CBDA Practice Exam Questions with Answers Certification in Business Data Analytics (IIBA - CBDA) Certification

One of the steps in identifying the research questions for business data analytics is to assess the feasibility and desirability of the proposed solution or change1. This involves understanding the needs, preferences, and satisfaction of the existing and potential customers. Therefore, asking whether the existing customers really like the product is a relevant research question for the analytics team. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 22.

The median is the type of statistics average that the analyst should use to best represent the results, because it is a measure of central tendency that divides the data set into two equal halves. The median is the middle value of the data set when it is arranged in ascending or descending order. The median is not affected by extreme outliers, unlike the mean, which is the arithmetic average of the data set. The median can give a more accurate representation of the typical score of the 8th grade students across the three schools. Options B, C, and D are not types of statistics average, but types of statistics measures that describe other aspects of the data set. The range is a measure of dispersion that shows the difference between the highest and the lowest values of the data set. The mean is a measure of central tendency that shows the sum of the values of the data set divided by the number of values. The mode is a measure of central tendency that shows the most frequent value of the data set. References:

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3: Analyze Data

•Understanding the Guide to Business Data Analytics, page 17

•Business Data Analytics (IIBA®-CBDA Exam preparation) | Udemy, Section 3: Analyze Data, Lecture 13: Descriptive Statistics

A justifiable concern at this stage of the team’s efforts is changing the mindsets of business stakeholders to trust insights gleaned from data over experience and intuition. This is because some stakeholders may have a strong attachment to their own opinions or beliefs, and may resist or ignore data that contradicts them. This can create a barrier to data-driven decision making, which requires a culture of curiosity, openness, and evidence-based reasoning. The team needs to communicate the value and validity of their data analysis, and persuade the stakeholders to adopt a data-driven mindset12 References: 1: Use Data to Accelerate Your Business Strategy 2: Data-Driven Decision Making: A Step-by-Step Guide

The ability to rerun the data analysis and the results are the same is the condition that the team should have before presenting the data, because it is a technique that ensures the validity, reliability, and reproducibility of the data analysis. By rerunning the data analysis, the team can verify that the results are consistent and not affected by random errors, biases, or anomalies. The team can also confirm that the data analysis process is well-documented, transparent, and traceable, and that the results can be replicated by other analysts or stakeholders. This can minimize the risk of acting on the data, and increase the confidence and trust in the data analysis. References:

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 4: Interpret and Report Results

•Understanding the Guide to Business Data Analytics, page 9

•Business Data Analytics (IIBA®-CBDA Exam preparation) | Udemy, Section 4: Interpretand Report Results, Lecture 20: Data Validation and Verification

A box plot is the technique that would allow for the display of the statistical five number summary, because it is a technique that shows the distribution of a data set using a rectangular box and whiskers. A box plot can help the data scientist visualize the minimum, maximum, median, first quartile, and third quartile of the data set, as well as any outliers or skewness. A box plot can also help the data scientist compare the variation and symmetry of different groups or categories of data. Options A, B, and C are not suitable for displaying the statistical five number summary, because they are techniques that show the frequency, relationship, or density of the data, but not the quartiles or outliers. References:

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3: Analyze Data

•Understanding the Guide to Business Data Analytics, page 18

•16 Best Types of Charts and Graphs for Data Visualization [+ Guide]

The mean is the average of all the scores, which is found by adding them up and dividing by the number of scores. The mode is the most frequent score, which is the one that occurs the most times. To find the mean and mode for the outlined scores, we can use the following steps:

•Arrange the scores in ascending order: 64, 70, 80, 80, 90, 98, 100

•Add up the scores: 64 + 70 + 80 + 80 + 90 + 98 + 100 = 582

•Divide the sum by the number of scores: 582 / 7 = 83.14

•The mean is 83.14

•Count how many times each score occurs: 64 occurs once, 70 occurs once, 80 occurs twice, 90 occurs once, 98 occurs once, 100 occurs once

