Data-Driven-Decision-Making VPC2Data-Driven Decision MakingC207 Questions and Answers

Prescriptive analyticsis the analytics classification that uses experimental design and optimization techniques to suggest a specific course of action. In data-driven decision making, prescriptive analytics represents the most advanced stage of analytics, as it not only predicts outcomes but also recommends decisions that lead to optimal results.

Descriptive analytics summarizes historical data to explain what has already happened, while predictive analytics uses statistical and probabilistic models to estimate what is likely to happen in the future. Diagnostic analytics focuses on understanding why something happened by identifying root causes. In contrast, prescriptive analytics answers the critical question:what should be done.

Prescriptive analytics relies on methods such as optimization models, simulation, decision trees, and experimental design. These techniques evaluate multiple scenarios, constraints, and objectives to identify the best possible action. For example, organizations use prescriptive analytics to optimize pricing, allocate resources efficiently, schedule operations, or determine optimal investment strategies.

Within data-driven decision-making frameworks, prescriptive analytics bridges analysis and action by directly supporting managerial decision-making. It transforms analytical insights into concrete recommendations that can be implemented to improve performance and outcomes. Therefore, the correct answer isC, as prescriptive analytics explicitly uses experimental design and optimization to suggest a course of action.

A strong customer focus leads toincreased revenue and market share, which is a key principle in data-driven decision making and quality management. Organizations that prioritize customer needs are better positioned to deliver value, improve satisfaction, and build long-term relationships.

By understanding customer preferences, behavior, and feedback through analytics, organizations can tailor products and services more effectively. This alignment increases customer loyalty, repeat business, and positive word-of-mouth, all of which contribute to revenue growth and competitive advantage.

While focusing on customers may also improve efficiency, reduce bias, or lower costs, these outcomes are secondary benefits rather than the primary result. The most direct and measurable impact of customer focus is improved financial performance.

Therefore, the correct answer isC, as customer-focused organizations tend to experience higher revenue and expanded market share.

In an experimental study,treatmentsare defined as the specific procedures or conditions applied to each subject or experimental unit. This is a fundamental concept in experimental design within data-driven decision making and inferential statistics.

Experimental units are the subjects or entities being studied, such as individuals, machines, or products. Responses are the measured outcomes observed after the treatment is applied. Inputs are factors or variables that may influence the experiment but are not the procedures themselves. Treatments, however, represent the deliberate interventions introduced by the researcher to study their effect on the response variable.

For example, in a pricing experiment, different price levels applied to customers would be considered treatments. In a manufacturing experiment, different machine settings would serve as treatments. By systematically varying treatments, analysts can determine causal relationships between variables.

Data-driven decision making relies on well-designed experiments to support valid conclusions. Clearly defining treatments ensures that the effects of specific actions can be isolated, measured, and analyzed accurately. Therefore, the correct answer isC, as treatments describe the procedures applied to each subject.

Thenet promoter score (NPS)measurescustomer loyaltyby assessing the likelihood that customers will recommend a company’s products or services to others. In data-driven decision making, NPS is a widely used indicator of customer satisfaction and long-term growth potential.

Customers are typically asked how likely they are to recommend the organization on a numerical scale. Responses are categorized into promoters, passives, and detractors, and the score is calculated by subtracting the percentage of detractors from the percentage of promoters.

NPS does not directly measure financial outcomes, employee performance, or quality assurance metrics. Instead, it serves as a customer-focused indicator that reflects overall perception and loyalty.

Therefore, the correct answer isB.

Ishikawa’sseven basic tools of qualitywere designed to be simple, visual, and accessible. In data-driven decision making, these tools help employees identify, analyze, and solve quality problems without requiring advanced statistical expertise.

The tools—such as flowcharts, histograms, Pareto charts, and cause-and-effect diagrams—represent processes graphically, making patterns and issues easier to understand. Additionally, they are intentionally designed so thatan average worker can easily understand and use them, supporting organization-wide quality improvement.

They do not rely on photographic representations, nor are they intended for advanced or expert-level training. Instead, they empower frontline employees to participate in continuous improvement efforts.

Therefore, the correct answers areA and C.

In linear regression, the equation y = mx + b is used to predict the value of the dependent variable based on the independent variable. In data-driven decision making, this equation represents the estimated relationship between advertising expenditure and sales revenue.

Here,xrepresents advertising spending,mis the slope (2), andbis the intercept ($25,000). Substituting the planned advertising expenditure of $20,000 into the equation gives:

y = 2(20,000) + 25,000

y = 40,000 + 25,000

y = 65,000

This result represents the expected sales revenue based on the regression model. The intercept indicates baseline sales when advertising spend is zero, while the slope shows the increase in sales for each additional dollar spent on advertising.

Therefore, the company should expect to generate$65,000in sales, making optionCthe correct answer.

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.

In regression analysis, theindependent variableis the predictor used to explain or estimate changes in another variable. In data-driven decision making, identifying the correct variable roles is essential for meaningful interpretation.

