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NEW QUESTION # 86
A consumer goods manufacturer has recently completed an analytics study to understand how to improve its operational excellence. From the top highlights, online sales outperformed other channels in sales growth and there was a direct relationship between positive customer reviews and increased internet sales. Which strategic business decision may be logically derived from these results?
- A. Improve quality of the products
- B. Improve operational efficiencies
- C. Encourage customers to complete online reviews
- D. Create an empowered and collaborative work culture
Answer: C
Explanation:
Explanation
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
NEW QUESTION # 87
An analyst is performing regression analysis and reviewing the results. They would like to rescale the variables in the model to more clearly reflect the relationship between the regression coefficients.Which technique could be used to rescale the variables?
- A. Normalization
- B. Clustering
- C. Mean Centering
- D. Dimension Reduction
Answer: A
Explanation:
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.
NEW QUESTION # 88
A professor at a university has received a few complaints of the exams being too difficult. The professor is looking at exam performance results over the past 5 years to understand the normal tendency and outliers.
Which chart should the professor use?
- A. Scatterplot
- B. Line
- C. Sunburst
- D. Pie chart
Answer: A
Explanation:
Explanation
A scatterplot is a type of chart that shows the relationship between two variables by plotting data points on a two-dimensional plane. A scatterplot can help the professor to understand the normal tendency and outliers of exam performance results over the past 5 years by displaying the distribution, trend, and correlation of the data. For example, the professor can use the x-axis to represent the year and the y-axis to represent the exam score, and see how the scores vary over time and across different exams. Outliers can be identified as data points that are far away from the main cluster or the line of best fit12 References: 1: Scatter Plot - Statistics How To 2: Scatterplots - IIBA BABOK Guide v3
NEW QUESTION # 89
Based on the results of a recently completed analytics initiative, the Human Resource department for a major department store implemented a change to its hiring practice to address the attrition rates of its sales associates. The new policy stated that candidates applying for sales positions must possess at least 3 years of relevant sales experience to be considered. After implementing the change, attrition rates are 10% higher and management is frustrated. Which of the following could result in this outcome?
- A. Analytics is not helpful given this situation
- B. The change proposed is not aligned to company strategy
- C. The results of analysis have been incorrectly interpreted
- D. Sales experience is not a relevant skill
Answer: B
Explanation:
Explanation
The change proposed is not aligned to company strategy, because it may not address the root cause of the attrition problem, or it may conflict with other organizational goals or values. For example, the change may reduce the pool of qualified candidates, increase the hiring costs, or lower the diversity or customer satisfaction of the sales team. The change may also ignore other factors that influence the attrition rates, such as compensation, training, feedback, or recognition. Therefore, the change may not achieve the desired outcome of reducing attrition, and may even worsen it. 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 13
NEW QUESTION # 90
A large number of text messages are received by Twitter each year making Twitter one example of Big Data.
What data characteristic represents this large number of text messages?
- A. Veracity
- B. Variety
- C. Velocity
- D. Value
Answer: C
Explanation:
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.
NEW QUESTION # 91
An analyst calculates the average, median, and mode values for a dataset.What type of analytics is the analyst performing?
- A. Diagnostic
- B. Descriptive
- C. Predictive
- D. Prescriptive
Answer: B
Explanation:
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.
NEW QUESTION # 92
An analytics team is interested in reviewing the results of a public opinion poll that is going to be conducted at the end of the month. One of the factors the team is interested in, is ensuring the result set is statistically significant. Why would this factor be important to the team?
- A. Guarantee that the objectives of the poll are met
- B. Ensure that results are not biased or random
- C. Improve the likelihood of receiving a response rate of 100%
- D. To make sure the criteria for the target audience is met
Answer: B
Explanation:
Explanation
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
NEW QUESTION # 93
A consumer product company has recently seen decline in sales in their athletic wear over the last 3 quarters.
Along with a customer satisfaction survey on their athletic wear products, a study on the competitive market has been initiated. The analyst working has created a dashboard, integrating the results from the market study with customer feedback. On reviewing with the analytics manager, the feedback received was that the visuals were powerful, but the dashboard lacked narrative.What does the manager mean by this?
- A. Insights need to be supported by context and comments to engage the audience
- B. Commentary around why each visual was selected to depict the data will provide context
- C. More commentary needs to be added to add value to the audience
- D. Adding a story example will augment the experience for the audience
Answer: A
Explanation:
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.
NEW QUESTION # 94
A real estate broker is tracking monthly sales between two of its teams. The results have been visualized. What insights can be drawn from the chart?
