what users need

• refers to the practice of making decisions based on the analysis of data

Why is data important in decision making?

• It helps to make decisions faster

• It provides evidence to support better decisions

• It is easier to make decisions without data

• It allows for making decisions without any mistakes

Which of the following is NOT a step in the data-driven decision making process?

• Directly using raw data

• Identifying the problem

• Collecting and analyzing data

• Evaluating and implementing solutions

When making decisions using data, it is important to ensure that the data is:

• Clean, accurate, and relevant

• Only from one source

• Kept secret

• Outdated and irrelevant

Which technique uses historical data to predict future trends

• Descriptive analysis

• Predictive analysis

• Prescriptive analysis

• Qualitative analysis

What is the main focus of data-driven decision making in business?

• Avoiding data analysis

• Random decision making

• Optimizing marketing strategies and product development

• Focusing solely on customer opinions

How can data visualization aid in decision making?

• By making data more complex

• By providing clear insights at a glance

• By hiding important information

• By complicating the data interpretation

Which of these is NOT typically a source of data for decision-making?

• Sales reports

• Customer surveys

• Guessing what might happen

• Financial statements

Which of the following tools can help visualize data for better understanding and decision-making?

• Verbal descriptions

• Data tables

• Printed reports

• Graphs and charts

Which of the following actions is an example of applying data-driven decision-making in a business?

• Deciding based on how you feel that day

• Relying on rumors about what competitors are doing

• Making decisions without considering customer feedback

• Using historical sales data to predict future sales trends