•The score that occurs the most times is 80

•The mode is 80

Therefore, the mean and mode for the outlined scores are 83.14 and 80, respectively12 References: 1: Mean, median, and mode review (article) | Khan Academy 2: Mean, Median, and Mode: Measures of Central Tendency - Statistics By Jim

Weighting is a technique that assigns different values or weights to different records or variables in a dataset, based on their importance or relevance. Weighting can be used to handle missing values by giving them a lower weight or imputing them with a weighted average of other values. Weighting can also help to adjust for sampling bias or non-response bias in the data collection process. References:

•Understanding the Guide to Business Data Analytics, page 16

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3: Analyze Data

•CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 4

The analytics team overlooked the fact that communicating information is not a one-way or one-time process, but rather a bi-directional and iterative one. This means that the team should not only present their recommendations, but also solicit feedback, address concerns, clarify doubts, and confirm understanding from the stakeholders. By doing so, the team can ensure that the stakeholders are fully engaged, informed, and aligned with the recommendations, and that any potential barriers or risks are identified and mitigated before moving forward. References:

•Understanding the Guide to Business Data Analytics, page 9

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 4: Interpret and Report Results

•CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 5, Step 3 – Schedule and Take The Exam

Meta data management is the practice that, if implemented, would address the situation and mature the organization’s business analytics practice, because it is a technique that involves defining, documenting, and maintaining the information about the data elements, such as their names, definitions, formats, sources, and relationships. Meta data management can help the analyst resolve the inconsistencies and ambiguities in the data element names, and ensure that the data is standardized, consistent, and understandable across different business units. Meta data management can also help the analyst improve the data quality, accessibility, and usability for the analysis. References:

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 2: Source Data

•Guide to Business Data Analytics - Iiba - Google Books, page 14

•Business Data Analytics (IIBA®-CBDA Exam preparation) | Udemy, Section 2: Source Data, Lecture 8: Meta Data Management

Ensuring the result set is statistically significant is important to the team because it means that the difference or relationship observed in the data is unlikely to be due to chance or sampling error. Statistical significance helps the team to assess the validity and reliability of their findings, and to draw meaningful conclusions and recommendations from the data. Statistical significance also helps the team to communicate their results with confidence and credibility to the stakeholders and decision makers12 References: 1: An Easy Introduction to Statistical Significance (With Examples) - Scribbr 2: Statistical Significance in Experimentation and Data Analysis - All About Circuits

The best option for the team under these conditions is to identify and carefully document theassumptions for their recommendation, such as the expected impact of the regulatory changes, the risks and benefits of implementing the changes before or after the announcements, and the sensitivity of the results to different scenarios. This way, the team can communicate their findings and recommendations clearly and transparently, while also acknowledging the uncertainty and limitations of their analysis. This can help the decision makers to evaluate the trade-offs and make informed choices12. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 242: Data-Driven Decision Making: A Primer for Beginners, Anand Rao, 2018, 1.

According to the Business Data Analytics (IIBA®- CBDA) principles, extreme outliers in a dataset can be identified visually on a scatter plot as points that are distinctly separate from the bulk of the data. In this visual, there are three points that are significantly higher on the y-axis (credit card expense) relative to their position on the x-axis (household income), indicating unusual credit card purchases. References: The identification and interpretation of outliers is a standard practice in data analytics and is covered under the Business Data Analytics (IIBA®- CBDA) learning resources.

