• Risk Wizard Team

4 Data Analysis Categories for Risk Managers

Updated: May 17

It’s been often said that data is the new oil. Who could have foreseen that a company like Google founded on helping users find websites through a search bar would become one of the biggest, most powerful corporations in the late 90s? Today’s world of online, digital business is more data-driven than ever before. This means new opportunities for data scientists, data security, improved methods of data collection and the ability to derive meaningful results and information from seas - no, oceans - of data.

Risk managers certainly stand to benefit from data analysis, but the method in which it’s presented makes a huge difference in how it is perceived and how effective the data can be to drive meaningful change towards mitigating and reducing risks.

Below are four data analysis categories worth considering:

1. Visual Analysis

Perhaps the most popular method of data analysis, visual analysis consists of simplifying data down to visual elements that clearly convey the meaning of the data without the need for coding languages, mathematical calculations, or any other ‘complex’ stuff that your average layman wouldn’t be able to grasp. Pie charts, simple graphs, and other such tools are simple, but nevertheless powerful at conveying data to everyone involved in the organisation.

2. Trend Analysis

For risk managers, cause and effect are crucial for determining root causes of risks and isolating solutions that reduce their chances of occurring going forward. By sorting and organising data in such a way that trends are clearly visible, risk managers can perhaps see what had previously been unseen in a thick fog of data.

For this reason alone, trend analysis is perhaps the most important method of data analysis for risk managers. Internal meetings of risk managers and key stakeholders can and should involve plenty of trend analysis that outlines the risk trends identified and how to resolve them.

3. Descriptive Analysis

Also commonly called ‘metadata,’ descriptive analysis is needed to explain, justify, and outline your data. It simply wouldn’t make sense to send giant spreadsheets or databases to every single employee within an organisation and to say “just go and find what we found on your own,” summarisation and clean, concise key findings need to be front and centre without any fluff.

Descriptive analysis is used to demonstrate what is the status quo of the organisation or a particular project, what has transpired over time, and ways forward, thus making it not a completely objective summary of data but rather it is also normative in that it interprets the data through the lens of the organisation’s goals.

4. Comparative Analysis

What good are numbers and data if they cannot be compared to one another? The number 10,000 on its own says nothing. Is it $10,000 in revenue for the last quarter? Or is it 10,000 lost hours of productivity due to workplace injuries? As you can imagine, there’s a massive difference and data needs to be displayed in a meaningful context, which is why comparative analysis is critical.

With better comparative analysis, risk managers are better able to visualise data in a better ‘big picture’ perspective and provide key insights that are relevant and thoroughly planned.

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