WGU Updated Data-Driven-Decision-Making Exam Questions and Answers by saskia

A simple index number is 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 the current 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, option A correctly represents the formula for calculating a simple index number.

A strong customer focus leads to increased 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 is C, as customer-focused organizations tend to experience higher revenue and expanded market share.

In regression analysis, the independent variable is 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 the independent variable.

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

An omission error occurs when crucial data is missing from 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 is D, as an omission error occurs when crucial data is missing.