- A. Q2 was the strongest performing quarter with Team B having the top monthly sales in May
- B. Q3 was the strongest performing quarter with Team A having the top monthly sales in the quarter
- C. Q4 was the lowest performing quarter with Team A having the lowest monthly sales in the Quarter
- D. Q4 was the lowest performing quarter with November having the lowest monthly sales in the year
Answer: D
Explanation:
The chart visualizes monthly sales data for two teams over a year, divided into quarters. By analyzing the data, it is evident that November (part of Q4) had the lowest monthly sales in the year, making option C correct.
There isn't enough information to verify the performance of individual teams in each quarter as per Business Data Analytics (IIBA®- CBDA) objectives and resources. 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
*[CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®], page 8, CBDA Exam Sample Questions and Self-Assessment, Question 7
NEW QUESTION # 95
A lab is conducting a study on protein interactions. They have used the data to create a graph visualization.In graph visualization, what would an edge represent?
- A. A dedicated algorithm that calculates the node positions
- B. A single datapoint
- C. A link between two datapoints
- D. A collection of datapoints and links
Answer: C
Explanation:
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, .
NEW QUESTION # 96
Analytics is being used to estimate the number of machine breakdowns a company will experience next year.
The business analyst provides an optimistic estimate of 10 breakdowns, a pessimistic estimate of 100 breakdowns, and a most likely value of 50 breakdowns.What type of estimation is being used?
- A. Delphi
- B. Parametric Estimation
- C. PERT
- D. Top-down
Answer: C
Explanation:
According to the Guide to Business Data Analytics, PERT (Program Evaluation and Review Technique) is a type of estimation that uses three values: optimistic, pessimistic, and most likely. The PERT estimate is calculated as the weighted average of these three values, with more weight given to the most likely value.
PERT can be used to estimate the duration, cost, or other variables of a project or activity, taking into account the uncertainty and variability of the data. PERT can help provide a realistic and reliable estimate based on the available information.
References: Guide to Business Data Analytics, page 54-55; CBDA Exam Blueprint, page 7; [Introduction to Business Data Analytics: A Practitioner View], page 16.
NEW QUESTION # 97
Collaborative games are used by a business analyst to identify the research questions to be explored within an analytics system.
Participants are asked to write down a research question on a sticky note, put the notes on the wall, and move them towards related research questions. What type of Collaborative game is being played?
- A. People polling
- B. Affinity Map
- C. Product Box
- D. Fishbowl
Answer: B
Explanation:
Explanation
An affinity map is a collaborative game that helps participants to group similar ideas or features together. It is useful for identifying research questions that are related to each other and finding common themes or patterns.
In this game, participants write down their research questions on sticky notes and place them on the wall.
Then, they move the notes around to form clusters of related questions. The clusters can be labeled with a descriptive name or a question that summarizes the theme. An affinity map can help participants to prioritize the most important or relevant research questions and generate insights from the data.
https://businessanalystmentor.com/collaborative-games-business-analysis/
NEW QUESTION # 98
The research question prompting the use of analytics is well-defined. The team obtains the results and determines that the source data did not provide reliable results. As a result of this finding, the team modifies the original question to one that can be answered by the data. What is a risk that could impact the value of this analysis?
- A. The quality of the analysis may be negatively impacted
- B. Increased costs associated with the source data
- C. The objective of the original research may not be met
- D. Timelines will be pushed out making stakeholders unhappy
Answer: C
Explanation:
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
NEW QUESTION # 99
The research study is complete, the data has been analyzed and the team has created the necessary high impact visuals.The business analysis professional urges the team to:
- A. Validate regression analysis
- B. Curate the data
- C. Develop the narrative
- D. Present the results to stakeholders
Answer: C
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.
References:1: Guide to Business Data Analytics, IIBA, 2020, p. 572: Introduction to Business Data Analytics:
An Organizational View, IIBA, 2019, p. 153: Data Storytelling: The Definitive Guide, Tableau, 4: Guide to Business Data Analytics, IIBA, 2020, p. 585: Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 27. : Guide to Business Data Analytics, IIBA, 2020, p. 55. : Data Analysis: The Definitive Guide, Tableau, . : Guide to Business Data Analytics, IIBA, 2020, p. 45. : Data Management: The Definitive Guide, Tableau, . : Data Storytelling: The Definitive Guide, Tableau, . : Guide to Business Data Analytics, IIBA, 2020, p. 57. : Introduction to Business Data Analytics: An Organizational View, IIBA, 2019, p. 15. :
Data Storytelling: The Definitive Guide, Tableau, . : Guide to Business Data Analytics, IIBA, 2020, p. 58. :
Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 27. : Guide to Business Data Analytics, IIBA, 2020, p. 55. : Data Analysis: The Definitive Guide, Tableau, . : Guide to Business Data Analytics, IIBA, 2020, p. 45. : Data Management: The Definitive Guide, Tableau, .
