“A potential issue with using data science in an organization is solving problems that don’t need to be solved, or solving irrelevant problems, and that’s never the fault of the data scientist.”
Ken Schultz, VP Data Science—Elevate Credit
Above is a quote from a fascinating webinar presented by Ken that explored the use of data science to advance credit risk decisioning. Ken provided many fantastic insights including advice on how to make sure that your financial services organization empowers its data science team to be as effective as possible. So, I wanted to use this week’s blog to expand on one of the points he made—how to make sure your team is solving the right problems.
Solving the right problem requires more than an understanding of the data
One of the most incredible things about data scientists is their ability to create stories from data. Why is this skill so incredible? Because stories offer an amazingly powerful way of communicating complex situations, problems, and solutions. Stories create emotion, they build connection, they challenge the way we think.
Stories evoke a response.
Which means that the data scientists in your organization are in a very powerful position, they have the opportunity to tell you a story using the data and suggest a data-backed business response that the c-suite can get behind. Perfect, right?
Let’s take a step back and imagine that your data scientist tells you a story from the data:
Once upon a time, there was a lonely prince/princess who had been locked in a faraway tower for many years. Our unfortunate prince/princess’ only chance of escape lies in the hands of our hero, who must help the prisoner escape before the sun sets—or they will be locked in the tower forever.
The hero, desperate to rescue the prisoner, must travel many miles to enable the escape, but they only have a couple of hours remaining to get to the tower and free the prisoner.
So, the data scientist looks at the data, sees the story, and develops a response that creates a rapid solution:
Our hero will not be beaten, desperate to secure the fastest transportation available they borrow their friend’s car but fail to reach the tower before the sun sets.
Nobody lives happily ever after.
What happened? How could the data scientist have been wrong???
How to Integrate Data Science into your Risk Strategy
Putting your hero in a Honda when she needed a horse
Your data scientist was right, they found the customer, saw the problem, and they read the story the data was telling them. So, they created a business response to that story, however, they were missing one very important thing:
You see this data scientist had been lost in the numbers. They could tell you the income of the prince/princess, they could even tell you their hair color, where they live, and many other facts. But the problem is that they weren’t involved in business planning, didn’t know how the data fitted into the bigger picture and didn’t know how their recommended solution would be implemented. They lacked the context, which meant that they saw the tower, but they didn’t see the rocky valleys that would need to be crossed to get to the tower. So, they suggested a Honda when what was really needed was a horse.
Context is everything.
Business buy in—the first step in making your data science team better storytellers
While organizations are drowning in data, without context it can’t provide all of the information a data scientist needs to be an effective part of an organization. The data science team can’t be used to its full power if it exists in a bubble separate from other departments within the business.
The first step in taking the data science team out of the bubble starts with the business fully embracing data science, on the webinar Ken was keen to point out. “I think it’s super beneficial for data science in an organization that it’s not seen as a service function. And that you get buy in from the company, and from the executives, that you’re fully integrated into the business.”
This is more than just bringing data science into business processes, it’s making sure that your data science leaders know what’s happening in the business at both the day-to-day level and the long-term plans and goals.
“In my office, behind one wall is the CEO; behind the other wall, is the SVP of risk strategy. I’m personally involved in all of our numbers type meetings. I know where the portfolio’s going, I know where there are issues and risks.”
Providing context for your Data Science team
To answer the right questions and provide the right solutions your data scientists need to understand:
- the business’ goals
- the marketplace it operates in
- the processes that power it
- it’s products
- it’s customers
- the future roadmap
- perhaps, most importantly, the risks these things expose it to
So, with data science fully embraced by the business, how do you ensure that your data science team has a clear understanding of what the business is and where it’s going? To answer this Ken shared how and why his company makes sure that they provide the team with the business education it needs to put the data in context:
“At team meetings every week, we have 30 minutes assigned for a guest speaker from strategy, finance, or marketing to come in and talk about what they’re working on, and what I’ve found is that if you can get your team and your data scientists to really understand the business, and to know where they’re going, to know what other people are working on, they’re way more effective.”
The old idea of a data scientist is someone who sits in a corner, just hacking away at the keyboard is not going to allow the team to be as successful as they can be. Our data scientists sit with our risk analysts, sit with our fraud team. Fostering collaboration is huge and in forcing that collaboration in some sense, people will open up to it and really start to enjoy it. And everyone gets better for it.”
Building solutions for the bigger picture
It was the architect Eliel Saarinen who said, “Always design a thing by considering it in its next larger context – a chair in a room, a room in a house, a house in an environment, an environment in a city plan.”
He understood that to successfully design something you needed to understand how it fits, and what its role is, in the bigger picture. Take, for example, a data scientist tasked with using the data to increase approval rates, with a clear understanding of the business they’ll know the market risks and the data needed to mitigate the risks. But not just that, they’ll know how that data fits into the risk model, that fits into the risk process, that provides a decision on the application, which expands the business lending portfolio, that drives the company towards its goals etc. etc. etc.
With context, your data science team helps drive your risk strategy forward.
Watch the How to integrate data science into your risk strategy webinar recording where I expand on today’s blog post and cover the key factors that directly impact data science success in a financial services organization, including the importance of a strong relationship between data science and risk teams, the technology needed to implement data science in risk decisioning, and why businesses need to fully embrace data science to drive success in their risk strategies. I also share a demo of how the Provenir Platform empowers data science in risk decisioning and eliminates data access challenges.