Pass the IIBA Business Data Analytics CBDA Questions and answers with CertsForce

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 bubble chart is a type of chart that displays three dimensions of data: the x-axis, the y-axis, and the size of the bubble. A bubble chart can be used to compare and contrast the characteristics of different solution options by plotting their cost, value, and risk level on the three axes. For example, a solution option with a high cost, high value, and low risk would be represented by a large bubble on the upper left corner of the chart, while a solution option with a low cost, low value, and high risk would be represented by a small bubble on the lower right corner of the chart. A bubble chart can help the analyst and the stakeholders to visualize the trade-offs and benefits of each solution option and to select the most optimal one based on the business objectives and constraints. References: Guide to Business Data Analytics, page 77; Introduction to Business Data Analytics: A Practitioner View, page 16; [Business Data Analytics: A Practical Guide], page 121.

Expert judgement and experience are valuable sources of knowledge and insight for business data analytics, but they can also introduce bias in the decision making process. Bias is a tendency to favor or reject a certain perspective, outcome, or solution based on personal or subjective preferences, beliefs, or expectations. Bias can affect the quality, validity, and reliability of the data analysis and the resulting decisions. Some examples of bias that can affect expert judgement and experience are confirmation bias, availability bias, anchoring bias, and overconfidence bias. To avoid or minimize bias, business data analysts should apply critical thinking, data literacy, and ethical principles throughout the data analysis process. They should also seek diverse perspectives, challenge assumptions, validate findings, and communicate uncertainties and limitations.

References:10 Cognitive Biases in Business Analytics and How to Avoid Them; Business Data Analytics: A Decision-Making Paradigm, page 8; Guide to Business Data Analytics, page 11.

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.

Descriptive analytics is the type of analytics that summarizes and visualizes data to provide an overview of what has happened or is happening. Descriptive analytics uses techniques such as statistics, charts, graphs, and dashboards to display data in an understandable and meaningful way. Descriptive analytics can help analysts explore data, identify patterns, and communicate insights. Calculating theaverage, median, and mode values for a dataset is an example of descriptive analytics, as it provides a measure of central tendency for the data distribution. 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.

The 4 Types Of Analytics Explained (With Examples), Analytics for Decisions, accessed on January 20, 2024.

Database normalization is the process of organizing the data in a database to reduce redundancy and improve integrity, consistency, and performance1. Database normalization rules are based on the concept of normal forms, which are levels of database design that meet certain criteria2. One of the most common normal forms is the third normal form (3NF), which states that a table should not have any transitive dependencies, meaning that a non-key attribute should not depend on another non-key attribute3. In the CustomerOrder entity, the set of attributes that will need to be normalized to avoid redundancies are ProductName and ProductQuantity, as they are non-key attributes that depend on another non-key attribute, Orderld. This means that the same product information may be repeated for different orders, which could lead to data inconsistency, duplication, or update anomalies. To normalize this set of attributes, a separate table should be created for the OrderDetails entity, which would have Orderld, ProductName, and ProductQuantity as its attributes, and Orderld and ProductName as its composite primary key.

The other sets of attributes do not need to be normalized to avoid redundancies, as they do not violate the 3NF. CustomerPhone and ProductName are non-key attributes that depend on the primary key, Customerld and Orderld respectively, which is allowed by the 3NF. Orderld and ProductName are part of the composite primary key of the OrderDetails entity, which is also allowed by the 3NF. Customerld and OrderDate are both primary keys of the Customer and Order entities respectively, which are also allowed by the 3NF.

References:1: Guide to Business Data Analytics, IIBA, 2020, p. 442: Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 93: Database Normalization: The Definitive Guide, Tableau, . : Database Normalization: The Definitive Guide, Tableau, . : Guide to Business Data Analytics, IIBA, 2020, p. 44. : Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 9. : Database Normalization: The Definitive Guide, Tableau, . : Database Normalization: The Definitive Guide, Tableau, .

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

Unstructured data is data that does not have a predefined format, structure, or schema, and that cannot be easily stored, processed, or analyzed by traditional databases or tools1. Unstructured data may include text, images, audio, video, or other types of data that are rich in information but complex and diverse in nature2. In the Customerissues entity, the ComplaintNotes attribute would be categorized as unstructured data, as it may contain free-form text that captures the details, sentiments, or emotions of the customers’ complaints, and that may vary in length, language, tone, or style. The ComplaintNotes attribute would require special techniques, such as natural language processing, text mining, or sentiment analysis, to extract meaningful insights from the unstructured data3.

The other attributes in the Customerissues entity would be categorized as structured data, as they have a predefined format, structure, or schema, and that can be easily stored, processed, or analyzed by traditional databases or tools4. Structured data may include numbers, dates, codes, categories, or other types of data that are simple and consistent in nature5. In the Customerissues entity, the CustomerlD, ConcernCategory, ConcernSubCategory, AgentID, and lssueResolved(Y/N) attributes would be categorized as structured data, as they may contain numeric, alphanumeric, or binary values that represent the identifiers, classifications, or statuses of the customers’ issues, and that may have fixed lengths, ranges, or domains.

References:1: Guide to Business Data Analytics, IIBA, 2020, p. 412: Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 83: Data Analysis: The Definitive Guide, Tableau, 4: Guide to Business Data Analytics, IIBA, 2020, p. 415: Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 8. : Data Analysis: The Definitive Guide, Tableau, . : Guide to Business Data Analytics, IIBA, 2020, p. 41. : Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 8.

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.

References: Guide to Business Data Analytics, page 53; CBDA Exam Blueprint, page 7; Data Model Types: An Explanation with Examples

According to the Guide to Business Data Analytics, a cost-benefit analysis is a technique that compares the costs and benefits of a project or decision over a period of time. The net benefit is the difference between the total benefits and the total costs. A positive net benefit indicates that the benefits outweigh the costs. However, a positive net benefit in one year does not necessarily mean that the project or decision is financially viable. The business analysis professional should also consider the cumulative net benefit, which is the sum of the net benefits over the entire time horizon. The cumulative net benefit reflects the overall value of the project or decision, taking into account the time value of money and the opportunity cost of capital. A project or decision is only financially feasible if the cumulative net benefit is positive at the end of the time horizon. Therefore, the business analysis professional should disagree with the team and suggest that they review the cumulative net benefit before endorsing the recommendation.

References: Guide to Business Data Analytics, page 55-56; CBDA Exam Blueprint, page 7; [Introduction to Business Data Analytics: A Practitioner View], page 19.