Developing a data strategy is a must for audit firms. Data is at the forefront of the audit. Auditors rely on both structured financial data and unstructured business process data when assessing risk, confirming transactions, and identifying discrepancies.
When used effectively, data empowers auditors to be more productive. It enables them to spend less time sorting through documents and more time focusing on areas of risk. In turn, data allows firms to embrace new technologies, provide greater value, and deliver faster and more complete audits to clients.
Today, however, many audit firms are not fully capitalizing on the data they have available to them. Without a proper data strategy, audit firms risk significant financial, legal, operational, and reputational consequences that weaken their competitive position and limit their future growth opportunities.
What Is a Data Strategy?
SAP defines a data strategy as “a comprehensive blueprint guiding the processes, policies, and technologies for data collection, storage, management, and analysis across every area of the business.”
Today, many audit firms tend to focus on the individual tools, technologies, and software applications that are used to improve a specific function or solve a particular business problem. But there’s often little consideration given to how each of these tools fits into the firm’s broader technology stack.
This piecemeal approach makes it difficult for auditors to fully leverage data. Excel spreadsheets or other CSV-formatted tables become the default and predominant application of the stack to build the working paper and transfer data back and forth between tools.
A well-defined data strategy, on the other hand, takes a holistic, long-term, and organizational approach. It encompasses the policies and procedures needed for data governance, the tools used for data management, and the frameworks that are put in place to ensure data quality, security, and compliance.
Why Audit Firms Need a Comprehensive Data Strategy
A clear and robust data strategy is essential for maintaining high standards of accuracy, compliance, and efficiency. It ensures consistent access to reliable data, improves the quality of audit outcomes, and frees up resources for higher-value activities.
By 2025, it is estimated that the global datasphere will grow to 163 zettabytes or 163 trillion gigabytes. Crucially, 80 percent of that will be unstructured data that is difficult to process and analyze using traditional methods.
Audit firms are increasingly encountering unstructured data in the form of emails, contracts, invoices, and other business process documentation that contains important information for audit purposes. Most audit tools and software, however, have been designed primarily to handle structured data.
As a result, audit functions continue to rely on outdated, time-consuming, and manual approaches that place an excessive burden on auditors and hinder their ability to focus on high-risk transactions. Firms end up allocating large amounts of labour to reviewing documentation and verifying routine transactions, driving up costs and compromising audit quality.
In response to these challenges, audit firms are increasingly embracing generative AI (Gen AI) and large language models (LLMs) to automate routine tasks, enhance data analysis capabilities, and provide deeper insights to clients. Already, roughly two-thirds of firms are considering how to add Gen AI technologies to their workflow.
A well-defined data strategy serves as the foundation for Gen AI and LLMs by providing high-quality, well-governed, and secure data from various sources and platforms.
Finally, as firms gain access to larger volumes of sensitive client data, they must ensure compliance with data privacy laws, auditing standards, and security regulations.
What Do You Want To Achieve With Data?
As with any strategy, the first step to developing a data strategy is to clearly define your objectives and desired outcomes.
What do you want to do with data? How will it be used to deliver higher-quality audits? And how will it allow auditors to be more productive?
These questions will help to clarify the purpose behind your data strategy and provide direction. From there, work backward to determine more specific requirements, considering the data collection, processing, and analytical tools required to meet these goals.
Once you understand where you’re going, it’s also important to take a step back and understand where you are today.
Start by reviewing the current tools and technologies that are being used by auditors. What role do they play in the audit process? Do they integrate and share data, or do auditors need to manually import and export data between applications? How does this impact their ability to use the data when conducting audits?
Next, perform an inventory of all data sources, both structured and unstructured. This includes commonly encountered client databases, cloud storage, an ERP platform, third-party data sources, and other internal or external data streams. Are your auditors able to easily access this data when needed? Are client data silos preventing the efficient use of information? Can data from multiple client sources be easily combined to support analysis?
Finally, evaluate your firm’s data storage systems. Are they scalable and capable of handling increasing amounts of data? Are they secure and compliant with regulations? And can they adapt to future technologies such as Gen AI and LLMs?
The Data You Need To Be Effective
The audit industry is in the middle of a significant transformation. The exponential growth in the volume and availability of data and the use of technology to process information has created new opportunities for auditors.
A comprehensive data strategy will ensure that audit firms can capitalize on these opportunities and achieve success going forward. By taking a holistic approach to how data is collected, stored, analyzed, and used, firms will ensure that auditors have access to the data they need to deliver accurate, complete, and independent audits to clients without increasing the cost of the audit.
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