WGU Data-Driven-Decision-Making - VPC2Data-Driven Decision MakingC207

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.

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.

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.

Starting an analysis with flawed data significantly undermines the effectiveness of data-driven decision making. One major consequence is thatmore time is spent managing data than analyzing data. Analysts must devote substantial effort to cleaning, validating, and correcting errors before meaningful analysis can occur, delaying insights and increasing costs.

Another critical result is thatmissing data tend to skew the results of the analysis. Incomplete data can distort averages, trends, and statistical relationships, leading to biased conclusions and unreliable decisions. This is especially problematic in predictive and inferential analytics, where assumptions about data completeness are essential.

Using spreadsheets or placing data in charts does not inherently result from flawed data, nor does it resolve data quality issues. While visualization can help identify errors, it is not a direct outcome of starting with flawed data.

Data-driven decision making emphasizes that poor-quality input leads to poor-quality output. Ensuring data accuracy and completeness before analysis is essential for producing valid insights. Therefore, the correct answers areB and D.

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.

Data-driven decision making requires comparing outcomes before and after an intervention to assess effectiveness. In this scenario, the mean patient satisfaction scoredeclined from 4.20 to 1.5following implementation of the campaign, indicating a substantial decrease in satisfaction.

Despite the reduced standard deviation, the large drop in the mean suggests the campaign did not achieve its intended goal and may have negatively affected patient experience. Continuing or modifying the campaign is not justified without evidence of initial improvement or external factors explaining the decline.

Management decisions must be grounded in outcome data rather than assumptions or unrelated metrics such as patient volume. Ethical and effective use of statistics requires discontinuing interventions that demonstrably worsen outcomes.

Therefore, the correct action is todiscontinue the campaign, making optionAthe correct answer.

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.

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.

Akey performance indicator (KPI)measures performance in asingle critical area, such as revenue growth or customer satisfaction. In contrast, abalanced scorecard (BSC)provides amulti-dimensional view of organizational performance, typically across financial, customer, internal process, and learning perspectives.

Data-driven decision making emphasizes that relying on a single metric can lead to incomplete or biased conclusions. The BSC addresses this by integrating multiple KPIs into a cohesive framework aligned with strategic objectives.

Therefore, optionAcorrectly explains the distinction between a KPI and a BSC.

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.