Data-Driven-Decision-Making Questions and Answers

Using both mean and median helps identifyoutliers, such as very high executive salaries that skew the average. A large difference between the two indicates uneven distribution.

Thus, the correct answer isC.

Anomission erroroccurs whencrucial data is missingfrom a dataset, which can significantly compromise the quality of analysis and decision-making. In data-driven decision making, omission errors are a serious concern because missing information can lead to biased results, incorrect interpretations, and flawed conclusions.

Omission errors may arise during data collection, data entry, or data integration processes. For example, failing to record customer demographics, transaction values, or time periods can distort descriptive statistics and weaken predictive models. Unlike inaccuracies, which involve incorrect values, omission errors involve the absence of necessary data altogether.

Outliers represent extreme values and are not omission errors. Similarly, failing to review all data is a process issue rather than a data-quality error definition. Inaccurate data refers to incorrect or erroneous values, not missing ones.

Effective data quality management emphasizes identifying and correcting omission errors through validation rules, completeness checks, and data audits. In data-driven decision making, ensuring that all relevant data is captured is essential for producing reliable insights and supporting sound business decisions. Therefore, the correct answer isD, as an omission error occurs when crucial data is missing.

ASIPOC diagram(Suppliers, Inputs, Process, Outputs, Customers) is specifically designed to closely monitor and understand the flow of inputs and outputs within a process. In data-driven decision making and quality management, SIPOC diagrams provide a high-level view of how value is created and delivered.

By clearly identifying suppliers and inputs at the start of a process and outputs and customers at the end, organizations can assess whether inputs meet requirements and whether outputs align with customer expectations. This visibility helps identify inefficiencies, gaps, or quality issues early in the process lifecycle.

Business process diagrams focus on workflow steps but do not emphasize supplier–input and output–customer relationships. Financial statements and pro forma statements are financial planning tools and are not designed for operational process monitoring.

Therefore, the correct answer isC, SIPOC diagram.

Thecause-and-effect diagram, also known as thefishbone diagramorIshikawa diagram, is used to identify potential causes of a problem. In data-driven decision making, it helps teams systematically explore root causes rather than focusing on symptoms.

The diagram visually organizes possible causes into categories such as people, processes, materials, machines, and environment. Histograms show distributions, flowcharts depict process steps, and scatter diagrams show relationships between variables.

Therefore, the correct answer isC, cause-and-effect diagram.

Analytics help organizations primarily by enabling the development offact-based strategies, which is a central principle of data-driven decision making. Rather than relying on intuition, assumptions, or anecdotal evidence, analytics allows organizations to systematically analyze data to understand performance, identify opportunities, manage risks, and support strategic decisions.

Through descriptive analytics, organizations gain insight into historical performance andoperational efficiency. Predictive analytics enables them to anticipate future trends, customer behavior, and potential outcomes. Prescriptive analytics further supports decision-making by recommending optimal actions under various constraints. Together, these approaches transform raw data into actionable insights that guide strategic planning and execution.

While analytics may support investment management, marketing, or information systems usage, these arespecific applications, not the fundamental organizational benefit. Analytics is not primarily used to persuade consumers, nor is its main objective to increase system usage among employees. Instead, its value lies in improving decision quality by grounding strategies in empirical evidence.

In data-driven decision-making frameworks, analytics serves as a structured approach to aligning data, models, and business objectives. By developing strategies based on verified data and analytical methods, organizations reduce uncertainty, improve performance, and gain competitive advantage. Therefore, the correct answer isC, as analytics enable organizations to developfact-based strategies.

Cluster analysisis an unsupervised learning technique used to group observations based on similarity. In data-driven decision making, it is commonly used formarket segmentation, allowing firms to identify distinct customer groups with similar preferences or behaviors.

For a new granola bar, cluster analysis helps determine which consumer segments exist, such as health-conscious buyers, convenience-focused consumers, or price-sensitive shoppers. This enables targeted marketing strategies and product positioning.

Understanding reasons for purchase requires survey or causal analysis, not clustering. Competitor benchmarking and trend analysis involve different analytical techniques.

Therefore, the correct answer isB, determining different segments or groups to target.

In anormal distribution, data is symmetrically distributed around the center, causing the mean and median to be approximately equal. This is a fundamental property emphasized in data-driven decision making.

Skewed distributions, such as Pareto, cause the mean and median to differ significantly. Therefore, the correct answer isA.

Patient-to-staff ratiosare a critical analytic measure for determining whether a hospital is adequately staffed for each shift. In data-driven decision making, staffing adequacy is best assessed by examining workload demand relative to available personnel.

Patient-to-staff ratios directly reflect how many patients each staff member is responsible for during a given shift. High ratios may indicate understaffing, increased risk of burnout, and reduced quality of care, while lower ratios suggest more manageable workloads and better patient outcomes.

