Risk managers cite a number of challenges in making use of data. We partnered with Airmic and Aon to research and address the challenges faced when managing data analytics in risk management.
The amount of data that has permeated the risk management space is daunting, but there is a world of opportunity to be gleaned by the effective use of data analytics. Certainly with the sheer volume, much of this data could be and in fact is superfluous, so analytics is the best way to sort through the noise to find the relevant data. With the right data, businesses can resolve problems and successfully manage risk. The benefits of utilizing data seem straightforward enough, but there are a number of barriers that stand in the way of using said data to the fullest extent.
Let’s take a look at some of the barriers to using data.
The number one reason that risk managers are not properly utilizing analytics is because they simply cannot access the data they need (Airmic  Driving the data dividend. Making use ofanalytics in risk management. Pg. 9). The data that may be provided could be limited, incomplete, or censored by other departments. Or data could have been acquired for an entirely different reason and it’s up to the risk manager to repurpose the data for their needs – sorting and labeling the information from various sources to have a singular database to refer to. Having a common source of information helps interdepartmental communication, hopefully reducing the resistance they receive in the future.
Many risk managers don’t think very highly of the quality of data they possess. Many things have an effect on the quality: the data producer, the person or software that enters and generates the data, and who will utilize the data. High quality data will be precise, expedient, and relevant for the situation it is required for. (Airmic, Pg. 9) This is difficult to achieve when risk managers are using the data after it has been collected so it’s necessary they ensure the data is verifiable and unbiased - sometimes quite the difficult task indeed.
Many businesses lack the resources to fully invest in a full-scale analytics approach. It isn’t clear why funds aren’t being appropriated, but one theory is that rather than spending money amassing potentially useless data, budget is better spent on gathering data to address an individual issue (Airmic, Pg. 8). Investing in technology can help solve this particular problem; our RiskConsole Advance software could help budget managers understand that investing in a product that can help you use data properly will absolutely provide a return on the investment.
Another resource issue is an organization’s infrastructure. A poll we conducted revealed alarmingly that 60% of risk managers still use spreadsheets while a mere 10% rely on a fully integrated data management system. (Airmic, Pg. 8) This is an issue because spreadsheets are easily fallible with lots of potential for human error. Data management systems offer you the ability to pull whatever piece of data you are looking for with ease. They can also handle the volume, variety, velocity, and variety that spreadsheets just aren’t equipped to.
Another problem facing risk managers is that they don’t feel properly supported by their stakeholders and that data analytics is not the answer to increased analytical sophistication (Airmic, Pg. 9). This supposition might be a byproduct of data governance and processing being owned by IT. Risk managers should bridge the gap between IT and the rest of the business so that data can be collected in accordance with business needs and incorporated into procedures that help draw evidenced based conclusions. It’s necessary to work together to present the data in a way that is both informative and aesthetically pleasing so stakeholders engage with the data and understand its importance. Not only that, but organizing the data in an accessible way ensures compliance with strict data privacy laws.
Our research found a number of case studies where companies are successfully gathering quality data with purpose, and putting it to good business use. For example, a financial services firm addressed its high volume and cost of motor using telematics (Airmic, Pg. 9). This gathered information on driver performance which was shared across the business. Data fed back into driver awareness guidelines and policies, cost benefit analysis and to monitor risk culture. This example shows the importance of having buy in across the business, having a strategy and taking a holistic view across the organization in order to solve an issue.
A comprehensive data management and analytics suite will help address the roadblocks standing in your way of fully harnessing the power of data. Perhaps most central to the solution is for companies to approach data holistically as opposed to an answer to an individual issue on a case-by-case basis. To learn more about the products and services Ventiv offers that could help your business use data more effectively, get in touch with us today.