Amp Up Business Performance With Audit Analytics

There has been a distinct surge in the amount of interest and media coverage on the use of data analysis technology in business today. I believe that this increased level of awareness is being driven not so much by new tech capabilities or the marketing efforts of software vendors, but rather by today's business climate and the lessons that are being learned as the economy drags itself out of the recession and realigns itself for growth.

As organizations navigate their way towards prosperity, they're seeking to better manage business risk and gain greater insight into the efficiency of their business processes, compliance requirements and overall corporate governance. After all, you could argue that it was insufficient attention to and management of business risk that led to many of the losses incurred during the recession.

There's increasing pressure on organizations to make better, more informed decisions and to gain greater insights into business risks. That means more pressure on internal audit departments to provide heightened levels of insight into organizational risk.

Many are turning to audit analytics technology to do just that.

The use of audit analytics (i.e., purpose-built data analytics for audit) has been identified by audit industry surveys as a top-five enabling strategic priority. Looking internally at key business processes and the underlying data that reflects the nature of those processes is a natural place to start. Leading internal audit functions are leveraging audit analytics to identify risk, evaluate the effectiveness of internal controls, determine levels of overall compliance, and combat fraud and inefficiencies.

Audit analytics enables organizations to analyze transactional data to obtain fact-based insights into their operations. It helps auditors identify indicators of risk, internal control failures, and non-compliance to internal or external requirements.

What Is the Value?

By leveraging audit analytics, internal audit is able to detect changes or emerging vulnerabilities in business processes and weaknesses that could potentially expose the business to unwanted risk. This level of insight is crucial to management's timely decision-making and the ability to steer an organization through a dynamic and changing business environment. The effective use of audit analytics also helps internal auditors identify changes in internal processes and provide assurance around today's risks -- rather than yesterday's.

By analyzing data against established business rules and control parameters, internal audit departments can provide fact-based assessments of how well automated controls are operating. By being able to analyze all relevant transactions and compare data from diverse sources, internal audit can identify instances of fraud, errors, inefficiencies or non-compliance. As the expression goes, "the devil is in the details" -- but therein also lies salvation.

A properly funded and supported internal audit function can help organizations navigate their way back to prosperity through improved business performance and risk management. Audit analytics has a critical role to play in helping strengthen internal controls and combat fraud, waste, and errors.

7 Steps to a Successful Audit Analytics Project

The question remains, "How do I integrate audit analytics into my organization?" I'm a firm believer that this is best accomplished through looking towards your people, your processes and technology -- in that order. The following seven steps can help you guide your audit analytics initiative to a timely and successful outcome:

1. Align your overall audit analytics strategy with your risk assessment processes, current audit plans, and longer-term audit goals and objectives.
2. Manage your audit analytics initiative like a program, focusing on your desired end-state of maturity and the steps necessary to get there.
3. Develop a uniform set of analytic practices and procedures across assessment functions.
4. Assign responsibility for data management, quality assurance, investigation and other key roles.
5. Standardize procedures and tests into a central and secure audit analytics repository.
6. Treat training as a continuous process, measured by ongoing growth and continuous development of capabilities.
7. Aim for constant improvement through leveraged use of data analysis as analytics evolve over time.