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

The sample used in this case is biased, meaning that it is not representative of the population of interest. The population of interest is the families in North America, but the sample is drawn from only one source of data: the credit card transactions from a leading North American credit card company. This sample excludes the families who do not use credit cards, or who use other credit card companies, or who use other payment methods. Therefore, the sample is not random or fair, and it may introduce sampling bias into the research results12 References: 1: Sampling Methods | Types, Techniques & Examples 2: Sampling Bias - an overview | ScienceDirect Topics

Before investing in an enterprise analytics practice, the decision makers should have a clear understanding of the expected value and benefits of such a practice. This requires conducting an up-front analysis that identifies the business problems or opportunities that can be addressed by analytics, the data sources and technologies that are needed, the analytical models and methods that are appropriate, and the metrics and indicators that will measure the impact and outcomes of the analytics solutions12. This analysis will help to define the scope, objectives, and requirements of the enterprise analytics practice, as well as the resources, roles, and governance structures that are necessary to support it34. An up-front analysis will also help to prioritize the analytics initiatives based on their feasibility, alignment with the business strategy, and potential value creation

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 trend of winter-related accidents over the past three years, or the percentage of drivers using winter tires12. Diagnostic analytics is a type of analytics that explores and analyzes the data to understand why something has happened or is happening, such as the primary reasons for these accidents, or the factors that influence the drivers’ decisions13. To answer the question, both descriptive and diagnostic analytics would be needed to provide the relevant information and insights for the company. 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. 53: Data Science for Business, Foster Provost and Tom Fawcett, 2013, p. 13.

A qualitative open-ended question is a question that allows the respondent to express their thoughts, feelings, or opinions in their own words, without being constrained by predefined options or categories. A qualitative open-ended question can help the interviewer explore the underlying reasons, motivations, or perceptions of the respondent. Option A is a qualitative open-ended question, because it asks the respondent to explain how call volume affects their job satisfaction and well-being, which may vary from person to person and require elaboration. Options B, C, and D are not qualitative open-ended questions, because they ask the respondent to choose between two alternatives (B and D) or provide a numerical value ©, which are quantitative and closed-ended responses. References:

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

•Understanding the Guide to Business Data Analytics, page 14

•CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 9

The strategic business decision that may be logically derived from the results is to encourage customers to complete online reviews, because the results show that there is a direct relationship between positive customer reviews and increased internet sales. By increasing the number and quality of online reviews, the consumer goods manufacturer can boost its online sales performance, which outperformed other channels in sales growth. Online reviews can also help the manufacturer gain customer feedback, improve customer loyalty, and enhance its brand reputation. 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

•CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 6

The business model canvas is the model that the task force has leveraged to facilitate the discussion, because it is a technique that describes the logic of how an organization creates, delivers, and captures value. The business model canvas consists of nine building blocks that cover the key aspects of a business: customer segments, value proposition, channels, customer relationships, revenue streams, key resources, key activities, key partnerships, and cost structure. The business model canvas can help the task force develop a shared understanding of the current state of the online retailer, and identify the opportunities and challenges for developing their analytics capabilities. References:

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 6: Guide Organization-level Strategy for Business Analytics

•Understanding the Guide to Business Data Analytics, page 9

•10.8 Business Model Canvas | IIBA®

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.

The risk that could impact the value of this analysis is that the objective of the original research may not be met, because the team modified the research question to fit the data, rather than finding the data that fits the research question. This could lead to a loss of alignment between the research question and the business problem, stakeholder needs, or analytical methods. The team may end up answering a different or less relevant question than the one they intended to answer, and thus provide less valuable insights or recommendations. References:

•Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 1: Identify the Research Questions

•Understanding the Guide to Business Data Analytics, page 10-11

•CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 10

Providing ‘one source of the truth’ means ensuring that there is a single, consistent, and authoritative source of data that can be used for analytics and decision making across the organization. This objective can be achieved by enforcing master data management principles and practices, which involve defining, governing, and maintaining the quality and integrity of the core data entities that are shared by multiple systems and processes. Master data management helps to eliminate data silos, reduce data duplication and inconsistency, and improve data accuracy and reliability12 References: 1: What is Master Data Management (MDM)? - Informatica 2: Master Data Management - IIBA BABOK Guide v3