Qualitative vs. Quantitative Analysis for Cybersecurity

Any company can benefit from a cybersecurity risk assessment. Learning where an organization’s most significant vulnerabilities lie is the first step to addressing those risks to stay safe. However, businesses face two main options for these assessments — qualitative vs. quantitative data analysis.

Qualitative and quantitative data analysis can reveal your cybersecurity risks, but they approach the issue differently. Because each organization’s risk landscape is unique, both approaches can be more or less useful in different scenarios. Making the best choice between them starts with understanding what each offers.

Qualitative data analysis seeks to answer “what if” questions about different risk scenarios. In a cybersecurity context, that could look like asking, “How likely are we to experience a data breach from our IoT endpoints? What would happen if an outsider did get through these devices?”

This kind of cybersecurity risk assessment is highly subjective, relying on people’s experience with and knowledge of these issues. Considering the world faces a cybersecurity workforce gap of 3.4 million people, that can skew the results in some businesses. Without a large or experienced enough security workforce, you may not be able to judge risks accurately based on opinion alone.

While qualitative analysis is prone to bias, it has several advantages, too. One of the biggest is its fast and relatively easy performance. Some insiders may also have valuable input on risks that can be difficult to quantify and this approach can be a valuable way to determine how and why something occurs.

The other side of the qualitative vs. quantitative data dynamic takes a more numbers-based approach. Instead of asking people to judge risks based on their knowledge and experience, it uses hard data to assign specific figures to these issues.

A quantitative cybersecurity risk assessment analyzes incident history and overall trends to determine a risk’s likelihood, then assigns it a dollar value based on company-specific information. Because it focuses on real numbers instead of intuition, there’s less risk of bias. Quantitative data analysis may also offer more practical insights by providing specific numbers about different risks’ costs.

The danger in quantitative analysis is businesses don’t always have the data they need for it to be reliable. Decisions based on inaccurate or incomplete data cost organizations $12.9 million annually, so a lack of available information or data entry errors can severely hinder effective decision-making.

Given these advantages and disadvantages, which option is best in the choice between qualitative vs. quantitative data analysis? More often than not, it’s best to use a little bit of both.

Qualitative data analysis is best for forming a hypothesis, whereas quantitative analysis is better at validating. Businesses can apply that in a cybersecurity context by using qualitative analysis to outline their most significant risks, then using quantitative research to assign specific numerical values to these vulnerabilities to provide more practical insights.

Insider opinions can be valuable sources of information on things that may be difficult to quantify with hard data and can explain why something is a risk. Quantitative data analysis can then provide more insight into these problems, showing where to invest the most to stay as safe as possible. Both sides are crucial in the fast-changing, often unpredictable world of cybersecurity.

Businesses today spend 47% more on data-related costs than they did before the COVID-19 pandemic. In light of those high expenses, protecting this data is more crucial than ever, so taking the most holistic approach to risk assessment is best. Using both sides can also help manage analysis costs for a better return on investment.

Cybersecurity risk assessments are crucial, so it’s essential to approach them the right way. Moving past the idea of qualitative vs. quantitative analysis to see them as complementary is the best way forward. By combining these approaches, you’ll account for the shortcomings of each and produce the most reliable, insightful results.