According to the Business Data Analytics: A Decision-Making Paradigm1, one of the key steps in the analytics process is to communicate insights and recommendations to stakeholders. The analyst should present the findings in a clear and concise manner, and provide actionable suggestions to improve the business outcomes. In this case, the analyst has identified that repeat visitors are not completing a sale, which indicates a possible issue with the sales checkout process. Therefore, the analyst should recommend the retailer to streamline the sales checkout process, which could reduce friction, increase customer satisfaction, and boost sales conversion rates for repeat visitors. References: Business Data Analytics: A Decision-Making Paradigm

Descriptive analytics is a type of analytics that summarizes and visualizes the data to provide an overview of what has happened or is happening, such as the attrition rates and the personality test scores of the employees12. The team has been asked to perform descriptive analytics to explore possible relationships between the data variables, without making any predictions or prescriptions for the future. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 182: Business Analytics: Data Analysis & Decision Making, S. Christian Albright and Wayne L. Winston, 2015, p. 5.

Acceptance and evaluation criteria are the techniques that the BA professional on the team should recommend they complete, because they are the standards or measures that are used to evaluate the suitability and value of each option. Acceptance and evaluation criteria can help the team compare the benefits, costs, risks, and impacts of each option, and determine which one best meets the needs and expectations of the stakeholders. Acceptance and evaluation criteria can also help the team communicate the rationale and evidence behind their recommendation, and ensure that the recommendation is aligned with the business goals and objectives. References:

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 5: Use Results to Influence Business Decision Making

•Understanding the Guide to Business Data Analytics, page 9

•Acceptance and Evaluation Criteria | Business Analysis

The data scientist is encouraging using the Gestalt Principle of Similarity with regards to data visualization. This principle states that the brain groups objects together that are similar in appearance, such as color, shape, size, or orientation. By using color to group objects needing to be associated, the data scientist is suggesting a way to reduce the complexity in the model and make it easier for the viewers to perceive the patterns and relationships among the data12 References: 1: Gestalt Principles For Data Visualization - Topcoder 2: Introduction to Data Visualization: Gestalt Principles

A Data Flow Diagram (DFD) is a technique that shows the flow of data among processes, data stores, and external entities in a system. DFDs can be categorized as logical or physical, depending on the level of detail and abstraction. A logical DFD focuses on the business functions and data flows, without specifying the implementation details. A physical DFD shows the actual componentsand mechanisms that are involved in the data flow, such as hardware, software, files, and network connections. References:

•10.13 Data Flow Diagrams | IIBA® - International Institute of Business …, menu, 10.13 Data Flow Diagrams

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 2: Source Data

•Introduction to Business Data Analytics: Organizational View, page 16, Figure 6: Data Flow Diagram

Agreeing on the business problem is the first and most critical step in any analytics project, as it defines the scope, purpose, and objectives of the analysis, and aligns the expectations and interests of the stakeholders1. Agreeing on the business problem involves identifying the problem statement, the problem owner, the problem context, the problem impact, and the problem criteria2. If the team did not agree on the business problem, the solution proposed by the team may seem misaligned with the actual needs, preferences, or assumptions of the decision makers, and may not address the root cause or the main drivers of the problem. In this scenario, the team and the Director of Sales may have different views on what the business problem is, why it is important, and how it should be solved.

The other options are not correct explanations of what has gone wrong. This scenario can be addressed with analytics, as it involves using data to understand customer behavior, identify factors influencing cart abandonment, and recommend improvements to the website or the pricing strategy. The team may or may not have agreed on the root cause of the problem, but that is not the main issue, as the root cause analysis is a part of the data analysis step, not the problem definition step. The team may or may not have performed an insufficient amount of planning, but that is not the main issue, as the planning process is a subsequent step after the problem definition step, and it depends on the clarity and agreement of the business problem.

References:1: Guide to Business Data Analytics, IIBA, 2020, p. 252: Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 11. : Guide to Business Data Analytics, IIBA, 2020, p. 25. : Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 11.

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.

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.

Normalization is a technique that rescales the values of the variables in a data set to a common range, such as [0,1] or [-1,1]. Normalization can help reduce the effect of outliers, improve the performance of some algorithms, and make the interpretation of the regression coefficients easier and more consistent. Normalization can be done using different methods, such as min-max scaling, z-score scaling, or unit vector scaling.

