Functions of Audit Productivity Software

In today's environment of increased pressure to perform duties to support regulatory compliance – on top of regular audit responsibilities and fewer skilled resources available to deploy – audit departments are increasingly motivated to find areas where they can optimize productivity and efficiency. Combine this with the fact that often corporate data sets are today measured in the terabytes and access to these crucial transactions is increasingly governed by a myriad of regulations, audit departments need to leverage advanced technology.

Departments that rely on conducting audit analysis with tools such as spreadsheets not only expose themselves to the risk of errors, but also miss out on the opportunity to take advantage of productivity gains offered by purpose-built audit software. Similarly, departments which have not advanced beyond using only client software applications for analyzing data for audit purposes are not able to harness to processing power and resource management afforded by today's server technology. Reliance on IT to provide the necessary data may cause lengthened audit cycles and reduce the quality and depth of the audit investigation.

ACL solutions allow audit teams to work more productively and efficiently by enabling direct immediate access to all of the source data required for audit analysis and by harnessing the power of server technology to rapidly analyze limitless volumes of any type of transactional data.

Direct access to source data means audit cycles are shortened and IT is removed as the "middle man" in accessing data. By moving the heavy lifting of analyzing large volumes of transactions from the auditor’s workstation or laptop to the server environment, not only is the auditor's machine freed up for other tasks, but also the processing power of server technology is fully leveraged. With ACL, audit scripts allow repetitive tasks to be constructed once and deployed as required on a regularly scheduled basis. Furthermore, these processes can run in off-peak hours to further optimize the data analysis process.