IIBA CBDA - Certification in Business Data Analytics (IIBA - CBDA)

A Treemap chart is a type of chart that displays hierarchical data as a set of nested rectangles, where the size and color of each rectangle represent a quantitative value and a categorical variable, respectively1. A Sunburst chart is a type of chart that displays hierarchical data as a set of concentric circles, where the size and color of each slice represent a quantitative value and a categorical variable, respectively2. Both charts are useful for visualizing hierarchical data structures, but they have different advantages and disadvantages. One advantage of using a Treemap chart over a Sunburst chart is that a Treemap chart is optimized to include more data points, as it uses a Cartesian coordinate system that fills the entire rectangular space of the chart area, whereas a Sunburst chart uses a polar coordinate system that leaves empty spaces in the corners of the chart area3. This means that a Treemap chart can display more levels of hierarchy, more categories, and more details than a Sunburst chart, without compromising readability or clarity. Therefore, the correct answer is B, as a Treemap chart is optimized to include more data than a Sunburst chart.

References:1: Treemap Charts in Excel - Easy Excel Tutorial, 2: Sunburst Chart in Excel - Easy Excel Tutorial, 3: Breaking down hierarchical data with Treemap and Sunburst charts | Microsoft 365 Blog

A bullet chart is a type of bar chart that shows progress towards a goal or performance against a reference line1. It consists of a bar representing the featured measure, a reference line denoting a target or threshold, and a background with qualitative ranges (such as poor, fair, good, excellent)2. In this case, the featured measure is the customer satisfaction rate for 2018, the reference line is the target of 70%, and the background ranges are 0-50% (poor), 50-70% (fair), 70-90% (good), and 90-120% (excellent). The chart also shows a thin black bar representing the customer satisfaction rate for 2017, which can be used for comparison. From the chart, we can see that the 2018 customer satisfaction rate is at 90%, which falls in the excellent range and exceeds the target of 70%. We can also see that the 2017 customer satisfaction rate was at 70%, which falls in the good range and meets the target. Therefore, the correct answer is D, as it summarizes both the 2018 and 2017 customer satisfaction rates and their relation to the target and the ranges.

References:1: Understanding and Using Bullet Graphs | Tableau, 2: Bullet Charts - What Is It And How To Use It - JSCharting

According to the Guide to Business Data Analytics, a narrative is a way of communicating the results of data analysis in a clear, concise, and compelling manner. A narrative should include the following elements: the purpose of the analysis, the main findings and insights, the implications and recommendations, and the evidence and reasoning. A narrative should also use appropriate language, tone, and style for the intended audience and medium. A narrative can enhance the impact and value of the data analysis by providing context, explanation, and interpretation of the data, as well as by highlighting the key messages and actions. A dashboard that lacks narrative may not be able to convey the full meaning and significance of the data, and may not be able to engage the audience or influence their decision-making.

References: Guide to Business Data Analytics, page 81-83; CBDA Exam Blueprint, page 8; [Introduction to Business Data Analytics: A Practitioner View], page 25-26.

According to the Guide to Business Data Analytics, a change strategy is a document that outlines the approach and plan for managing the change resulting from the data analysis and the proposed solution. A change strategy should include the following elements: the vision and objectives of the change, the scope and impact of the change, the stakeholders and their roles and responsibilities, the communication and engagement plan, the training and development plan, the transition and implementation plan, the risk and issue management plan, and the evaluation and measurement plan. A change strategy can help ensure that the change is aligned with the business goals, that the stakeholders are informed and involved, that the risks and issues are identified and mitigated, and that the benefits and outcomes are realized and sustained.

References:Guide to Business Data Analytics, page 84-85;CBDA Exam Blueprint, page 8; [Introduction to Business Data Analytics: A Practitioner View], page 26.

According to the Guide to Business Data Analytics, one of the tasks under the domain of “Identify the Research Questions” is to identify sources of business information to consider. This task involves reviewing existing business information, such as documents, reports, databases, and systems, to determine what data is available, relevant, and reliable for answering the research questions. This task also involves identifying any gaps or limitations in the existing information and proposing ways to address them.

References: Guide to Business Data Analytics, page 18-19; CBDA Exam Blueprint, page 6.