References:Guide to Business Data Analytics, page 41; Introduction to Business Data Analytics: A Practitioner View, page 12.

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.

According to the Guide to Business Data Analytics, descriptive analytics is a type of analytics that summarizes and presents data in a meaningful way. Descriptive analytics uses techniques such as statistics, charts, tables, and dashboards to provide an overview of what has happened or is happening in the data. Descriptive analytics can help answer questions such as who, what, when, where, and how. In this situation, the analyst has been asked to look through past sales data to help guide product decisions. The business sponsor for this initiative would first like to know ‘What is the most profitable product line?’. This is a descriptive analytics question, as it involves summarizing and presenting the past sales data by product line and calculating the profit margin for each product line.

References: Guide to Business Data Analytics, page 49; CBDA Exam Blueprint, page 7; [Introduction to Business Data Analytics: A Practitioner View], page 14.

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 p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true. A p-value is used to make conclusions in hypothesis testing by comparing it to a significance level, which is the maximum probability of making a type I error (rejecting the null hypothesis when it is true). If the p-value is less than or equal to the significance level, then there is strong evidence against the null hypothesis and it is rejected in favor of the alternative hypothesis. If the p-value is greater than the significance level, then there is weak evidence against the null hypothesis and it is not rejected. In this situation, the team realizes that the p-value of 0.02 is lower than the initial target, which means that the probability of observing such a result under the null hypothesis is very low. This clearly indicates that the team can reject the null hypothesis in favor of the alternative hypothesis, as there is sufficient evidence to support their hypothesis.

References: Guide to Business Data Analytics, page 57-58; CBDA Exam Blueprint, page 7; Understanding P-values | Definition and Examples - Scribbr

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.

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.

Linear programming is a business analytics technique that can lend itself to supporting these types of business decisions. Linear programming is a mathematical method that optimizes the allocation of limited resources to achieve a desired objective, subject to a set of constraints1. Linear programming can help the operations manager to determine the optimum number of vans to purchase, the most efficient routes and schedule to follow, and the minimum cost or time to shuttle guests to/from the airport, by formulating a linear objective function and a system of linear inequalities that represent the relevant variables, parameters, and restrictions2.

The other options are not correct business analytics techniques for these types of business decisions. Factor analysis is a statistical method that reduces the dimensionality of a large set of correlated variables into a smaller set of uncorrelated factors that explain the underlying structure or patterns of the data3. Factor analysis can help the operations manager to identify the key factors that influence the guest satisfaction or loyalty, but it cannot help to optimize the resource allocation or efficiency. Regression is a statistical method that estimates the relationship between one or more independent variables and a dependent variable. Regression can help the operations manager to predict the demand or revenue of the hotel based on the variables such as season, price, or location, but it cannot help to optimize the resource allocation or efficiency. K-means clustering is a machine learning method that partitions a set of data points into a predefined number of clusters based on the similarity or distance between the data points. K-means clustering can help the operations manager to segment the guests into different groups based on their characteristics or preferences, but it cannot help to optimize the resource allocation or efficiency.

References:1: Guide to Business Data Analytics, IIBA, 2020, p. 532: Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 93: Guide to Business Data Analytics, IIBA, 2020, p. 54. : Guide to Business Data Analytics, IIBA, 2020, p. 54. : Guide to Business Data Analytics, IIBA, 2020, p. 55. : Guide to Business Data Analytics, IIBA, 2020, p. 53. : Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 9. : Guide to Business Data Analytics, IIBA, 2020, p. 54. : Guide to Business Data Analytics, IIBA, 2020, p. 54. : Guide to Business Data Analytics, IIBA, 2020, p. 55.

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.

