Challenges of Auditing Big Data

Big data is anticipated to make important contributions in the audit field by enhancing the quality of audit evidence and facilitating fraud detecting. A data set can be considered big if the current information system is cannot deal with it.

Challenges of Auditing Big Data:

Challenge 1: Equipping Auditors With The Right Skills

Today’s auditors are faced with complex business models which do not always operate in the same way as the more traditional ones.

Technological developments have created sophisticated systems which have greater capabilities and the auditor needs some insight into, and understanding of, how these systems work to be able to audit the organisation effectively.

This presents a challenge around how to appropriately train and educate our future auditors and has implications for the pre- and post-qualification training options that we provide.

Challenge 2: Variation In Data Quality

Big data has the potential to play a vital role in the audit process by providing insight into information which we have never had access to previously.

However, it is important to recognise that data quality is an issue with all data and not simply with big data. Inaccurate data or data which does not deliver the appropriate information poses a challenge for the auditor.

If this data is relied on in an audit it may result in incorrect conclusions being drawn.The challenge will be in determining what data is accurate.

Challenge 3: Data Protection And Privacy Laws

We need to ensure that we have a rigorous approach as to how we use and store data that is in the public domain or which has been provided to us by third parties.

Auditors carrying out forensic work will find data held on mobile phones, computers or household electrical items to be tremendously useful and they may use a range of different techniques to extract information from them.

However, as with all digital data we need to ensure that we handle it in the correct way and this will involve adherence to the principles of the Data Protection Act and associated legal guidance.

Challenge 4: Technology Integration

The challenge for the auditor is to understand how to integrate these big data sources into their existing data management infrastructure and how to use the data effectively.

The auditors of the future will need to be able to use data held in large data warehouses and in cloud-based information systems.

Challenge 5: Data Integrity

In some instances the auditor may have access to high quality data from off-the-shelf systems but there may be doubts as to the integrity of the data.

This may be due to the systems having been used for other purposes over a long period of time so there may be concerns about the reliability of the data.

The challenge facing the auditor is to be able to determine whether the data they use is of sufficient quality to be able to form the basis of an audit.

Challenge 6: Lack Of Access To ‘source’ Information

Access to good quality data is fundamental to the audit process.

As an audit progresses it will be necessary to retrieve additional data and if the data is not up to the required standard it may be necessary to carry out further work to be able to use the data.

Auditors will need to have access to the underlying data and if the auditor has doubts about the quality of the data it will be more challenging to determine whether the information is accurate.

Challenge 7: Big Data Analytics

An audit tool with the right analytics will strengthen the auditor’s ability to evaluate and understand information.

Data analytics involves those processes which are designed to transform data into information and which help the auditor to identify and assess risk. Audit analytics will allow the auditor to analyse the data they are now using and to scan their findings against what they already know about the entity.

Depending on the analytical tool being used, the results may be returned to the auditor in interactive digital dashboards providing results in a range of different formats. These will contain statistical summaries, visualisations of data and other analytical items which the auditor may use to identify material misstatements or to check for fraud.

Challenge 8: Data Integration And Data Integrity Across Multiple Sources

One of the challenges to be addressed in the future is how to integrate multiple sources of data using detection models – so that as new data sources are discovered they can be seamlessly integrated with the existing data.