Data-Driven Versus Model-Driven Decision-Making

A version of this post originally appeared on MortgageOrb.

It’s safe to say that nearly every business today is using data to help make decisions.

In this digital age, organizations have access to an abundance of data points that they can put to use. The question is no longer “Should we use it?” but “How do we use it effectively?”

Being data-driven is important, but it’s only the first step in a company’s data journey. In a recent interview with MortgageOrb, Vinny Souza, Head of Data Science at Enact, discusses the next destination on this digital journey: taking the leap from data-driven to model-driven decision-making.

What’s the difference between data-driven and model-driven?

It may sound like I’m talking about the same thing. Although they are similar, models take it a step further.

On this journey, think of data as a paper map. It shows you the roads, highway distances, and more, and, in theory, has all the information you need to get from point A to point B. However, much like we did before the internet, you have to plan your route based on what you can see on the map. With whatever the map tells you, you can then make a data-driven decision about the best route to take.

Now, think of data models like a GPS – more specifically, the navigational app on your smartphone. The only data you need is a destination, and often the app will know your starting point already. From there, it will plan your route and even give you options based on where the traffic and tolls are, along with the fastest routes available. It can even provide you with choices based on directness, fuel efficiency, or where you’d like to stop along the way.

That’s the key difference between leveraging data and leveraging data models. We make data-driven decisions when we leverage reports, visualizations, interactive dashboards, and descriptive analytics to extract information and understand the current scenario. These tools give us the “what,” rather than the “why.”

Data models use all the information an organization has to derive actionable insights. Data models go from descriptive analytics to prescriptive, meaning they can explain why something happened, predict what might happen next, and prescribe the best course of action based on what it knows. Ultimately, data models are a “decision optimization engine,” providing organizations with the best possible course of action based on its knowledge and the way it is programmed.

Why make the change?

Data can do everything from helping efficiently retain clients to increasing employee engagement, expanding sales coverage, and maximizing profitability simply through better decision-making.

Rather than spending all their time merely making sense of the data, companies should increasingly rely on artificial intelligence (AI) and machine learning to do the time-consuming and repetitive work of data analysis.

Machine learning models work by training with a diverse array of data points that teach them to make predictions and recommendations. These models help inform leaders on the best course of action so they can make better-informed decisions quicker and spend that time focusing on more complex problems. That way they’re not spending all their time observing trends and reacting to elementary insights.

AI and machine learning are important for any industry, but especially for finance functions like mortgage lending. Data models can identify which market segments to prioritize, show lenders how to maximize the efficiency of operations, help them minimize delinquencies, prescribe the most effective portfolio mix, and more.

It might feel harder to trust a model than it is to solely trust the data. However, the benefits are significant.

What are the benefits of being model-driven?

The first and most obvious gain is better decision-making. AI and machine learning can look at more data than any person or team could handle at one time. Its resulting decisions consider what’s going on in the business and the market, while also considering past situations and multiple scenario simulations. Data models create an incredibly well-rounded view of an organization’s situation, and, in turn, help inform the best decisions that consider all factors affecting the business.

Model-driven organizations also can expect efficiency gains. Since machine learning models are automated and process vast amounts of data at once, they free up staff to do more complex work that actually requires human attention. Eliminating repetitive tasks and simple operational and process decisions gives staff more time to spend problem-solving, innovating, and supporting customers.

While AI can carry a negative connotation for fear of job loss, these models are not meant to cut the worker out, but to free them up. AI helps people work smarter, not harder.

Leveraging data models in decision-making is a critical step in any digital transformation. Keep in mind, though, digital transformation is a journey. It is not one event, but rather a long process where every business will find ways to use models differently. Regardless of how businesses integrate their models, leaders and decision-makers will find that model-driven insights help propel their business forward.

Source: Vinny Souza, Head of Data Science at Enact Mortgage Insurance.

The statements in this article are solely the opinions of Vinny Souza. They do not necessarily reflect the views of Enact or its management.

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