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According to the IIBA® Guide to Business Data Analytics, analytics work should be driven by well-defined business problems or opportunities that are aligned with the organization’s strategic objectives1. Having an abundance of data does not necessarily mean that all of it is relevant, reliable, or useful for the analytics purpose. Therefore, the analytics team should focus on using the data to answer a limited number of key questions that are derived from the business context and that can generate actionable insights and outcomes. This approach can help the analytics team prioritize the most important data sources, methods, and tools, as well as avoid wasting time and resources on analysis that is not impactful or meaningful for the organization.

References:1: IIBA® Guide to Business Data Analytics, Chapter 3: Business Data Analytics Process, page 24-25

Prescriptive analytics is the type of analytics that provides recommendations or suggestions for optimal actions or decisions based on data analysis. Prescriptive analytics uses techniques such as optimization, simulation, and decision analysis to generate and evaluate various scenarios and outcomes. Prescriptive analytics can help financial institutions to use credit scoring to determine the best loan offers, interest rates, and repayment terms for their customers, as well as to manage risk and compliance. Prescriptive analytics is the most advanced and complex type of analytics, as it requires a high level of data quality, integration, and modeling, as well as human judgment and domain expertise. References:

Certification in Business Data Analytics (IIBA ® - CBDA), IIBA, accessed on January 20, 2024.

Business Data Analytics Certification - CBDA Competencies | IIBA®, IIBA, accessed on January 20, 2024.

Guide to Business Data Analytics, IIBA, 2020, p. 15-16.

A/B testing, also known as randomized controlled trial or split testing, is a technique that compares the outcomes of two or more groups that are randomly assigned to different treatments or interventions. The purpose of A/B testing is to measure the causal effect of the treatments on the outcomes of interest, such as pain reduction in this case. A/B testing can help determine whether the new medicine is more effective than the existing drug by comparing the average pain scores of the two groups of participants after the treatment.

References: Guide to Business Data Analytics, page 60-61; CBDA Exam Blueprint, page 7; [Introduction to Business Data Analytics: A Practitioner View], page 18.

The mean of a set of numbers is the sum of the numbers divided by the number of numbers. The median of a set of numbers is the middle value when the numbers are arranged in ascending or descending order. To find the mean and median of the given scores, we can use the following steps:

To find the mean, we add up all the scores and divide by 7, the number of students. The mean is (64 + 70 + 80 + 80 + 90 + 98 + 100) / 7 = 582 / 7 = 83.14

To find the median, we arrange the scores in ascending order: 64, 70, 80, 80, 90, 98, 100. Since there are an odd number of scores, the median is the middle score, which is 80.

Therefore, the mean and median for the outlined scores are 83.14 and 80, respectively.

References: Guide to Business Data Analytics, page 54; CBDA Exam Blueprint, page 7; [Introduction to Business Data Analytics: A Practitioner View], page 16.

According to the IIBA’s Guide to Business Data Analytics, one of the steps in the data analysis process is to interpret and report results, which involves explaining the meaning, significance, and implications of the results in the context of the business problem and the stakeholders’ needs1. When interpreting results, one of the questions the analyst will be asking is what do the results mean in the context of the business, which means how the results relate to the business situation, objectives, and outcomes, and how they can be used to support decision making and action taking2. For example, the analyst may ask how the new government regulation affects the business performance, operations, or strategy, and what recommendations or changes are needed to comply with the regulation and achieve the business goals.

The other options are not correct questions for interpreting results. How will the recipients receive the results is a question for presenting results, not interpreting results. Presenting results is a subsequent step after interpreting results, and it involves choosing the best format, medium, and style to communicate the results to the audience3. Are the right data dimensions being used is a question for analyzing data, not interpreting results. Analyzing data is a prior step before interpreting results, and it involves applying the appropriate techniques, tools, and methods to manipulate, transform, and explore the data4. Is the data accurate based on the sources being used is a question for sourcing data, not interpreting results. Sourcing data is a prior step before analyzing data, and it involves identifying, collecting, and validating the data from the relevant sources5.

References:1: Guide to Business Data Analytics, IIBA, 2020, p. 572: Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 253: Guide to Business Data Analytics, IIBA, 2020, p. 584: Guide to Business Data Analytics, IIBA, 2020, p. 555: Guide to Business Data Analytics, IIBA, 2020, p. 45. : Guide to Business Data Analytics, IIBA, 2020, p. 57. : Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 25. : Guide to Business Data Analytics, IIBA, 2020, p. 58. : Guide to Business Data Analytics, IIBA, 2020, p. 55. : Guide to Business Data Analytics, IIBA, 2020, p. 45.