NEW QUESTION # 100
Interested in building out the analytics capability based on the positive results obtained by past analytics efforts, the Chief Marketing Officer (CMO) pitches the idea of using analytics to guide future decision making across the enterprise. Before allocating budget to build up an enterprise analytics practice, the decision makers should:
- A. Identify the sponsor and a project manager who can collaborate on the development of the project charter
- B. Oversee the completion of up-front analysis to determine how value can be achieved through an enterprise-wide analytics practice
- C. Determine if the company has the sufficient resources to build up the analytics practice
- D. Request that a small team be assembled to brainstorm a list of capabilities to develop with any approved monies
Answer: B
Explanation:
Explanation
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
NEW QUESTION # 101
A company wants to gauge the thoughts of their employees towards a new company product. On the 25th of March the interviewer makes a list of all employees who were at work on that day and then chooses a subset of those employees to interview. Which term describes the list of all employees present on March 25th?
- A. Sample weights
- B. Sampling frame
- C. Population of interest
- D. Survey sample
Answer: B
Explanation:
Explanation
The sampling frame is the term that describes the list of all employees present on March 25th, because it is a technique that defines the set of elements from which a sample is drawn. The sampling frame should ideally match the population of interest, which is the group of elements that the researcher wants to study or make inferences about. In this case, the population of interest is the employees of the company, and the sampling frame is the subset of employees who were at work on a specific day. The survey sample is the technique that selects a portion of the sampling frame to participate in the survey. The sample weights are the technique that assigns different values or importance to each element in the sample, based on their representation in the population. 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 14
NEW QUESTION # 102
An analytics team has been asked to answer the following question: "Given that you're a customer, would you work at our company?" The team is concerned about answering this question because it is:
- A. Short
- B. Insignificant
- C. Unethical
- D. Unclear
Answer: D
Explanation:
Explanation
The question "Given that you're a customer, would you work at our company?" is unclear, because it is a hypothetical and subjective question that does not specify the purpose, scope, or context of the analysis. The question also does not define what constitutes a customer, or how the customer's experience or satisfaction relates to the employee's motivation or performance. The question needs to be refined and clarified to make it more focused, relevant, and feasible for the analytics team to answer. For example, the question could be rephrased as "How does the customer satisfaction score affect the employee retention rate in our company?" 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 16
NEW QUESTION # 103
While sourcing data, an analyst runs into a situation where different business units are using different names to refer to the same data element. This lack of standardization is resulting in confusion and additional time required to properly prepare data for analysis. Which practice, if implemented would address this situation and mature the organization's business analytics practice?
- A. Data quality management
- B. Database operations management
- C. Data warehousing
- D. Meta data management
Answer: D
Explanation:
Explanation
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
NEW QUESTION # 104
Results of the data analysis have been analyzed and the team was confident with the results but also quite surprised the outcome was not what was expected. In pondering the value of what can be gleaned from the data, the team has no feasible solution to put forth to address the business need.A logical next step would be to:
- A. Provide the results to a 2nd analytics team to see if similar conclusions are drawn
- B. Check the quality of the data that was used for the analysis
- C. Repeat the business analytics cycle with the formation of a new research question
- D. Analyze the data again, to determine if any insights were overlooked
Answer: C
Explanation:
According to the Guide to Business Data Analytics, the business analytics cycle is an iterative process that consists of four phases: identify the research questions, source data, analyze data, and interpretand report results. The cycle can be repeated as many times as needed until the business problem or opportunity is addressed or resolved. In this situation, the team was confident with the results but also surprised that the outcome was not what was expected. This means that the initial research question may not have been relevant, specific, or testable enough to provide a feasible solution for the business need. Therefore, a logical next step would be to repeat the business analytics cycle with the formation of a new research question that is more aligned with the business goal, scope, and context.
References: Guide to Business Data Analytics, page 47-48; CBDA Exam Blueprint, page 7; [Introduction to Business Data Analytics: A Practitioner View], page 15.
NEW QUESTION # 105
A data scientist at a consumer goods company, has been asked to do a detailed analysis on customer profiles.
