A business relies on data to drive its decision-making. In the past, a business’s only source of data for a decision was those generated internally from manual operations and maintained in files. But the advent of technology has witnessed the proliferation of data so much that, one can say, we are in the era of big data. That said, big data can be described as the exponential growth and availability of data created by people and digital applications. Big data is a complex dataset that is obtained from large, disparate and often external data sources and usually beyond the capacities of traditional data-processing systems. Generally, big data holistically considers data which is structured and human-generated as well as unstructured and machine-generated.
A structured data is organized in a standard format and traditionally stored within an organization and maintained in relational databases or files. A structured data is easier to control because it is internally generated from established sources in a company. Unstructured data, on the other hand, is technology-driven and takes the form of texts, emails, images, legacy formats, audio-visual resources or binary programming. Indeed, unstructured data is typically more difficult to manage due to its evolving and unpredictable nature. Indeed, the use of unstructured data to augment audit evidence is growing and becoming more relevant in managing business risks.
Big Data as Audit Evidence
Conventionally, audit judgements rely on sole evidence sourced from structured datasets in an organization’s financial records. But technological advances in data storage, processing power and analytic tools have made it easier to obtain unstructured data to support audit evidence. Big data can be used for prediction by using a complex method of (big data) analytics to glean audit evidence from datasets and other sources which encompass organizations (e.g. transaction data, customer data), industries (e.g. economic data), nature (e.g. weather, seismic data, locations and maps), internet clicks, social media, market research and numerous other sources.
Big Data Features for Audit Evidence
An auditing evidence or data should satisfy the requirements of its sufficiency, reliability, provided from an appropriate source and relevant to the audit at hand. In this regard, big data is considered a real-time audit evidence based on its nature and set of characteristics which should include:
- Volume: The amount of data being created is vast as compared to traditional data sources and counted in hundreds of gigabytes, terabytes or even petabytes.
- Veracity: Data can be verified for its accuracy and context.
- Variety: Data is in different formats and generated within an organization and also created from external sources.
- Velocity: Data can be generated extremely quickly and continuously.
- Variability: Big data is extremely variable and always changing.
- Visualization: Big data analytics involves the process of examining information and patterns from large datasets. Analytic results from big data are often hard to interpret; therefore, translating vast amounts of data into readily presentable graphics and charts that are easy to understand is critical to end-user satisfaction.
- Value is generated when new insights are translated into actions that create positive outcomes.
The application of big data in auditing is on the rise, hence, there is the need for many companies to leverage on it to make auditing more accurate and reliable. This means that a company needs to integrate and automate their processes with new digital tools and software applications that can collect large volumes of data, aggregate and process it for insightful audit evidence and analysis. It is, therefore, imperative for an organization to have adequate network infrastructure, big data management and governance system, a processing platform with huge capabilities and what is also known as advanced algorithms for high-volume and speed processing in place.
We should not lose sight of the fact that auditing influences decisions with regard to an organization’s overall investment in risk management, governance and compliance. In light of this, those factors cannot be treated lightly in establishing an innovative system for big data analysis. An innovative system will not only enable the application of artificial intelligence embedded Natural Language Processing (NLP) to streamline unstructured data but also ensure its integration with an Optical Character Recognition (OCR). These capabilities and other new cutting-edge technologies will effectively help to convert both structured and unstructured data into meaningful insights to drive audit. Thus, the use of big data is to make it easier to eliminate human errors, flag risks in time and spot fraudulent transactions and, in effect, modernize audit operations, thereby improving the efficiency and accuracy of the financial reporting process.
Leveraging Big Data & Security
It has been established that navigating and extensively screening large pool of big data (structured and unstructured data) provides valid information for sound audit judgement. The efficiency and effectiveness of the process must equally be supported by a strong cyber-security environment. This calls for robust internal controls and documentation over:
Network Security: This requires anti-malware and virus programs to protect against malicious actors from infiltrating the systems and corrupting data sources.
Network Configurations: A deliberate network configuration must be in place to spot any potential threats and safeguard network infrastructure and systems.
User Access: Controlling user access to data through passwords and other recommended measures is necessary to prevent the manipulation of data which can compromise the audit process.
Privileged Access Management: This will help to monitor the activities of privileged users who have access to specific data.
It is also important for auditors to build capacity in data analytics to empower them with the relevant skills. This way, they will be able to use different methods and tools to conduct audit with big data in the modern and evolving technological world.
Conclusion
In all, it has been established that the impact of technological innovations on big data in improving the quality of audit and assurance cannot be underestimated. Indeed, big data analysis needs an all-encompassing system architecture for data collection, transmission, storage, processing and analysis and visualization mechanisms. There again, new technologies are advancing rapidly and requiring considerable investment of resources to keep pace with changing realities. In situations where an organization with limited resources cannot invest significantly in big data infrastructure, it can partner with a leading technology firm that has the expertise to support them to tap the full benefits of big data.
BERNARD BEMPONG
Bernard is a Chartered Accountant with over 14 years of professional and industry experience in Financial Services Sector and Management Consultancy. He is the Managing Partner of J.S Morlu (Ghana) an international consulting firm providing Accounting, Tax, Auditing, IT Solutions and Business Advisory Services to both private businesses and government.
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