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AICPA Introduces New Disruptive Technology

Audit data analyticsWith the issuance of the AICPA’s Audit Data Standards, the Institute has introduced what promises to be a significant disruptive technology – data on demand. ADS were designed to bridge the gap between disparate Enterprise Resource Planning systems and apps (tools and technologies) that analyze a company’s data. Until now business information consumers had to rely on IT personnel to either develop customized extraction routines or develop applications within the ERP system to do what users were asking for. But, as many business information consumers know, that process is often expensive and time-consuming. All that is about to change.

What are ADS?

The vision behind ADS is to create data on demand. As a start, the AICPA has worked with companies, ERP vendors and internal and external auditors to understand what data they need. Use of the word “audit” is reflective not only of a major user of the data standards, but also of the quality of the data. Unlike some data standards, the ADS have built-in validation tests that ensure the data is complete, accurate and reliable. ADS, however, should not be thought of as limited to auditor use only. In reality, the standards enable any authorized non-IT user to access the data, including the companies’ managerial accountants, analysts and others. 

ADS are expected to grow and evolve as systems change and as users’ needs evolve. The first release in this new ADS series included a set of base standards, as well as ones covering the general ledger and accounts receivable. These were designed with retail and commercial sectors in mind. The second wave was just released and includes the order-to-cash and the procure-to-pay processes. The Emerging Assurance Technologies Task Force of the AICPA Assurance Services Executive Committee is accepting comments on these subledger standards until February 28, 2015. Please send all feedback via email to Amy Pawlicki. Plans are underway to develop other significant business processes and then to “industrialize” these by tailoring them for industry sectors (such as financial services, health care, etc.)

How do they work?

Once the target is defined (i.e., the data standard), it can be readily mapped to any ERP system. After this mapping has occurred, the data extract process can be set up as a highly automated, repeatable process. Edit checks can be built in to ensure the data is accurate and complete. 

To demonstrate how easy this process can be, Microsoft created a pilot program that uses a simple routine to extract data from all of its ERP systems. The routine can be run either at the click of a mouse or on a scheduled basis. Once the data is validated, it can be passed to a designated location. The data and its transmission is highly secure using encryption and password protection. Authorized users can then access the data whenever they want. Data is no longer locked away in a company’s ERP system, to which only a skilled IT professional holds the key. 

Customers can design reports that comply with the Audit Data Standard, including all supporting transactional data and download them to Excel so that they can become the source for further data analysis. Users will be able to tweak them for their company and begin using them.

Companies can develop their own extractors. HP, a pioneer in this area, has developed a routine that extracts data from its SAP systems, loads it into a decision support database mapped to the ADS and presents validated data directly to the auditor. HP has reengineered a cumbersome process that used to take two weeks into a repeatable process that now takes a matter of minutes. As an alternative, data extraction companies can be used instead.

What’s next?

The obvious business case for ADS has been readily apparent to users outside the US. As a result, discussions are underway to adopt similar standards globally. In fact, it is expected that future standards may be a joint global initiative. 

William R. Titera, CPA, Partner- Professional Practice- Auditing, EY

Person reviewing data image via Shutterstock


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