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How to use analytics for risk management

Calculating and managing risk is a vital part of business, but how can data analytics play into successful management? Read on to find out what risk management analytics is, why it’s important and how to use it to improve your business.

What is risk management analytics?

Risk management analytics (or rather, using analytics for risk analytics) is the technology-empowered, data-driven approach to managing potential issues or opportunities within a business. These analytics help identify, measure, and predict risk based on vast amounts of data, enabling more accurate insights into where risk can be better managed.

Risk management has traditionally been undertaken by senior management figures within organizations, but by relying on humans only for risk monitoring, your business can potentially miss important flags hidden within your data. Risk analytics uses the power of big data, artificial intelligence, machine learning, and more to deep dive into your business, identifying trends and weaknesses and providing comprehensive insights into their resolution.

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Why is data analytics important in risk management?

Using data analytics is imperative to predict, manage and avoid risk in the modern age. Here are the key reasons why risk analytics are a vital part of managing your risk:

It makes you adaptable, and therefore competitive

The market of today’s world is constantly moving, at speeds that can be difficult to keep up with. To consistently meet market and consumer expectations, you’ll need to be able to glean large quantities of information and process it to find the insights that will give you a competitive advantage. Deloitte has found that 55% of businesses think that data analysis improves their competitiveness, and 96% agree that risk analysis will be more important in future. Thankfully, risk management technology can evolve with the times, and provide you with a strategic edge when it comes to managing potential risk.

It matches your capabilities to your needs

New risks are constantly coming to the fore. Without the ability not only to identify, but provide suggestions to resolve these risks, your risk management strategy – and business – will be weaker. Including risk management capabilities across your entire business operations gives you oversight that simply can’t compare with a human-led approach. Advanced analytics can process vast volumes of unstructured data at speed and flag risks that you aren’t even looking for, in near real-time.

It can identify and predict trends to minimize costs without sacrificing service

Ideally, risk management involves a strong element of prediction. What will be the risks that are most likely to affect your organization? With risk analytics, you’re able to see the red flags and identify broader trends. Issues that could be dismissed as one-offs form a clearer pattern when all your data is evaluated on one platform. This will give you a clear road to get ahead and solve problems before it’s too late and.

It is objective

Rather than relying on subjective opinion as to the potential risks your organization faces, you’re able to take a more objective view by using risk analytics. Risk owners might miss key trends because they don’t have enough oversight, have their own goals to meet or overly rely on intuition to guide strategy. By using risk analytics, you’re able to assess the situation rationally, with all the data at hand to see the true risk at play.

It helps you monitor performance, and mitigate internal risk

By analyzing all the data your company produces, you’re able to monitor performance across your different business units, giving you insight into where risk might be better managed. Rather than each team managing risk in a silo, the interdependent risk can be seen comprehensively with a more global risk management approach. Whether it’s underperforming teams or unfit-for-purpose solutions, great risk management analytics can root out problems and give you ideas on how to resolve the risk.

How is analytics used in risk management?

Risk management can essentially be broken down into different stages: identification, risk assessment, mitigation/response, and finally monitoring and reporting. Risk analytics can help you with all of these stages.

Risk management flow chart


Key risk indicators are usually found in two areas: internal, and external. Internal risks - such as inefficient business processes, capital flow, operating costs etc. - are often highly complex, with many interdependencies. External risks - such as macroeconomic fluctuations, political changes, regulatory requirements etc. - require an awareness of constantly-changing information.

In this day and age, identifying risk across these two sides is an enormous task when all the available data is not taken into account. Analyzing internal and external data points together using risk analytics allows you to spot, monitor, and take action on risks no matter where they occur, without burdening human risk managers with the data analysis of huge volumes of disparate information.

Risk assessment

Effective risk assessment and prioritization is key for successful risk management. Analyzing issues within internal operations while considering external risk factors requires sophistication and access to a framework of priorities that informs your business’s next actions. Risk analytics can create complex risk profiles and models that will help to complete more accurate risk assessment, determining the likelihood of occurrence and the impact a risk might have by using the comprehensive bank of data at its disposal.


Responding to risk requires data-driven insights that are action-oriented, with all the potential impacting factors considered. Risk analytics can help to answer those “what if?” scenarios and suggest optimal responses based on the available information.

Monitoring and Reporting

Risk analytics can help to monitor the impact of the actions you take, tracking progress over time. Rather than taking action and hoping for effective change, risk analytics can pinpoint the movement of data in relation to your risk response, and identify if the strategy is effective and timely. It can also be used to create a steady workflow, enabling your teams to continually address issues quickly and productively.

Reporting is a key part of risk management, allowing your team to reflect on the progress made and potential risks identified for the future. Effective risk analytics solutions will have comprehensive reporting facilities to allow organizations to make a thorough risk plan.

