1. A brief history of data analysis
2. How prevalent is data analytics’ use?
3. How can data analytics contribute to audit quality
4. Enhanced audit quality through data analytics

The Financial Reporting Council (FRC) has called for audit firms to do more to support the structured roll-out of effective data analytics (“DA”) techniques and technologies.

In a January 2017 review of the six largest UK audit firms (“Firms”) – BDO LLP, Deloitte LLP, Ernst & Young LLP, Grant Thornton UK LLP, KPMG LLP and KPMG Audit plc, and PricewaterhouseCoopers LLP – the FRC analysed nineteen examples of executed data analytics for the year ending 2015, of which three were nominated by the Firms themselves to demonstrate a particular data analysis software in use.

We here at AuditWare have combed through the report to pick out the key points that show why internal audit departments in all kinds of public and private sector organisations should be looking at investing in a strong and flexible data analytics solution.

But what, according to the FRC, is data analytics, exactly?

A brief history of data analytics

Before we can look at the future of DA, it pays dividend to know its history:  Many of today’s more advanced DA solutions started out as CAATs, or computer-assisted audit techniques, programmes which were used to analyse specific sets of data in order to identify a need for further, traditional testing. This process, however, required significant investments of time, as the CAATs were usually tailored to the specific entity being tested.

With the continuous development of technology over the years, it has now become easier for auditors to capture, transform, store and analyse up to 100% of the data, 100% of the time.  A key characteristic of the current increase in the use of DA in organisations is the roll out of standardised software solutions, such as our very own IDEA Data Analysis solution, coded and tested by specialist staff and deployed with central support, which results in more efficient, consistent and reliable use of DA.

This technological march has not abated, though, with some of the Firms are now considering further investment such as continuous control monitoring and benchmarking of data between clients at a transactional level.

How prevalent is data analytics’ use?

The six large UK audit firms are all investing heavily in DA. Their DA capabilities form part of the Firms’ offering to the market and, increasingly, they are promoting their use of it to build or maintain their market share. Audit committees are also increasingly requiring bid teams to be explicit about what capabilities they have, and whilst the use of data analytics is increasing, the pace of change is not as fast as the audit committees and investors might think.

For a number of years now, Firms have used DA tools and techniques in journal entry testing, and it is by far the most common form of DA testing seen in practice, followed next by general ledger testing. Beyond this, testing practices among the six Firms review varied widely, with some not using DA at all beyond the two tests mentioned.

How can data analytics contribute to audit quality

The FRC found that the DA solutions they saw in practice offer the potential, when properly implemented, to improve audit quality in a number of ways. Amongst the Firms, the FRC saw technologies implemented which could, among other things:

  • analyse all transactions in a population, stratify that population and identify outliers for further examination
  • re-perform calculations relevant to the financial statements
  • match transactions as they pass through a processing cycle
  • assist in segregation of duties testing
  • compare entity data to externally obtained data
  • manipulate data to assess the impact of different assumptions
  • visualise the data to assist understanding


With audit quality cited by all Firms as a driver for the implementation of DA on a larger scale than they currently being used, these technologies offer a number of advantages beyond simply speeding up the audit process. Improving audit consistency, efficiency, and central oversight on a larger number of complex datasets (where a manual approach would not be feasible), enhancing communications with audit committees, and identifying instances of fraud in a timely fashion were all cited as potential advantages of efficiently-used solutions.

Certain technologies also offered the facility to visualise the data being presented in order to assist auditors and the audit committees in understanding what it is they are seeing. It was noted that when the information was presented in formats such as bar charts, pie charts, or scatter graph, the audit teams could more accurately identify trends and outliers in populations.

Despite these potential benefits, however, the FRC also found that the use of DA in audit is not as prevalent as one might expect.

Enhanced audit quality through data analytics

All Firms questioned in the report cited an increase of audit quality as one of the main drivers in their adoption of DA. It was noted that when auditors worked through their first run of analysis they stood a greater chance of identify sub-populations of interest, and that results obtained at year end were notably more reliable if an analysis was run at an interim stage first. This allows audit teams and data assurance specialist to work together and address any unexpected anomalies, and identify whether they are true exceptions or standard transaction of a kind not initially considered, such as online orders shipped in multiple parts.

Enhancements to and streamlining of the audit process can take at least two years to deliver the full benefits of a data driven audit approach once it has been identified which tests run against sub-populations. It is up to the audit teams themselves to make a judgement on whether to refine their DA logic and subsequently, apply that logic to independent data sets.

Effective and efficient data capture is key to the successful use of data analytics in an audit. Teams should ascertain at an early stage whether the quality of the data that the entity’s management can provide is sufficient to support the envisaged analytic. In those Firms where the drive to adopt data analytics was ‘pushed’ from the centre, it was found that successful uptake was higher, as the internal auditors themselves may see no reason to change their audit approach from traditional, time-consuming methods they have grown used to. In situation like these a local product ‘champion’ on the team would be beneficial to ensure uptake and use remains consistently high.

Read the full report here. Please note that any images are used solely for illustrative purpose and remain the property of the Financial Reporting Council.