How Risk Management Ecosystem Is Evolving with Data Analytics

According to IBM “Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.” 

According to IBM “Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.” 

Innovative industries and organizations have already begun to capitalize on this wealth of data. From supply chain to insurance analytics and other big data ecosystems, data analytics is truly transforming the risk management world.


That being said, it is still important to interrogate how specifically the risk management ecosystem is evolving with data analytics. In a nutshell, data analytics influences risk management and its different ecosystems in 4 fundamental ways as illustrated in the diagram below:

 4 types of Data Analytics and the role they play in informing decisions 1:

In essence, data analysis helps the risk management ecosystem, and especially risk teams, to achieve increasingly accurate hindsight, insights and foresight drawn from a variety of data sources (whether structured or unstructured), almost instantaneously.

As Jason Hill, (Executive Partner – Reply) concisely put it,


‘Time is critical in the new world of risk management. If you can react to a risk faster, you have a competitive advantage’

Therefore, through data analytics, individuals in the risk management ecosystem are able to rapidly and effectively sense and respond and/or predict and act faster and better, informed by a vast amount of risk variables.

Benefits of data analytics in risk management ecosystems

1.    Instantaneous intelligence and robust utilization of risk assessment

In today’s world, the variety and complexity of data sources (including email, social media, apps, sensor data and documents) mean that static structures and interaction paths are no longer tenable.

The speed required to retrieve and analyze data has necessitated not only how big data has been utilized and approached in the recent past, but more importantly how it is can be used today.


Take for example risk management in a banking eco-system. if you want to conduct a risk assessment in determining the risk of giving a loan to a new customer, you can easily use data analytics to get an almost instantaneous risk profile based on a wide range of data. The analyzed data can be derived from spending habits, customer credit reports and social media, all within a matter of seconds.

2.    Improved decision-making

Through data analytics, decision making has become more robust and evidence-based across a number of key risk domains including; market risk, credit risk, operational risk, integrated risk management, compliance risk etc.

Additionally, since data analytics allows the synthesis of vast amounts of data, this allows for the development of new ways to work and collaborate. Risk management teams can, therefore interact and collaborate more with other teams within an organization including; Finance, IT and operations teams. This inherently leads to collaborative decisions and therefore better decision-making.

3.    Substantial cost savings

In business, efficiency translates to cost savings. Through data analytics, significant cost savings can be experienced through simply combating risks that can cost businesses lots of money and resources


According to an Accenture’s study, using Big Data analytics in supply chain operations can increase efficiency by 10 percent or greater, reduce order-to-delivery cycle times and improve demand-driven operations. All this is possible through improved data analytics in the supply chain risk management ecosystem.

4.    Improved analytical power and the stabilization of risk models

The quintessential approach of data analytics is to transition from descriptive analytics to predictive and prescriptive analytics. This allows for better hindsight, insight, and foresight. Additionally, data analytics is increasingly producing a better quality of filtered, real-time data which tends to also stabilize risk models.


As you would expect in any domain or industry these days, risk management faces constantly evolving challenges and demands. In order to respond to these demands and challenges, risk managers constantly require not only more data but more detailed data and also increasingly sophisticated reports.


Through data analytics, risk managers are increasingly able to reduce the ‘noise’ inherent in vast volumes of data. This allows for improvements in risk coverage, risk monitoring and in the enhancement of stability and predictive powers in risk models. All this is in an effort to support the Risk Officer’s decision-making.