Types of risk management analytics can be used for

In risk management, there are generally five types of approaches to risk management:

  • Avoidance: Rather than be involved in a risky activity or environment, your organization elects to steer clear. Risk analytics can identify key external trends and help you to avoid problems before your business gets entangled.
  • Retention: Some risk is par for the course, but deciding which risk is acceptable is where risk analytics shines. Evaluate vast amounts of information for accurate risk prioritization and make effective decisions on which risk is worth it.
  • Sharing: Instead of risk being focused on one receiving party, risk can be shared across the business. Understanding the impact of each risk on all aspects of your business and modeling how it can be shared is part of why risk analytics is worth the investment.
  • Transferring: Which risks need to be transferred to an external party to handle? Risk analytics allows you to see the impact of each risk on your organization, helping you to decide which is an unfortunate part of the operation, but not a necessary issue for your own business to weather alone.
  • Loss prevention and reduction: Minimizing risk is the focal point of risk analytics. Preventing repetitive losses and reducing risk becomes more streamlined with data analysis that can make predictions and target your response more effectively.

Common risks requiring management

The types of risk you might need to manage include:

  • Identifying and tracking product-related or service-related risk. This could include safety issues, warranty coverage and more. Without addressing these issues, your customers are likely to complain - if not to you, on public forums where your brand image is put at risk.
  • Mitigating financial risk. Often, businesses have financial requirements that can incur large fines if not met - risk management analytics can help to keep costs from escalating and requirements from being missed.
  • Depending on your business sector, you may have specific legal liabilities that need to be addressed. Monitoring your internal activity ensures that you can identify lapses, track resolutions and mitigate risk.

How does risk analytics help to identify key risk indicators?

Key risk indicators are the main measure of the likelihood of a risk occurring and having an impact. A deep analysis and understanding of your business, its risk appetite and its goals is necessary to pinpoint risk indicators particular to your organization.

Risk management analytics relies on a thorough, data-led understanding of your business to make predictions and glean insights. Indicators can be surfaced more easily when analyzing large quantities of relevant data from both inside and outside your organization. Whether your risk indicators come from your people, process systems, technological solutions, financials or more, you’re able to identify risk factors, rank potential risks for priority, and plan appropriately with risk analytics that use deep business intelligence.

Guide to implementing your risk analytics solution

Each organization will likely need a different approach to implementing their risk analytics solution, but this general guide will allow you to make your data work harder for you.

1. Gather and classify your data

As mentioned, your data will likely come from two sources, internal and external. Ideally, you should collect all data relevant to your business no matter where it resides - for example, on social media or internal communications channels. It’s recommended that you:

  • Ensure that your data can be accessed by your risk management solution. Information can often be siloed or encrypted - ensure your program can see the data it needs.
  • Take into account the why, as well as the what. Emotion, effort and intent are also important factors to consider when collecting data on customers, for example, as it can inform you as to why a particular information point is occurring.
  • Get data in real-time. For the most accurate and timely responses, you’ll need your business intelligence to be gathered and analyzed in as close to real-time as possible.
  • Classify your data. What’s important when considered alongside your business goals?

2. Understand goals and requirements

Your business goals will have a large impact on the prioritization of your risks. You’ll need to:

  • Align your executives on what the main risks are. Determine your key risk indicators in accordance with your company’s goals and likely main areas of disturbance.
  • Ensure you take into account each department’s risk. Some risks will be particular to a particular department, or even team - make sure you’re not missing the small risks that can turn into bigger ones.
  • Take into account legal liabilities and regulatory risks. External issues sometimes can’t be predicted, but compliance is a big area of potential risk. Make sure you’re up to speed on the things you absolutely have to consider because often this will dovetail with your key risks.

3. Develop your risk library

Though there are likely to be risks you won’t be able to anticipate until your data has been analyzed via machine learning and more, you should create an initial risk library that can be updated over time. This library, your risk plan, and your risk analytics will work together seamlessly to create a continual workflow of risk assessment, prediction, mitigation and reporting.

4. Create your risk assessment matrix

This matrix will allow you to prioritize your risks, depending on how likely they are to occur, the impact they’d have, and how bad the fallout will be. Taking your indicators into account, you’ll be able to keep your risk register updated and relevant. Not only that, but you’ll be able to compare risks and determine which are the highest priority. Your risk analytics tool should be able to help you develop this using AI and machine learning.

5. Use both qualitative and quantitative analysis

As mentioned, it’s important to understand not only what is happening (and in what frequency), but the qualitative reasons why risk is occurring. Your data analysis will likely spot the immediate quantitative trends, but the qualitative inputs - staff surveys, customer feedback etc.  - will also be very informative.

6. Create visualizations of your risks for easy comprehension

Your risk management will very likely depend on all members of your team understanding what the risks are and how to manage them. Data visualization can be a powerful tool for helping everyone understand their priorities - and where action needs to be taken.

Empower your risk management with Qualtrics

Uniting machine learning-driven data analytics, conversational analysis, and workflow automation, the Qualtrics Experience Management Platform™ propels your business forward with accurate, timely insights for risk assessment and mitigation. Never miss a data point or potential resolution with real-time analytics that tackle the issues that matter most.

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Qualtrics // Experience Management

Qualtrics, the leader and creator of the experience management category, is a cloud-native software platform that empowers organizations to deliver exceptional experiences and build deep relationships with their customers and employees.

With insights from Qualtrics, organizations can identify and resolve the greatest friction points in their business, retain and engage top talent, and bring the right products and services to market. Nearly 20,000 organizations around the world use Qualtrics’ advanced AI to listen, understand, and take action. Qualtrics uses its vast universe of experience data to form the largest database of human sentiment in the world. Qualtrics is co-headquartered in Provo, Utah and Seattle.

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