The Data Scientist has identified an external data source that carries valuable additional information on their customers. The data scientist also identifies the address column as the most reliable column to join the internal data source with the external data source. Addresses may appear in different formats for example:
File A = "13 Smith St"
File B = "Unit 7, 13 Smith Street"
Which of the following techniques would be useful in this situation?
- A. Genetic linkage
- B. Cuff linkage
- C. Probabilistic linkage
- D. Deterministic linkage
Answer: C
Explanation:
Probabilistic linkage is a technique that uses statistical methods to match records from different data sources based on the similarity of key variables, such as name, address, date of birth, etc1. Probabilistic linkage can handle variations, errors, or missing values in the data, and assign a score or probability to each potential match2. Probabilistic linkage would be useful in this situation, as the address column may have different formats, spellings, or abbreviations in the internal and external data sources, and a deterministic linkage (which requires exact matches) might miss some valid matches or create false matches.
Deterministic linkage is a technique that uses predefined rules or criteria to match records from different data sources based on the exact agreement of key variables, such as identifiers, codes, or hashes3. Deterministic linkage would not be useful in this situation, as the address column may not have consistent or unique values in the internal and external data sources, and a probabilistic linkage (which allows for some variation or uncertainty) might find more accurate matches or avoid false matches.
Genetic linkage is a term used in genetics to describe the tendency of genes or DNA sequences that are located close together on a chromosome to be inherited together4. Genetic linkage is not relevant to this situation, as it has nothing to do with matching records from different data sources based on the address column.
Cuff linkage is a term used in sewing to describe the process of attaching a cuff to a sleeve by stitching or fastening. Cuff linkage is not relevant to this situation, as it has nothing to do with matching records from different data sources based on the address column.
References:1: Guide to Business Data Analytics, IIBA, 2020, p. 452: Data Linkage: The Definitive Guide, Tableau, 3: Guide to Business Data Analytics, IIBA, 2020, p. 454: Genetic Linkage, National Human Genome Research Institute, . : Cuff Linkage, Sewing Dictionary, . : Data Linkage: The Definitive Guide, Tableau, . :
Genetic Linkage, National Human Genome Research Institute, . : Cuff Linkage, Sewing Dictionary, .
NEW QUESTION # 106
A consumer products company gained popularity with increased growth and brand recognition with one of its products. Although they have a loyal customer base and past year's performance results have shown steady growth, the Senior Leadership team wants to keep product leadership as their primary strategic priority.What would be their primary goal?
- A. Ensure that their top product continues to gain market share and maintain high standards
- B. Focus on providing value to customers by offering innovative and leading edge products
- C. Focus on their other products/product lines so that they gain momentum in popularity as well
- D. Maintain operational efficiencies so that their products can continue to be competitively priced
Answer: B
Explanation:
According to the IIBA's Introduction to Business Data Analytics: An Organizational View, product leadership is one of the three generic strategies that an organization can pursue to achieve competitive advantage in its market. Product leadership means that the organization focuses on providing value to customers by offering innovative and leading edge products that are superior in quality, design, functionality, or features than those of the competitors1. Product leadership requires the organization to invest in research and development, to foster a culture of creativity and experimentation, to embrace change and risk, and to leverage data and analytics to generate new ideas, test hypotheses, and measure outcomes2. Therefore, if the Senior Leadership team wants to keep product leadership as their primary strategic priority, their primary goal would be to focus on providing value to customers by offering innovative and leading edge products.
References:1: Introduction to Business Data Analytics: An Organizational View, IIBA, 2019, p. 102:
Introduction to Business Data Analytics: An Organizational View, IIBA, 2019, p. 11.
NEW QUESTION # 107
A business analyst is conducting a series of interviews to understand the research questions that will be explored within a new analytics project. Which of the following is true about interviews?
- A. Interviews must be structured to be effective
- B. Interviews should only be conducted with one interviewee
- C. Goals for the interview should be clearly articulated
- D. Planned interviews are less effective than unplanned
Answer: C
Explanation:
Interviews are a technique to elicit information from stakeholders and subject matter experts. Interviews can be planned or unplanned, structured or unstructured, depending on the context and purpose of the interview.
However, regardless of the type of interview, it is important to have clear goals for the interview, such as what information is needed, what questions will be asked, and how the information will be used. Having clear goals for the interview helps the interviewer to prepare, conduct, and follow up the interview effectively, and also helps the interviewee to understand the expectations and provide relevant and accurate information. References: Guide to Business Data Analytics, page 25; Certification in Business Data Analytics Handbook, page 9; How to Ace Your Next Business Analysis Job Interview
NEW QUESTION # 108
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