1. Align Your Data Strategy to Business Goals
For starters, financial organizations should design their data strategies with their top business goals in mind. Once data management objectives are clearly defined, IT leaders can more easily work backward from that point to select the right technologies.
“There are many areas in financial services that will drive technology priorities,” says Jerry Silva, program vice president at IDC. “However, the emergence of generative artificial intelligence has had an exponential effect on investments in almost every area of technology investment, potentially affecting other areas of financial services, sometimes negatively.”
To ensure that new tech investments reap the maximum benefits, IT leaders should focus less on AI trends and more on people, process and policy.
DISCOVER: How artificial intelligence helps financial organizations manage their risk.
Washburn says that this begins with asking what the business needs: “What are the value drivers? Where are we trying to go with it? How is the data being handled, and what is the policy around it?”
Taking time to answer these questions is a crucial step toward creating an effective data strategy that can support GenAI use cases.
2. Make Data Governance Central to Your Strategy
It’s imperative that IT leaders incorporate data governance and privacy measures into their data strategies.
“If you don’t have data governance, you don’t really have a modern data ecosystem,” Washburn says. “You have a really shiny penny that does some cool things. But there’s no guarantee that you can trust the data that’s in the system. And without trusting the data, it doesn’t matter how much you spent; you really don’t have a great system.”
Without data governance, IT leaders can struggle to comply with regulatory requirements and security frameworks. According to the Future Business Journal, data governance impacts businesses’ processes and ethical compliance the most.
Data governance also makes it possible for organizations to trust the legitimacy of their data. The “modern data ecosystem” gives users that sense of trust and privacy, particularly important in the financial landscape, Washburn says.
With data validation in the mix, there is a significantly lower chance of error. This makes any GenAI outputs you’re using more reliable.
RELATED: How one hedge fund is reinventing itself with the right digital strategy.
3. Build a Scalable AI Infrastructure
Once IT leaders have a fundamental data strategy in place, they need to make sure it’s built to last. Data literacy helps employees incorporate new GenAI use cases across workflows. It also helps IT leaders make more informed decisions on an ongoing basis. The more employees operate from a data-centric mindset, the more adaptable and refined a data strategy will be.
“To get a data culture, you need really good governance and a really good modern-day ecosystem,” Washburn says.
Ultimately, a well-structured, scalable AI infrastructure promotes data accessibility and integration across platforms, which is essential for driving innovation in the age of GenAI.
