December 29, 2017
When it comes to enterprise risk management, there is no greater tool at our disposal than data science. Many companies are starting to come to the realization that data is perhaps their most valuable commodity because it can be used for more than just spotting key trends and identifying markets; it can also highlight ways to reduce risk and even increase gains, by helping predict future outcomes.
However, don’t be fooled into thinking data science is only the process of amassing data. More importantly, it involves purposely mining data to find key insights and specific examples that can help companies protect themselves in the decision-making process, ensuring high-quality decision-making. Knowledge, or in this case data, is power.
What is data science and how does it help enterprise risk management?
In the 1950s and 1960s, when enterprise risk management began to be practiced, decision makers didn’t always have data to back up their choices and, when confronted with a risk, they trusted their gut or relied on experience based on a set of principles and guidelines.
However, since then, two things have changed. Firstly, we now live in a world where finance has been significantly deregulated. Although this has led to increased possibilities for the creation of money on a large scale, it has also made the global economy less predictable. Secondly, we now have the technology at our disposal to not only estimate the outcome of a situation with a high degree of accuracy but also use that data to plan the next steps to take with a variety of options to use in addition to spotting possible future problems well before they become an issue.
Put simply, data science includes the following:
- Validating and testing data to ensure information is correct.
- Testing implemented changes.
- Creating algorithms to collect data in specific areas.
- Data mining, which is the process of scouring databases to create new information.
- Explaining complicated findings to employers and stakeholders.
Know when to say no and when to say yes
Using data science to aid enterprise risk management not only provides valuable data, it also helps you make crucial decisions. By performing the right checks, companies will be able to produce high-quality estimates on what could possibly happen, for example, if they dropped an important client or changed a supplier, allowing companies to feel more confident in their decisions.
Turn potential losses into a win
Inevitably, disaster will strike businesses, but with the help of data science consultants, these disasters can potentially be reduced, stopped or even guided to success.
Use your data to strategize
Data can be used to create a method/routine and, in turn, build company policy. There are five steps to enterprise risk management and all of them work with the aid of data science.
- Risk identification – use the data to find the risk.
- Risk analysis – analyze the risk and how it is caused.
- Risk response – use the data to strategize how to eliminate the risk
- Risk control – implement the changes.
- Risk monitoring – monitor the effects of the new strategy.
Put simply, once you have discovered the causes of risk and prevent them from getting worse, you can ensure it doesn’t happen again by developing a strategy to look out for the signs of this problem in the future.
Develop a clearer image of how well your business is doing
Through the collection of data, analysis and the creation of insights, companies are able to understand how well they are doing in a much clearer way which not only allows them to isolate risks but understand the value of their company in a better way.
One of the best ways to do this is to apply data science to analyze data to be prepared for auditors. This way companies can find issues before audits take place and fix them before they turn into a bigger problem.
Back up your arguments with high-quality data
There will be times when executives or employees are certain there is a risk right in front of them but just can’t prove it. Data science allows them to dive deep inside this data and find what they are looking for. If they are correct, they will then have the evidence they need to warn their colleagues and action change.
On top of this, you will be able to identify potential risks you weren’t even aware of and unmask business practices you didn’t realize were secretly destroying your business, for example, bad or even illegal sales practices.
The more data you have the more valuable it becomes
Every day, the amount of data a company possesses gets larger and larger, and within this data are many different possible scenarios allowing more complicated questions to be asked.
Dunkin’ Donuts is the perfect example of this. As the company’s profits were starting to slump year upon year, it decided to launch a loyalty card to collect data on what customers are spending their money on. This turned out to be a genius idea because it allowed Dunkin’ Donuts to use this data on specific customers to reach them with deals that appealed to them, saving Dunkin’ Donuts from disappearing.
Data science is the tool that allows enterprise risk management to function in the 21st century. As it becomes a more effective tool, we are likely to see it’s use more frequently in the office and play a more integral part in how we make decisions and analyze the risks associated with them. In fact, it may even replace the decision-makers themselves.
And the benefits don’t stop in just enterprise, they can also be found in a number of industries, from transport to healthcare, and anywhere else where data can be created or mined. Wherever risk can be minimized, there is always data science to help – not only to collect information, but also to direct decision-making.
Emilia Marius is a senior business analyst with eight-plus years of experience. She focuses on IT solutions for retail and eCommerce, has applied her skills to such projects as a sales analysis system for a retail company, a mobile payment solution for an e-shop and more.