In this scenario, land availability is used to predict commute time. Commute time is the outcome being explained, making it the dependent or target variable. Land availability influences or explains changes in commute time, which makes it theindependent variable.

Controls are additional variables included to isolate effects, but land availability is the primary predictor of interest. Therefore, optionCis correct.

Thebalanced scorecardis the most appropriate performance tool for this scenario because it integratesfinancial and nonfinancial performance measuresinto a single framework. In data-driven decision making, the balanced scorecard supports a holistic view of organizational performance.

The firm’s objectives include productivity (internal processes), staff morale (learning and growth), client satisfaction (customer perspective), and long-term financial performance (financial perspective). The balanced scorecard explicitly incorporates all these dimensions, ensuring alignment between strategic goals and operational execution.

Net promoter score focuses only on customer loyalty, results-based management emphasizes outcomes but lacks multi-perspective integration, and KPI dashboards may display metrics but do not inherently provide strategic balance.

Therefore, the correct answer isC, balanced scorecard.

Theincidence rateis a healthcare analytic calculated as the number ofnew casesof a condition divided byperson-time units at risk. In data-driven decision making, this metric is essential for understanding how quickly new cases occur within a population over time.

Person-time accounts for both the number of individuals and the duration they are observed, making the incidence rate particularly useful when populations are dynamic or when observation periods vary. This distinguishes incidence rate from cumulative incidence, which measures new cases over a fixed population and time period without person-time adjustment.

Prevalence measures existing cases at a point in time, and morbidity is a broader term describing illness burden rather than a specific rate calculation.

Because the question explicitly referencesnew cases compared to person-time units, the correct answer isB, incidence rate.

Statistical process control (SPC)focuses on monitoring production processes to ensure they remain within acceptable limits. In data-driven decision making, SPC uses control charts and statistical measures to detect variation and identify whether a process is operating as intended.

For a drill manufacturer, determining whether theweight of selected drills is within a tolerable rangeis a classic SPC activity. Consistent weight indicates stable materials and manufacturing processes, while deviations may signal defects or process drift.

Market segmentation and demand forecasting are strategic analytics tasks, not process control activities. Collaborative planning forecasting and replenishment relates to supply chain coordination rather than manufacturing quality control.

Therefore, the correct answer isB, as SPC is concerned with maintaining process consistency and product quality.

When the null hypothesis is accepted in anF-test, it indicates that there is no statistically significant difference between group variances or means, depending on the test design. Acceptance occurs when thetest statistic is less than the critical value, meaning the observed variation is within expected limits.

Accepting the null hypothesis implies thatno meaningful differenceexists between the samples. If the test statistic exceeded the critical value, the null hypothesis would be rejected.

Thus, the correct results areA and D.

Six Sigmais specifically designed to reduce process variation to3.4 defects per million opportunities, making it the correct tool for achieving this goal. In data-driven decision making, Six Sigma provides a structured methodology (DMAIC) for defining problems, measuring performance, analyzing root causes, improving processes, and controlling future outcomes.

Statistical process control supports monitoring, but Six Sigma integrates SPC with strategic improvement initiatives. Linear programming optimizes resource allocation, and just-in-time focuses on inventory efficiency rather than defect reduction.

Therefore, the correct answer isD, Six Sigma.

In results-based management (RBM),activitiesrepresent the actions undertaken to achieve desired outputs and outcomes. In data-driven decision making, activities include training, assessments, and implementation efforts.

Hiring a third-party company to train staff and assess procedures is an action being performed, not the result of that action. Inputs would include funding or resources, outputs would be trained staff or reports, and outcomes would reflect improved surgical performance.

Therefore, the correct answer isD, activities.

The80/20 rule, or Pareto principle, states that80% of problems result from 20% of causes. In data-driven decision making, this concept helps organizations prioritize efforts where they will have the greatest impact.

Pareto charts visually demonstrate this principle by showing that a small number of factors contribute disproportionately to overall problems. By addressing these critical few causes, organizations can achieve significant improvements with focused effort.

Therefore, the correct answer isD.

Thechi-square statisticis used to compare frequencies of categorical, mutually exclusive outcomes. In data-driven decision making, it is appropriate for analyzing differences between observed counts.

New and used car sales represent mutually exclusive categories, making chi-square the correct choice. Therefore, the correct answer isB.

Asimple index numberis used in data-driven decision making to measure relative change in a variable over time, commonly prices, quantities, or values. The standard formula for a simple price index compares the value in the current period to a base period and expresses the change as a percentage.

The correct formula is thecurrent price divided by the base year’s price, multiplied by 100. This standardization allows analysts and managers to easily interpret changes relative to a reference point. An index value of 100 indicates no change from the base year, values above 100 indicate an increase, and values below 100 indicate a decrease.

This approach is widely used in economic analysis, inflation tracking, and business trend evaluation because it simplifies comparisons across time. Dividing the base year by the current price would reverse interpretation, and omitting multiplication by 100 would fail to express the index in percentage terms.

Therefore, optionAcorrectly represents the formula for calculating a simple index number.

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.