Earlier this year, Hakkoda’s 2024 State of Data report leveraged a survey of 500 data leaders to identify trends in data strategy and architecture across eight major industries and provide a detailed portrait of where these organizations stand and where they plan to go in their data modernization efforts.
This week, we expanded that portrait with the Financial Services and Insurance (FSI) State of Data report, which drew on responses from 145 data leaders from large financial services and insurance institutions to identify industry-specific themes, trends, and investment opportunities.
In this blog, we’re revealing five of the most interesting and valuable insights we gleaned into the state of data for FSI organizations in 2024, which include both industry-specific barriers to data modernization and the winning strategies that will help FSI leaders overcome them.
1. Data Modernization is an Urgent Need
Results from the Financial Services and Insurance State of Data revealed that data modernization is an urgent need for large FSI organizations. Only 30% of the FSI organizations surveyed had already modernized their data stack to centralize data operations on a primary cloud platform like Snowflake, and just 8% reported that their data stores and pathways are “extremely efficient and scalable.”
Not only are FSI organizations less likely than organizations in other industries to have centralized their data on a primary cloud platform, they’re also less likely to use cloud-based data storage repositories, warehouses, or cloud data lakes for analytics and processing applications. For example, just 37% of FSI data leaders reported deploying a data lake as part of their data stack, compared to 53% of data leaders in the entertainment industry.
With so many large FSI organizations still dependent on legacy platforms, applications, and databases, there’s a huge opportunity for FSIs to increase scalability and lower their analytics costs by shifting to a modern, cloud-based data infrastructure. FSI data leaders recognize this opportunity as well, with 74% telling us their organizations intended to centralize data in the cloud in 2024.
2. FSI Orgs Lag Behind in Key GenAI Use Cases
Generative AI (GenAI) algorithms learn to create new content by training on massive amounts of existing data. Once trained, GenAI tools can be deployed to satisfy a variety of IT and data management use cases, including things like data governance, documentation and metadata generation, data cataloging, schema matching and integration, and AI copilots.
The report also found that FSI organizations are ahead of the curve on some GenAI use cases, but lagging behind in others. For example, 51% of FSI organizations are leveraging GenAI for documentation and metadata generation, compared to an industry average of 47%. However, just 31% of FSIs have deployed AI copilots compared to an industry average of 35%.
There is also a disconnect between FSI leaders’ confidence in their ability to integrate AI and their clarity of vision as to how it should be implemented into their everyday operations. 81% of FSI data leaders reported they were “very” or “extremely confident” that their organization’s data team could build their desired GenAI capabilities, but only 26% reported having defined GenAI use cases that were ready for implementation. A key theme and challenge for many FSI organizations in 2024 will be transforming AI confidence into AI readiness through initiatives around data modernization, quality, and governance.
3. Data Quality and Governance are Major Operational Challenges
FSI organizations are confident about their ability to start leveraging AI, but many don’t currently have the data infrastructure or processes in place to support valuable GenAI use cases. To understand more about the barriers to successful GenAI implementation in the FSI industry, we asked FSI data leaders to identify the major challenges for data management and operations within their organizations.
We had already identified data modernization as an urgent need for the organizations participating in our survey, so it was no surprise when 37% of FSI data leaders told us that modernizing legacy data systems was currently a major challenge.
But most notably, 49% of our respondents said that “Ensuring data quality and governance” was a major challenge for their organization and 41% said the same about “Integrating data across multiple silos”.
Data quality, governance, and interoperability are also vital for implementing GenAI. AI models rely on high-quality, interoperable data to learn and make decisions, and organizations need appropriate governance processes in place to mobilize that data in a secure and compliant way. FSI organizations will need to address data quality, governance, and interoperability challenges to successfully implement GenAI use cases in 2024.
4. Scalable and Efficient Data Infrastructure is Needed to Support Data Monetization
When we asked FSI data leaders about their organization’s upcoming plans for monetizing data, we identified a significant gap between where these companies are today and where they want to be in the future.
Seeing the huge desire for data monetization from our respondents made us wonder: What was preventing these organizations from getting started?
One compelling reason could be a lack of scalable and efficient data infrastructure to support monetization initiatives. Just 8% of FSI data leaders told us their organizations had an “extremely efficient and fully scalable” data stack, while more than 50% reported low-moderate efficiency and scalability challenges.
5. FSI Orgs Need External Support to Achieve their Data Goals
FSI data leaders recognize that their organizations will need help to modernize data infrastructure, address data governance, quality, and interoperability challenges, and implement GenAI and data monetization initiatives.
According to the report, 74% of data leaders indicate that their organizations will need a “moderate” to “large” amount of outside help to modernize their data infrastructure in 2024.
This marks an attitudinal shift within the industry that will serve data leaders well in driving value and efficiency in their data modernization efforts. According to the report, FSI orgs outsourced only 53% of their data management in 2023, which is significantly less than the 63% of Innovation-stage organizations following data best practices. These same Innovation organizations also saw significantly higher returns on their data investments than their less data-mature counterparts.
Forging Your Path to Data Modernization with Hakkōda
To solve data challenges, modernize data infrastructure, and implement initiatives like data modernization and Gen AI, FSI organizations will need external support from Managed Services Providers, Systems Providers, Systems Integrators, and data transformation experts like Hakkoda.
Our Snowflake consultancy teams combine industry experience with certifications across the modern data stack to help Financial Services and Insurance businesses at every stage in their data innovation journey unlock the value of their data to drive new growth opportunities.
Through our robust portfolio of data modernization services, Hakkoda provides FSI organizations with the skills and expertise they need to modernize data infrastructure in the cloud, solve challenges around data governance, monetize data with Snowflake Native Apps, and implement advanced data analytics and GenAI use cases.
Ready to learn more about how top FSI leaders are closing the data monetization gap while driving cutting edge innovation in the space? Unlock additional insights from the Financial Services and Insurance State of Data by reading the full report.
