IIBA CBDA Practice Test - Questions Answers, Page 11

Explanation: Developing the narrative is the process of creating a clear, concise, and compelling story that communicates the key insights, findings, and recommendations from the data analysis to the stakeholders1.Developing the narrative is an important step after completing the research study, the data analysis, and the high impact visuals, as it helps to bridge the gap between the data and the decision-making, to engage and persuade the audience, and to drive action and change2.Developing the narrative involves defining the purpose, audience, and message of the story, choosing the best format and medium to deliver the story, and using effective storytelling techniques, such as structure, context, emotion, and call to action3. Presenting the results to stakeholders is the process of delivering the data story to the intended audience, using the appropriate communication channels, methods, and tools4. Presenting the results to stakeholders is a subsequent step after developing the narrative, as it requires a well-crafted and well-prepared data story to be effective and impactful.Presenting the results to stakeholders involves planning and rehearsing the presentation, adapting to the feedback and questions, and evaluating the outcomes and impacts of the presentation5. Validating regression analysis is the process of checking the assumptions, accuracy, and suitability of a statistical model that estimates the relationship between one or more independent variables and a dependent variable. Validating regression analysis is a part of the data analysis step, not a step after completing the data analysis. Validating regression analysis involves testing the significance, fit, and residuals of the model, and comparing the model with alternative models or methods. Curating the data is the process of organizing, annotating, and preserving the data for future use, reuse, or sharing. Curating the data is a part of the data management step, not a step after completing the data analysis.Curating the data involves applying the data policies, standards, and best practices of the organization, and ensuring the quality, integrity, security, and accessibility of the data.

Explanation: According to the Introduction to Business Data Analytics: A Practitioner View, churn rate is a measure of customer attrition, or the percentage of customers who stop using a product or service over a given time period. Churn rate is an important indicator of customer satisfaction, loyalty, and retention. A high churn rate implies that customers are dissatisfied or have found better alternatives, which can negatively affect the revenue and growth of a business. A low churn rate implies that customers are satisfied and loyal, which can positively affect the revenue and growth of a business. In this situation, the telecommunications company wants to increase their efforts on retaining customers, so they need to track their churn rate and try to reduce it.

Explanation: According to the Guide to Business Data Analytics, a conceptual data model is a type of data model that describes the highest level of relationship between entities and represents how a business perceives its information. A conceptual data model is independent of any specific technology or implementation details. It focuses on the key concepts and their attributes, as well as the business rules and constraints that govern them. A conceptual data model can help communicate the business requirements and scope of the data analysis project to various stakeholders.

Explanation: According to the Guide to Business Data Analytics, a study population is the subset of the population that meets the eligibility criteria for the research question. A study population should be representative of the population of interest and relevant to the business problem or opportunity. In this situation, the survey will establish a poor study population because the students from local colleges and universities within a 50km radius may not reflect the characteristics, preferences, and behaviours of the potential online customers for the fifty-year-old brick and mortar business. The students may have different demographics, income levels, shopping habits, and needs than the target market for the business. Therefore, the survey results may not be generalizable or applicable to the population of interest and may not provide valid and reliable insights for predicting future sales.