What Is Data-Driven Risk Management?

David Thomas

Data-driven risk management sees companies compile and analyze data from multiple sources and use it to identify, forecast, and preemptively mitigate issues in business operations. Using data, AI, and machine learning can help companies decide which problems may arise in most business activities to brainstorm solutions before these problems occur. A data-driven approach can also help keep managers, employees, and shareholders on the same page regarding business risks. 

In our information-hungry culture, it only makes sense to apply data to risk management — and in many cases, it may be the difference between success and failure.

 

Building a Data-Driven Approach

Although the term may sound straightforward, the task of building a data-driven approach to risk management is more complex. You may be familiar with risk management, but it is considered a relatively recent practice to implement data analysis into your strategy. Often, data analysis can pinpoint inefficiencies, threats, errors, and uncertainties better than human speculation.

For a well-rounded risk management strategy, companies must include several sources of data, as not to miss any upcoming issues. However, there are several things to keep in mind when taking a data-driven approach to your risk management assessment.

 

Data Democratization and Accessibility

Whatever data sources you choose to use, you must make them easily accessible to anyone involved in the risk management and solution processes. Data silos don’t provide a huge benefit, and can impede your business’s ability to perform. 

For instance, if a risk manager has access to this data and highlights a potential problem to be solved by another department, that department had better be able to access this data. If they can’t, not only is the data virtually useless, but more resources will need to be put toward making that data accessible — all while the original problem continues to fester.

Essentially, the process of giving everybody access to data that needs it is called data democratization. In the above example, the manager is a gatekeeper that creates a bottleneck to the flow of information. 

For a smoothly operating data-driven approach to risk management, it is not enough for select individuals to have the passwords to the data sources (or software) being used to identify risks. 

 

Use Data

Once everyone who needs this data has easy access, they will then need to know how to interpret the data appropriately and put it into action. If no one knows what the information means, they won’t be able to use it to inform action. 

 

Align Data

It’s important to keep the bigger picture in mind when sifting through all of the information. To support organizational goals fully, you will need to understand how one piece of data connects to another. For instance, the data may show that product productivity is low, but it may connect it to a larger problem of inefficiencies in the supply chain.

 

Using Data for Risk Management

Between finding the right tools and tightening up your strategy, there are plenty of ways data can bolster your risk management processes.

 

Find the Right Tools

An essential tool you will need for a data-driven risk management strategy is a risk management information system (RMIS). An RMIS platform allows organizations to simplify and automate claims management and incident reporting, with additional customizable modules to support a variety of industries. For example, an RMIS can help businesses pinpoint liabilities such as outdated systems, navigate complicated insurance policies, and simplify risk-tracking processes. 

The right risk management information system solution can transform your data processes by finding hidden insights in your risk data and predicting outcomes. Once an RMIS is integrated into your risk management strategy, it can be used to monitor company risks continually.

 

Expand Data Collection

Now that a foundation has been established to pinpoint risks, you will need to expand your data collection efforts to build a more comprehensive risk management database. You will want to pull data from various sources to get a bigger and better data pool. Sources you may draw data from include customer feedback, social media, marketing analysis, user behavior data, and government-generated data such as a census.

 

Measure Strategically

Some data may not be of use to your company, while other sources may be vital to pinpointing errors in business operations. It will be up to a risk manager to determine which analytics will best identify the specific risks involved in the nature of their business.

 

Share Your Findings

The data you do choose to use for your risk management assessment should be shared with everyone involved in making solutions for the future. This will involve managers, employees, and stakeholders. Additionally, all data will need to be easily read and understood for everyone engaged in business operations. Advanced analytics is not easily understandable by most — however, there are ways to make messy, unsystematic data more discernible to put into actionable solutions.

It is easy to understand how data can upgrade your risk management assessment, but knowing what data to use can be complicated. However, once you understand what data to start collecting and building upon, your company can thrive knowing that risks will be identified and solutions can be carried out promptly.

 

Next steps

If you would like to learn more about this topic please contact David Thomas.

Jul 27, 2021

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