References: Introduction to Business Data Analytics: A Practitioner View, page 17; CBDA Exam Blueprint, page 7; [Churn Rate Definition - Investopedia]

Data sampling is the process of selecting a subset of data from a larger population to represent the characteristics of the whole population. Data sampling is often used when the population is too large or costly to collect data from every individual. Data sampling can help reduce the time, cost, and complexity of data analysis, while maintaining the validity and reliability of the results. Data sampling can also help avoid biases and errors that may arise from collecting data from the entire population. Data sampling can be done using various methods, such as random sampling, stratified sampling,cluster sampling, or convenience sampling, depending on the research objectives and the availability of data. In this example, the analytics team develops a survey to send to randomly selected customers, which is a form of data sampling. The survey aims to collect data from a representative sample of customers that can reflect the preferences and opinions of the entire customer population. The survey data can then be used to analyze the performance and demand of different products, and provide recommendations to management. References:

• [Business Data Analytics: A Practitioner’s Guide], Chapter 4: Data Analysis, Section 4.2: Data Sampling, pp. 69-72.
• [A Guide to the Business Analysis Body of Knowledge® (BABOK® Guide)], Version 3, Chapter 6: Solution Evaluation, Section 6.2: Analyze Performance Measures, pp. 152-153.

A data strategy is a long-term plan that defines how the enterprise will identify, store, manage, share, and use its data to achieve its business goals and objectives1. A data strategy aligns the data vision, mission, principles, and policies with the business strategy, and guides the data governance, data quality, data architecture, data security, data integration, data analytics, and data culture of the organization2. A data strategy helps the organization to leverage its data as a strategic asset, to create value, to improve performance, and to gain competitive advantage3.

A data roadmap is a document that outlines the specific actions, milestones, deliverables, and timelines for implementing the data strategy. A data roadmap is a tactical tool that helps the organization to prioritize, coordinate, and communicate its data initiatives, and to track its progress and outcomes. A data roadmap is not a long-term plan, but a dynamic and flexible plan that can be updated and revised as the data strategy evolves.

A business strategy is a high-level plan that defines how the enterprise will achieve its vision, mission, and goals in a competitive market. A business strategy sets the direction, scope, and value proposition of the organization, and guides its decisions on resource allocation, product development, customer segmentation, pricing, marketing, and differentiation. A business strategy is not a plan that defines how the enterprise will identify, store, manage, share, and use its data, but a plan that defines how the enterprise will create and sustain value for its stakeholders.

A data management plan is a document that describes the data that will be collected, generated, or used in a specific project, and how the data will be handled, stored, preserved, shared, and reused during and after the project. A data management plan is a operational tool that helps the project team to comply with the data policies, standards, and best practices of the organization, and to ensure the quality, integrity, security, and accessibility of the data. A data management plan is not a long-term plan, but a project-specific plan that can be modified and updated as the project progresses.

References:1: Guide to Business Data Analytics, IIBA, 2020, p. 392: Introduction to Business Data Analytics: An Organizational View, IIBA, 2019, p. 143: Data Strategy: The Definitive Guide, Tableau, . : Data Strategy: The Definitive Guide, Tableau, . : Data Roadmap: The Definitive Guide, Tableau, . : Business Strategy: The Definitive Guide, Tableau, . : Business Strategy: The Definitive Guide, Tableau, . : Data Management Plan: The Definitive Guide, Tableau, . : Data Management Plan: The Definitive Guide, Tableau, . : Data Strategy: The Definitive Guide, Tableau, . : Guide to Business Data Analytics, IIBA, 2020, p. 39. : Introduction to Business Data Analytics: An Organizational View, IIBA, 2019, p. 14. : Data Strategy: The Definitive Guide, Tableau, . : Data Roadmap: The Definitive Guide, Tableau, . : Business Strategy: The Definitive Guide, Tableau, . : Data Management Plan: The Definitive Guide, Tableau, .

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.

