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Free IIBA CBDA Exam Braindumps (page: 7)

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PDF with 150 Questions

An analytics team has completed some initial data analysis but is considering revising their research question based on their analysis findings. The team was concerned the original question was too broad.
What outcome would lead the team to have this concern?

  1. Data once analyzed had significant data quality issues
  2. Data the team had planned to use was not available
  3. Difficult to identify the KPIs to measure
  4. The source data sets could not be merged

Answer(s): C

Explanation:

A research question is a clear and focused question that guides the data analytics process and defines the expected outcome or value of the analysis1. A research question that is too broad may lead to the concern of being difficult to identify the key performance indicators (KPIs) to measure, as KPIs are specific, quantifiable, and relevant metrics that indicate the progress and success of the analysis in relation to the research question23. A broad research question may also result in too much or too little data, unclear or conflicting objectives, or irrelevant or ambiguous results4.


Reference:

1: Guide to Business Data Analytics, IIBA, 2020, p. 202: Guide to Business Data Analytics, IIBA, 2020, p. 233:

Key Performance Indicators: Developing, Implementing, and Using Winning KPIs, David Parmenter, 2015, p. 34: How to Write a Good Research Question, ThoughtCo, 2021, 1.



A manufacturing company, specializing in turf maintenance equipment, has recently seen a decline in their lawn mower sales. As a result, the analytics team is asked to review the latest customer satisfaction survey results. An analyst on this team creates a report for senior management with attractive visuals, supported by the KPI results. Upon reviewing the report, the analyst's manager mentions that the report is missing the narrative.
What does this mean?

  1. The data tables that support the visuals and help answer questions
  2. A narrative that supports insights with additional context and draws correlations
  3. Notes on assumptions and unavailable data for analysis
  4. Commentary around why each graphic was selected to provide additional context

Answer(s): B

Explanation:

A narrative is a written or spoken explanation of the data analysis results that tells a story with the data, provides additional context and background information, highlights the key insights and findings, and draws correlations and implications for the decision makers12. The report is missing the narrative, meaning that it does not communicate the meaning and value of the data analysis effectively, and it leaves the interpretation and action to the senior management without any guidance or recommendation34.


Reference:

1: Guide to Business Data Analytics, IIBA, 2020, p. 672:
Storytelling with Data, Cole Nussbaumer Knaflic, 2015, p. 93: Data Storytelling: The Essential Data Science Skill Everyone Needs, Brent Dykes, 2016, 14: The Power of Data Storytelling, Harvard Business Review, 2018, 2.



The analytics team scheduled a meeting with key stakeholders to present their recommendations. The team envisioned this as the final step of their work and fully expected complete acceptance of those recommendations, particularly given that very few questions were asked. They were surprised when they received word that the organization wasn't ready to move forward.
What did they overlook?

  1. Stakeholders need to hear the same information multiple times
  2. Stakeholders never make quick decisions
  3. Communicating information requires a written report
  4. Communicating information is bi-directional and iterative

Answer(s): D

Explanation:

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.


Reference:

- 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



While creating a dataset for analysis, the analyst reviews the data collected and finds a large percentage of records are missing values.
Which activity would the analyst perform in order to use this dataset?

  1. Clustering
  2. Scale validation
  3. Weighting
  4. Factor analysis

Answer(s): C

Explanation:

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.


Reference:

- 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



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