An increase in cybersecurity threats characterizes the growth of an organization. As such, institutions endeavor to improve their risk management techniques to ensure that all the information is protected from malicious individuals. One such measure involves the automation of most of the company’s documentation processes to comply with various security regulations.
The rapid evolution in technology has been utilized by cybercriminals to develop sophisticated techniques to phish data. As such, organizations are obliged to ensure that they not only institute measures to curb such activities, but also conduct regular auditing to establish their efficacy.
To further guarantee security, organizations use internal audit data analytics which gives more targeted recommendations thus ensuring tighter security regulation controls. This article will explain the concept of big data and its uses in data analysis and auditing.
Big Data: What is it?
Classification of data is based on the velocity, volume, and variety of the pieces of information it contains.
Big data constitutes large volumes of company information that may belong to different departments. The data could be structured (in the form of tables), unstructured (text or images), or binary programming. All of the information involved in big data requires flawless integration to ensure an easy analytic process.
Due to the large volume, velocity, and wide variety of big data, it can be cumbersome for organizations to integrate it without significant chances of costly errors. As such, it is necessary that organizations use an audit program that can help filter data from various locations in real-time when collecting information.
How Data Analysis Utilizes Big Data
Companies’ data sets can contain a lot of information. If a company fail to utilize a data analytics program, this information will remain irrelevant and difficult to implement. As a result, employees will struggle to find errors and make recommendations. Every company should strive to utilize analytics tools to boost productivity.
Data analysis can either be predictive or prescriptive:
- Predictive Analysis. This approach uses machine learning, data mining, and modeling to predict what is likely to occur next. As such, it’ll become relatively easy for the organization to take precautionary measures whenever there are possible risks. This makes the tool ideal for business risk analysis.
- Prescriptive Analysis. When a company needs to develop a priority list, then this approach will be highly instrumental in the entire process. Leaders should utilize it whenever they want to make crucial company decisions. For example, they may use the approach when identifying the financial reporting controls that need modification to strengthen them before use.
When a company uses data analysis techniques, organizations can more easily identify risks, develop mitigation plans, and establish a highly efficient risk and activities prioritization plan.
What is Audit Data Analytics?
The American Institute of Certified Public Accountants (AICPA) has set auditing standards that every organization should follow to guarantee data usability and safety. The regulation requires that the internal audit process is documented for ease of reference. Other regulatory bodies that dictate the use of data include the Sarbanes-Oxley Act of 2002 (SOX). The law ensures that the external auditors are provided with internal audit reports that explain the efficacy of the steps instituted to guarantee effective financial reporting controls.
It’s undisputed that different organizations use varying Software-as-a-Service platforms. As such, it’s necessary that every institution conduct continuous monitoring of cybersecurity threats. All of these approaches utilize data analytic tools which ensure a strengthened system.
Impact of Big Data for Audit Evidence on Professional Skepticism
Professional skepticism plays a crucial role in audit quality. Traditionally, the audit process relied heavily on the personal opinion of the auditors who reviewed the organization’s procedures, policies, and processes. However, this method was never fool-proof!
As such, organizations have adopted automation to enhance the efficacy of the entire process. However, the change should be accompanied by the development of new audit procedures to ensure that the entire process upholds professional skepticism.
The Importance of Data Scientists in an Audit Firm
The quality of the data analysis team determines directly the success of any organization. Leaders should focus on ensuring that their firm has scientists that can effectively use the automated systems to develop simplified reports for the consumption of all the stakeholders.
Additionally, these data science professionals should develop tight security measures to ensure that the company’s data is protected from cybercriminals. This may include an established firewall, password-protected segments, limitation to unauthorized entry, and other mechanisms to keep malicious individuals at bay.
Protection Mechanisms for Companies During the Big Data Era
Organizations should always ensure that they maintain strong data protection measures. This will be achieved by developing continuous monitoring systems which eliminate any chances of data infiltration by malicious individuals. Below are some of the strategies that an organization can utilize to enhance protection:
- Network Security. Ensure that no individual can track passwords and other protection mechanisms using your network
- Change Configuration. One should always change configurations provided by the vendor
- Use Access. Limit data access to specific people (data segregation)
- Privileged Access Management. Ensure that the organization monitors people with exclusive data access rights. In case of change of companies, ensure that there are enough measures to prevent the employee from leaking data
Why Cybersecurity Enables Audit Quality
Contrary to the beliefs of many, cybersecurity does not only protect a company from data breaches. It also enhances all business processes. The integration of technology ensures a smooth flow of business operations, which further enhances the quality of audit processes.
Ken Lynch is an enterprise software startup veteran, who has always been fascinated about what drives workers to work and how to make work more engaging. Ken founded Reciprocity to pursue just that. He has propelled Reciprocity's success with this mission-based goal of engaging employees with the governance, risk, and compliance goals of their company in order to create more socially minded corporate citizens. Ken earned his BS in Computer Science and Electrical Engineering from MIT. Learn more at ReciprocityLabs.com.