Risk analysis is a critical practice for any organization or business leader. Risk analysis methods generally fall into two categories: qualitative (based on subjective or descriptive data) and quantitative (based on numerical data). By following a clear, structured process—and using scalable tools like @RISK and Predict!—businesses can move beyond spreadsheets to improve forecasting, reporting, and risk-informed decision-making.
The world of business has always been inherently risky. Today, the number and intensity of risks has increased: from global pandemics and extreme weather to cyberattacks and geopolitical instability, organizations must find ways to identify, quantify, and mitigate the risks to their operations.
For organizations to operate successfully in this environment, risk analysis is a strategic necessity. Comprehensive risk analysis helps businesses manage uncertainty and develop more robust business continuity plans.
This article offers an essential guide to risk analysis: what it is, key use case scenarios, and the basic steps involved in carrying it out. It also looks at how advanced risk analysis software solutions are helping businesses of all sizes gain access to sophisticated forecasting tools and centralize their risk management processes.
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Risk analysis is part of the overall risk management process. It involves:
Performed well, risk analysis can help inform mitigation strategies—processes and practices that blunt the impact of a threat.
Risk analysis practices can be applied in many ways across many industries. However, it’s possible to draw up broad risk analysis use cases that span multiple industries. Here are a few of the most common scenarios for risk analysis:
Cost risk analyses look at the financial impacts of risks in order to help develop more realistic budgets. When conducted using Monte Carlo simulation tools, such as those in @RISK, cost risk analyses can be especially helpful for identifying areas of variability (e.g., fluctuating costs for materials or energy). They can also be used to more accurately allocate contingency funds—additional resources beyond the project’s baseline cost.
Schedule risk analyses evaluate different project risks to help determine realistic timelines and deadlines. Again, Monte Carlo simulation-based tools like ScheduleRiskAnalysis, part of Lumivero’s DecisionTools Suite, can help generate probabilistic completion ranges that account for uncertainty rather than deterministic, single-point deadline estimates.
Project risk blends the previous two types of analysis to evaluate the overall risks facing a project. The goal is to evaluate, rank, and mitigate any threats that could impact a team’s ability to complete the project on time, within budget, and in a manner that meets stakeholder expectations.
Risk analysis matters because it helps drive better-informed decision-making within an enterprise. Better decisions can lead to more efficient operations. Additional benefits of risk analysis include:
Sophisticated risk analysis processes, such as those based on Monte Carlo simulation techniques, can help businesses identify which aspects of a project, process, or investment are likely to have the greatest negative impact on their goals. This risk identification process informs mitigation strategies that can reduce or avoid missed deadlines, overspends, and other losses and failures.
Mitigating these losses and failures also contributes to business continuity planning—strategies and tactics for ensuring organizational survival even in the face of serious events such as a major product recall, a ransomware attack, or other business-threatening occurrences.
Once risks are identified, leaders are able to develop plans that account for uncertainty and prioritize risks by level of impact. This helps managers and leaders understand how to distribute resources—whether that’s labor, materials, or investment funds—to further mitigate the riskiest aspects of their projects.
Strong risk analysis practices can also help support better reporting for compliance and better stakeholder communication. Risk management tools, such as Predict!, make it possible to provide an auditable trail of decisions and actions that show how risks were analyzed, what steps were taken to mitigate risks, and the impact on project deliverables or business goals.
With Predict!, risk analysts can also produce clear, boardroom-ready reports and visualizations that help communicate risk management issues to a wide range of stakeholders.
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There are many different risk analysis practices and techniques. However, all risk analyses fall into one of two categories: qualitative or quantitative. Most organizations will use both qualitative and quantitative methods to ensure they are capturing as much as they can about the risks their organization face, and some types of risk analysis involve a mixed-methods approach (combining qualitative and quantitative information into one report).
Qualitative risk analysis uses informed but subjective methods for identifying and evaluating risk. Gathering and comparing expert opinions about risk scenarios, for example, is a qualitative practice, as is conducting a survey.
Qualitative risk analysis can:
Qualitative analyses usually produce outputs that describe risks and consequences in verbal terms.
Popular qualitative risk analysis techniques include:
Bow-tie analysis is a risk analysis practice that involves gathering expert opinions to define proactive and reactive mitigation strategies for specific risks. Bow-tie analyses are named for the chart used to visualize them: proactive risk controls on the left, the risk in the center, and consequences of the risk on the right (along with reactive mitigation strategies for reducing the risk’s impact).
Named for the famous Ancient Greek oracle at Delphi, the Delphi method is a systematic way of brainstorming about risks. Anonymous panels of subject-matter experts evaluate risks, review group answers, and then re-evaluate them, eventually arriving at a group consensus.
Another qualitative risk analysis technique is generating a risk assessment matrix, sometimes also called a heat map. A risk assessment matrix is a grid that helps rank different risks according to their impact. The ranking can be based on how likely the risk is to occur (a consequence-probability matrix) or by how difficult it will be to deal with the consequences of the risk (an impact-difficulty matrix).
There are many different types of risk matrix templates, ranging from basic 3x3 grids to more complex designs. Predict! includes a range of different configurable assessment tools that allow you to generate matrices and other reports directly from your risk register.
