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Leveraging Risk Management with Data-Driven GRC

Leveraging Risk Management with Data-Driven GRC

The world is changing. In almost every industry, a new risk landscape has emerged. Risk management has become a board-level concern due to the rate of change in the risks that organizations face, both in terms of quantity and type. Unexpected risks also have the potential to bring down the business.

The significance of governance, risk, and compliance (GRC) data to organizations' risk management strategies increases as the risk landscape continues to change. As a result, efficient methods for managing risk have also evolved. This demands a thorough and methodical approach to data, which many businesses strive for, but few fully realize.

Then, data-driven GRC comes as a solution to address this problem.

But before jumping into data-driven GRC, let’s review a little bit about risk management.

Risk Management at a Glance

Risk management is defined as recognition, analysis, and control of risks or the likelihood of risks that pose a threat to an organization (Hessami & Savoj, 2011). Risk management can also be defined as the design and implementation of procedures to manage a business risk. Risk management is the preparation for the increasingly complex business activities that are brought on by scientific and technological advancement (Kasidi, 2010). In essence, risk management is the process of identifying the appropriate risks, allocating the appropriate likelihood, and estimating potential harm.

Almost every industry now faces a shifted emerging risk landscape. Data-driven GRC refers to a consolidation of methodologies that significantly improve the possibility to address that problem.

Introducing Data-Driven GRC

Data-driven GRC is an integrated methodology that uses technological tools to analyze transactional business data to evaluate and monitor strategic risk in real time. According to Galvanize (2019), data-driven GRC represents a consolidation of methodologies, both functional and technological, that dramatically enhance the opportunity to address emerging risk landscapes. This maximizes the reliability of organizational performance.

Several companies can conduct an ongoing dialogue about a change endeavor with employees, enabling change managers to link this discourse to the development of the projects they are launching. These tools can already have a significant impact on change programs, but as presented in previous researches and practiced on developing predictive models of change, the data stream produced may become much more crucial. Data-driven change projects should be implemented effectively for future success.

How to Achieve Data-Driven GRC

A data-driven approach to risk and control-related processes will be used to achieve the future state of maximized value and relevance within the organization for risk and control-oriented functions. Data-driven GRC is a method that uses technological tools to assess and track strategic risk in real time by looking at transactional level business data.

By taking these actions, organizations can achieve data-driven GRC.

1. Identify frontline controls for important strategic risks accurately

2. Conduct control tests based on empirical data

3. Set such tests on a regular basis for ongoing evaluation

4. Link testing results in real time to corporate risks

End-To-End Integrated Data-Driven GRC Methodology

The four steps above are only the preview of steps in achieving data-driven GRC, or at least the least measurement that organizations should take. According to Galvanize (2019), following a phased approach to building this methodology is the core driver of successful end-to-end data-driven GRC.

Step 1: Design a simple, practical GRC methodology

Any GRC function needs to establish a fundamental procedure for identifying risks, controls, tests, and resulting issues. It should be closely related to the executive management and board levels of the organization's corporate risk agenda.

Step 2: Leverage data analysis in controls testing

The execution of truly effective tests to evaluate controls typically ends up being the organization's biggest weakness once the procedure for assessment and mitigation of key risks has been defined. Utilizing next-generation testing (technological tools) techniques is necessary to get a better understanding of when control deficiencies and gaps occur.

Step 3: Integrate GRC & data analysis methodology

In this step, an organization may start the strategic integration of next-generation and analytical techniques with GRC processes. The organization's risk and control-oriented functions should start standardizing on necessary coverage models. The executive dashboarding of the results and issues as well as the visual reporting will be provided, and they will also directly integrate the reporting from the testing into the process.

Step 4: Leverage continuous monitoring for real-time insight

The next step in improving value delivery for an organization after utilizing data analytics testing is to move at the pace of current economic realities. Only identifying and reporting on a particular risk or issue on an annual, or even quarterly, basis is no longer sufficient. Automated tests that are continuously running are required.

Step 5: Integrate GRC & continuous monitoring methodologies for data-driven GRC

Organization then links the results of ongoing monitoring activities with the risk and control environment in which they are appropriate. To accurately reflect where data indicators of those risks sit in, each process is automatically updated with information about the identified issues. This in turn guides the assessment of risks at the strategic risk level. This is the crucial stage because it is here that all of the work can result in stakeholders meaningful decisions in the present that reduce risk levels they were previously blind to, ultimately improving the consistency of organizational performance.

Data-driven GRC is not achievable without technology to support the overall process. From the technology perspective, these 4 key components are required for a successful data-driven GRC implementation.

1. Integrated Risk Assessment Technology

It keeps track of the strategic risks and evaluates how well they are managed.

2. Project & Controls Management System

It enables to create project plans for each risk and control function that correspond to the risk.

3. Risk & Control Analytics Toolset

It is used to acquire all the data required to be combined, filtered, and processed for decision making.

4. Knowledge Content

It should be acquired to support the individual risk and control objectives.

All in all, an effective data-driven GRC is essential to maximize the risk management across all three lines of defense, integrating and standardizing risk data, and making it visual, relevant and actionable.

Galvanize. (2019). Mastering Risk with Data-Driven GRC. Retrieved from https://www.wegalvanize.com/assets/white-paper-data-driven-grc.pdf?_ga=2.168561057.1005016456.1634001296-654547982.1630483413
H. Z. Hessami & A. Savoji. (2021). Risk Management in Supply Chain Management. International Journal of Economics and Management Sciences, vol. 1 no. 3, pp. 60-72, 2011.
Kasidi. (2010). Risk Management. Jakarta: Ghalia Indonesia.

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