Linear bias is a type of cognitive bias that assumes a linear relationship between two variables, when in fact the relationship may be more complex or nonlinear. In this case, the analytics team assumed that the higher the discount, the higher the sales and profit, without considering other factors that may affect customer behavior, such as price elasticity, perceived quality, or competition. By changing their recommendation, the team can avoid making a suboptimal decision that may result in lower profit or customer satisfaction.

References:10 Cognitive Biases in Business Analytics and How to Avoid Them, page 5; [Business Data Analytics: A Decision-Making Paradigm], page 9.

According to the Guide to Business Data Analytics, a heatmap is a type of visualization technique that uses colours to represent the values of a variable across a two-dimensional space. A heatmap can help reveal patterns, trends, and outliers in the data, as well as show the relative importance or intensity of different areas. In this situation, the analyst has decided to communicate the information about the website usage by displaying the top pages overlaid with colours denoting the volume of clicks. This is a heatmap, as it uses colours to show the distribution and magnitude of clicks across the web pages.

References:Guide to Business Data Analytics, page 61;CBDA Exam Blueprint, page 7;Heat Maps | Trendz Analytics

Velocity is one of the four V’s of Big Data, along with Volume, Variety, and Veracity. Velocity refers to the speed at which data is generated, collected, and processed. A large number of text messages received by Twitter each year is an example of high-velocity data, as it requires real-time or near-real-time processing and analysis to extract insights and value from it. High-velocity data poses challenges and opportunities for business data analytics, as it requires efficient and scalable data infrastructure, streaming analytics, and timely decision-making.

References:1, page 9; 2, page 6.

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 research question is a clear, concise, and specific question that guides the analytics work1. The research question should be SMART: Specific, Measurable, Achievable, Relevant, and Time-bound2. The question ‘How can shipping costs be lowered in the northeast to remain competitive?’ is not SMART, as it lacks some important details, such as:

• What is the current shipping cost and the target cost reduction?
• What are the criteria for measuring competitiveness?
• What is the time frame for achieving the cost reduction and competitiveness?
• What are the factors that influence the shipping cost and competitiveness?
• What are the assumptions and constraints of the analysis? Therefore, the question needs to be further defined before mathematical modeling can begin, as the modeling requires clear and precise inputs and outputs. The other options are not correct, as they imply that the question is either too vague or too narrow, which is not the case.

References:1: Guide to Business Data Analytics, IIBA, 2020, p. 312: Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 11.

A graph visualization is a type of visualization that shows the relationships among data points by using nodes (or vertices) to represent the data points and edges (or links) to represent the connections between them1. A graph visualization can help reveal patterns, clusters, outliers, or hierarchies in the data2. In a graph visualization, an edge represents a link between two data points, indicating that they have some kind of association, interaction, similarity, or dependency3. For example, in a study on protein interactions, an edge could represent a physical or functional interaction between two proteins, such as binding, signaling, or regulation4.

A single data point, a collection of data points and links, and a dedicated algorithm that calculates the node positions are not correct definitions of an edge in a graph visualization. A single data point is represented by a node, not an edge, in a graph visualization. A collection of data points and links is the whole graph, not an edge, in a graph visualization. A dedicated algorithm that calculates the node positions is a method of graph layout, not an edge, in a graph visualization. A graph layout is the way the nodes and edges are arranged in a graph visualization, which can affect the readability, aesthetics, and interpretation of the graph.

References:1: Guide to Business Data Analytics, IIBA, 2020, p. 692: Data Visualization: The Definitive Guide, Tableau, 3: Graph Visualization: The Definitive Guide, Tableau, 4: Protein Interaction Networks, Nature, . : Graph Visualization: The Definitive Guide, Tableau, . : Guide to Business Data Analytics, IIBA, 2020, p. 69. : Data Visualization: The Definitive Guide, Tableau, . : Graph Visualization: The Definitive Guide, Tableau, . : Protein Interaction Networks, Nature, . : Graph Visualization: The Definitive Guide, Tableau, .

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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