Risk Assessment Analytics: 9 Steps to Elevate Your Risk Management Plan

Risk Assessment Analytics: 9 Steps to Elevate Your Risk Management Plan

Any comprehensive risk assessment includes the evaluation of threats to your organization or enterprise — but what tools or technology do you use to conduct this analysis? Part of the answer depends on your metrics — what are you measuring? The right statistical solution can help you use big data to better predict trends and outcomes, discover potential risks to your enterprise, and prioritize actions in your risk management plan. The following article covers the different types of risk assessment analytics you might consider applying during the evaluation phase of your initial risk assessment, so you can create a strong, accurate risk management plan. 

What Is Risk Assessment Analytics? 

If you take the time to collect data about the risks your organization may face in the future, don’t just leave that data sitting in a table. Analyze it! Risk assessment analytics is the practice of applying data analytics to your risk assessment in order to identify threats to your organization and develop a clear risk assessment plan for remediating and responding to those threats. Data analytics for risk assessments can range from basic statistical modeling during the design phases of a project to sophisticated machine learning models that paint a dynamic risk picture based on large data sets.

How Do You Analyze a Risk Assessment?

A good risk management plan collects and collates all potential risks your organization might face. It should break down the risks into categories, assign risks to specific owners, and consolidate data that shows where risk mitigation efforts will be worthwhile. Your approach to risk assessment analytics will depend on where data is sourced, the type of data you’ve collected, the regulatory requirements you are following, and your goals as an organization.

Key risk indicators (KRIs) are a great way to track and monitor risks. KRIs are typically measurable and predictable and allow organizations to monitor risks as early warning signals of potential risk, while also tracking trends over a period of time.

Why Is Risk Assessment Analytics Important for Business? 

Risk assessment analytics are a core part of the evaluation phase of a risk management plan; analytics allows you to measure the cost, impact, and likelihood of your potential risks and their potential outcomes. This is important as it helps you to prioritize which threats are worth tackling, which one you will want to address first, and which risks require greater budgetary allocation than others.  

9 Steps to Boost Your Risk Management Plan Using Risk Assessment Analytics

While your approach will ultimately be tailored to your specific industry, data requirements, and needs, here are nine steps to guide you in choosing a strategy for risk assessment analytics that will work best for your organization: 

Step 1: Classify Your Data

There are three types of data you may be working with when you’re assessing a risk: public, private, or restricted. 

  • Public data is readily accessible to the public and does not need special processing for data analysis or sharing; this data type includes social media posts or real estate records. 
  • Private data includes any information that can be tied to a specific individual, like electronic health records or payment card information; to be processed or shared, this type of data needs to be secured based on applicable security regulations such as PCI DSS and HIPAA
  • Restricted data often refers to classified governmental or military records related to matters of national security; if you are handling this kind of data, you will need to follow specific internal guidelines and/or regulatory requirements your organization may adhere to. 

Step 2: Determine Data Accessibility

Depending on your organization or field, you may have barriers to accessing certain types of data or sharing data across your organization. Make sure your risk owners and analysts can access the data they need. If a particular type of data requires encryption, de-identification, or special handling in any way, your risk analytic program needs to take this into consideration. 

Step 3: Consolidate Your Business Goals

It’s crucial to achieve alignment among your C-suite executives on business objectives before conducting your risk assessment. This includes achieving CFO compliance, making sure that your financial team is educated on the bottom-line benefits of risk analytics. Creating consensus on your business goals will help you determine which risks are going to be most important to address in your budget and, thereby, which data analytics will best serve your organization. 

Step 4: Identify Your Compliance Needs

The regulatory requirements of your field will help you to determine your most salient risks and the data you need to collect to measure those risks. For example, if you need to be HIPAA compliant, because you handle private health information, you could face hefty fines for a data breach. This knowledge allows you to know you have to assess private data, test your information security systems, and potentially survey your staff on their knowledge of HIPAA before you know the shape of the risk. 

Step 5: Compile Your Risk Library

Based on your compliance needs, business goals, and industry standards, develop a comprehensive library of risks that are specific to your company; the library, risk assessment plan, and data analytics will form a feedback loop, and you will update the library as you gather new insights from the data. 

Step 6: Build a Risk Assessment Matrix

A risk assessment matrix will allow you to rank and order the likelihood, impact, and severity of each risk and identify key risk indicators (KRIs), so you can update your risk register and prioritize risks accordingly. A 2019 Safety Science article proposes a three dimensional risk assessment matrix to accompany their machine learning model, adding an axis of knowledge to the traditional impact/probability dimensions; if you choose to automate your risk assessment using machine learning tools, your risk assessment matrix will take a different shape. 

Step 7: Select Your Methods

Your risk analytics methods will most likely be quantitative. For example, if you are reviewing HIPAA violations, you will likely look to quantify risk likelihood and severity. However, in some cases, you may need to collect risk data from surveys, interviews, or even observational studies of stakeholders in order to identify unknown risks or behaviors that might introduce new risks. For example, say you have trained your staff in HIPAA, but you survey your staff and discover that employees are taking shortcuts to bypass the effort of a multi-step email encryption process. Your data analytics around HIPAA breach risks may incorporate qualitative methods to make sense of the social dimension of the risk and tell the story behind your quantitative data. 

Step 8: Conduct a Comparative Analysis 

A comparative analysis will help you prioritize your risks and determine which to tackle first and which can wait. For example, your risk assessment matrix might leave you with a few risks that seem similarly impactful — you will have to determine which one of these risks needs to be tackled first. 

Unlocking Operational Risk Management: Empower the Front Line to Effectively Manage Risk

Step 9: Visualize Your Risks

Once you have compared risks and you are ready to communicate your findings across your organization, to your C-suite executives, or stakeholders, visuals will also make your data analysis comprehensible and enable you to distill it into actionable agenda items. A modern risk management platform should integrate risk assessment analytics and data visualization, so you can create compelling materials that will move your organization forward in managing risk and protecting its bottom line.  

Ready to Start Quantifying Your Risks Effectively? 

Risk assessment analytics is a dynamic field — from traditional statistical tools to AI and machine learning, the approach you take to analyzing your data will depend on the maturity of your organization and the scale and complexity of your risk data. Take the insights you’ve gleaned from your data analysis, and you’ll be prepared to mitigate and monitor your risks. A connected risk management platform centralizes your audit, risk, and compliance data in a single source of truth, giving you a complete view of risk across the enterprise. Learn how AuditBoard’s risk management solution can help you create a central repository for risks, measure the effectiveness of your efforts over time through configurable KRIs — and visualize your progress.