Question 671 - CISA discussion

The percent of issues resolved by the first contact, also known as the first contact resolution (FCR) rate, is a metric that measures the effectiveness and efficiency of the IT help desk services. It indicates how many customer support issues are resolved on the first interaction with the IT help desk, without requiring any follow-up calls, emails, chats, or escalations.The FCR rate is calculated by dividing the number of issues resolved on the first contact by the total number of customer support issues, and multiplying by 100%1.

The FCR rate is the best indicator of service quality among the four monthly performance metrics, because it reflects the following aspects of the IT help desk services:

Customer satisfaction: Customers are more likely to be satisfied with the IT help desk services if their issues are resolved quickly and effectively on the first contact, without having to wait for a response or repeat their problem to multiple agents.A high FCR rate can improve customer loyalty, retention, and advocacy2.

Cost efficiency: Resolving issues on the first contact can reduce the operational costs of the IT help desk services, such as labor costs, phone costs, or overhead costs.A high FCR rate can also increase the productivity and utilization of the IT help desk agents, as they can handle more issues in less time3.

Service level: Resolving issues on the first contact can improve the service level of the IT help desk services, such as reducing the average handle time (AHT), increasing the service level agreement (SLA) compliance, or decreasing the backlog of unresolved issues.A high FCR rate can also enhance the reputation and credibility of the IT help desk services4.

Therefore, an IS auditor should review the FCR rate as a key performance indicator (KPI) of the IT help desk services, and compare it with the industry standards and benchmarks. According to MetricNet's benchmarking database, the FCR industry standard is 74 percent.This number varies widely, however, from a low of about 41 percent to a high of 94 percent5. An IS auditor should also recommend ways to improve the FCR rate, such as:

Training and empowering the IT help desk agents to handle a wide range of issues and provide accurate and consistent solutions

Implementing a knowledge base or a self-service portal that provides relevant and updated information and guidance for common or simple issues

Improving communication and collaboration between different departments or teams that may be involved in resolving complex or escalated issues

Using feedback and analytics tools to monitor and measure customer satisfaction and identify areas for improvement

This is because analyzing the data against predefined specifications is a method of data quality assessment that can help the organization achieve a reasonable level of data quality. Data quality assessment is the process of measuring and evaluating the accuracy, completeness, consistency, timeliness, validity, and usability of the data. Predefined specifications are the criteria or standards that define the expected or desired quality of the data.By comparing the actual data with the predefined specifications, the organization can identify and quantify any gaps, errors, or deviations in the data quality, and take corrective actions accordingly12.

Reviewing data against data classification standards (A) is not the best answer, because it is not a method of data quality assessment, but rather a method of data security management. Data classification standards are the rules or guidelines that define the level of sensitivity and confidentiality of the data, and determine the appropriate security and access controls for the data. For example, data can be classified into public, internal, confidential, or restricted categories.Reviewing data against data classification standards can help the organization protect the data from unauthorized or inappropriate use or disclosure, but it does not directly improve the data quality3.

Outsourcing data cleansing to skilled service providers (B) is not the best answer, because it is not a recommendation to help the organization achieve a reasonable level of data quality, but rather a decision to delegate or transfer the responsibility of data quality management to external parties. Data cleansing is the process of detecting and correcting any errors, inconsistencies, or anomalies in the data. Skilled service providers are third-party vendors or contractors that have the expertise and resources to perform data cleansing tasks.Outsourcing data cleansing to skilled service providers may have some benefits, such as cost savings, efficiency, or scalability, but it also has some risks, such as loss of control, dependency, or liability4.

Consolidating data stored across separate databases into a warehouse is not the best answer, because it is not a method of data quality assessment, but rather a method of data integration and storage. Data integration is the process of combining and transforming data from different sources and formats into a unified and consistent view. Data warehouse is a centralized repository that stores integrated and historical data for analytical purposes. Consolidating data stored across separate databases into a warehouse can help the organization improve the availability and accessibility of the data, but it does not necessarily improve the data quality.