Staff productivity levels measure efficiency but do not directly capture demand. Education levels reflect qualifications rather than staffing sufficiency. Patient satisfaction is an outcome metric and may be influenced by many factors beyond staffing levels.

By analyzing patient-to-staff ratios across shifts, hospital administrators can identify imbalances, allocate resources more effectively, and improve operational efficiency. Therefore, the correct answer isB.

Quality controlinvolves monitoring, measuring, and analyzing production output to identify defects and variations. In data-driven decision making, quality control focuses on detecting problems after or during production rather than preventing them in advance.

In this scenario, the manager reviews defect data and identifies a pattern indicating more errors during the night shift. This analysis is a classic quality control activity, as it involves examining performance data to detect issues in the production process.

Quality assurance focuses on process design and prevention, while service and underlying activities are not standard quality management classifications. Therefore, the correct answer isD, quality control activity.

This scenario requires the use of **conditional probability**, which applies when the likelihood of one event depends on the occurrence of another event. In data-driven decision making, conditional probability is used to model dependent events within processes, workflows, and project timelines.

In this case, the enterprise analytics team’s ability to move into production is **dependent on** the infrastructure team completing the migration. Because one event cannot occur unless another event has already occurred, the probability of completing the project on time must account for this dependency.

The multiplication principle applies to independent events, Bayes’ theorem updates probabilities based on new information, and combinations are used for counting outcomes, not dependency analysis. Conditional probability explicitly captures the relationship between dependent tasks.

Project risk analysis and scheduling often rely on conditional probability to assess completion likelihood when tasks are sequentially linked. Therefore, the correct answer is **C**, conditional probability.

Seasonalityrefers to predictable patterns in data that repeat at regular intervals, such as quarters or months, due to seasonal factors. In data-driven decision making, identifying seasonal patterns helps organizations forecast demand and plan operations.

The boutique’s revenue shows distinct quarterly patterns: declines and increases that align with different times of the year. The sharp increase in the fourth quarter is especially indicative of seasonal effects, such as holiday shopping.

Random variation and irregularity describe unpredictable fluctuations, while cyclicality refers to long-term economic cycles rather than recurring annual patterns. Therefore, the correct answer isC, seasonality.

A 95% confidence result indicates a statistically significant difference between groups. Since the measured outcome isbrand awareness, the correct conclusion is that the advertisement was effective in increasing brand awareness.

Confidence levels do not measure sales, preference, or dislike. Therefore, the correct answer isB.

To determine the probability of achieving a specific monetary threshold, data-driven decision making relies onstandardization using the z-score. A z-score measures how many standard deviations a value is from the mean and allows analysts to calculate probabilities using the normal distribution.

In this scenario, the nonprofit wants to assess the likelihood that total donations will reach at least $50,000 given a known mean and standard deviation. The z-score enables conversion of the donation target into a standardized value, which can then be evaluated using probability tables or statistical software.

R-squared measures model fit in regression, the t-statistic is used in hypothesis testing, and the median does not support probability calculations. Therefore, the appropriate measure for determining probability in this context is thez-score, making optionDcorrect.

Big datamust be analyzed using powerful analytic tools due to its volume, velocity, and variety. In data-driven decision making, traditional tools are often insufficient for processing massive, complex datasets generated from digital platforms, sensors, and transactions.

Big data requires advanced computing power, specialized software, and sophisticated algorithms to extract meaningful insights. Inferential statistics and small datasets can often be handled with conventional statistical tools.

Therefore, the correct answer isC, big data.

When predicting a continuous outcome based on a single predictor, data-driven decision making recommendssimple linear regression. In this case, sales figures are continuous, and advertising spend is a single explanatory variable.

Linear regression models the relationship between one independent variable and one dependent variable by estimating a straight-line relationship. Time series regression is used when data are indexed over time, logistic regression is used for binary outcomes, and multiple linear regression requires multiple predictors.

Because the company is using only advertising spend to predict sales,linear regressionis the most appropriate method. Therefore, the correct answer isB.

Az-scorestandardizes a value by expressing how many standard deviations it lies from the mean. A fundamental assumption of z-score analysis in data-driven decision making is that the data can be transformed to astandard normal distributionwith amean of zero and a standard deviation of one.

This transformation allows analysts to compare values from different distributions on a common scale and to calculate probabilities using the standard normal table. The formula for a z-score subtracts the mean from the observed value and divides by the standard deviation, resulting in this standardized distribution.

Outliers are not eliminated by default in z-score calculations; instead, z-scores are often used to identify outliers. A standard deviation of 2 is incorrect and would not represent a standardized distribution.

Therefore, the correct answer isA, reflecting the core assumption underlying z-score usage.