Audit software designed to detect invalid data, extreme values, or linear correlations between data elements can be classified as which type of data analytics tool?
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The type of data analytics tool that is designed to detect invalid data, extreme values, or linear correlations between data elements is a diagnostic tool.
Diagnostic analytics involves the use of techniques and tools that examine data to understand the root cause of an issue or problem. It seeks to answer the question, "Why did it happen?" and aims to identify the factors that led to the occurrence of a particular event.
In the context of audit software, diagnostic analytics can help auditors identify anomalies, inconsistencies, or errors in data that may indicate fraud or other irregularities. For example, an auditor may use diagnostic analytics to identify transactions that fall outside of established thresholds or to detect unusual patterns of behavior that may warrant further investigation.
Descriptive analytics, on the other hand, is a type of data analytics that summarizes and describes data to provide insights into past performance. It is concerned with answering the question, "What happened?" and is often used to generate reports and visualizations that provide a snapshot of key performance indicators or other metrics.
Predictive analytics, as the name suggests, uses statistical models and algorithms to make predictions about future outcomes based on historical data. It seeks to answer the question, "What is likely to happen?" and can be used to identify trends, forecast demand, or predict customer behavior, among other things.
Finally, prescriptive analytics involves the use of advanced modeling techniques to recommend actions that will optimize a particular outcome. It seeks to answer the question, "What should we do?" and can be used to optimize supply chain operations, improve customer service, or enhance marketing campaigns, among other things.
In summary, diagnostic analytics is the type of data analytics tool that is used to detect invalid data, extreme values, or linear correlations between data elements. It is used in the context of auditing to identify anomalies and inconsistencies that may indicate fraud or other irregularities.