Quantitative risk analysis uses objective, numerical data and mathematical modeling to evaluate risks and measure their impact. Quantitative methods can help assign values to the likelihood and cost of risk outcomes. This is how it differs from qualitative analysis, which usually assigns descriptive terms to risk like “high”, “medium”, or “low”.
Quantitative risk analysis can:
Common qualitative risk analysis techniques include:
Monte Carlo simulation is a statistical analysis technique that involves running hundreds or thousands of simulated events using defined ranges of variables. For example, a company planning to switch to renewable power for its production plant might model wind and sun conditions to determine how likely it is that a given solar panel or turbine array will provide sufficient energy to run the factory—and what the cost to the business could be when it doesn’t.
Decision trees are similar to flow charts: they represent different scenarios that can result from different decisions. However, they are qualitative because they are based on prior probabilistic analysis, and because they take into account the conditional nature of decisions. They help turn probabilistic forecasts into policy pathways that minimize risk. Decision trees can be applied to everything from healthcare treatment decisions to planning litigation strategies.
Monte Carlo simulation tools like those found in @RISK can help risk analysts model hypothetical scenarios to understand how they impact an organization, project, or process. An article published by the Global Association of Risk Professionals explains that stress testing produces “‘what if’ scenarios for the strategic and capital planning processes.”
Stress testing methodologies include:
Now that we’ve identified the different categories of risk analysis—qualitative or quantitative—and outlined some of the techniques used in each category, we can look at some of the common risk analysis types. Each of these analyses may use a different blend of qualitative and quantitative techniques, depending on the industry, the goals of the analysis, and the types of data available to risk managers.
Risk-benefit analyses (or cost-benefit analyses) are an extremely common risk analysis type. They involve weighing up the negative and positive aspects of taking a risk in order to guide decision-making. The potential costs are subtracted from the benefits to develop a ratio of risk to reward.
Conducting this type of risk analysis challenges organizations to account for every type of potential cost and benefit involved with a decision. It’s crucial that as many variables as possible are considered. Incomplete or incorrect data can result in faulty analysis results.
Another common form of risk analysis is a business impact analysis (BIA). A BIA models specific disruptions to a business—for example, supply chain failures, power cuts, or cybersecurity attacks—to determine potential financial and operational impacts.
BIA outputs can be used to inform decisions about risk mitigation strategies, including which steps to prioritize for restoring business operations.
A needs assessment analysis helps identify gaps between the current state of an organization and its desired state. According to a webpage published by the Lawrence Berkeley National Laboratory, a needs assessment is “a systematic search for identifying deficiencies between actual and desired job performance.”
Needs assessments are frequently used in education and training contexts, but can also be deployed as part of a risk analysis to evaluate mitigation strategies and other factors.
A root cause analysis (RCA) complements other risk analysis techniques by working backwards from an outcome and evaluating all the possible causes to understand how the result could be repeated. Originating in the airline industry as a way to evaluate the causes of crashes, RCAs are now applied to problems in many other industries, including manufacturing, healthcare, and more.
The Six Sigma website describes several different approaches to modeling an RCA, including fault trees, fishbone diagrams, and scatter plots.
A value at risk analysis (VaR) is a common type of financial risk analysis. It’s an evaluation of an enterprise’s exposure to financial losses within a specific timeframe. According to Investopedia, VaR can be computed in three ways:
Marginal VaR is a slightly different risk assessment: it evaluates how much a new investment is likely to change the existing risk for a portfolio.
There is a wide array of tools and solutions available to support risk analysis. Different tools are suited to different risk environments—for example, a complex financial investment strategy may prefer to use statistical software to analyze past performance data rather than relying solely on a qualitative, Delphi method-style approach. Some of the most used tools include:
Risk registers are centralized documents that list all known risks for a given project or organization. Risk registers include, at a minimum:
Predict! risk management software helps organizations create centralized risk registers that can be used to generate joint probabilistic cost and schedule risk reports via integration with @RISK.
In a data-driven world, statistical analysis software makes complex quantitative risk analysis techniques possible. Lumivero’s @RISK, for example, is a Microsoft Excel add-on program that makes it possible to run a wide range of Monte Carlo simulation-based analyses and other statistical analyses on your models.
Enterprise risk management systems aim to end fragmentation of risk reporting and enable risk visibility across an entire organization. Predict! offers a centralized, scalable platform for risk management, from the development of risk registers and risk analysis calculations to generating reports for stakeholders or compliance audits.
Learn how to solve top business challenges with proven statistical and risk models in “The Essential Guide to Business Decision-Making."
Risk analysis techniques vary widely according to the issue at hand, the size of the organization, and the industry involved. However, there are four basic stages involved in carrying out any kind of risk analysis. These include:
A risk analysis is only useful if it’s carried out using sound methodologies and high-quality data—then subjected to thorough evaluation and feedback from stakeholders or experts. Some best practices to keep in mind include:
Want to standardize best practices for risk analysis across your organization? Learn more about Lumivero’s enterprise-level tools for every aspect of risk management, including analysis.
The exact methods for performing risk analysis can vary greatly depending on your organization, your industry, and the types of data you have to work with. However, there are generally four steps:
Risk analysis software helps you gather, organize, and evaluate data about your risks. This could be qualitative data that generates a risk register and/or reports such as bow-ties or risk matrices, or it could be quantitative data that results in cost estimates based on simulations or a sensitivity analysis that evaluates the impact of different risk factors.
There are many types of financial risk techniques. A few common ones include:
