Demystifying the complexity in Financial Data

Mark Hookey, CEO of Demyst, has been on a 15-year journey to modernize  how financial institutions leverage external data. Demyst's core mission is to reduce friction in data access and mobilization for banks, insurers, and fintechs, serving as a crucial bridge between the vast world of external data and the specific needs of financial institutions. He sat down with World of DaaS to talk about what’s gone right and wrong in the evolution of data in the financial space and what to expect. 

The Financial Services Data Conundrum

Financial institutions are often perceived as technological pioneers, yet the reality of data utilization in the industry is more nuanced. "It's shocking how little [data] actually gets used inside financial services," Hookey notes. This observation isn't a criticism, but rather highlights an opportunity for growth and innovation.

The challenge isn't a lack of data supply. Instead, it's the friction in data access and mobilization that presents hurdles. Hookey explains, "If there's a glut of supply, if data exhaust is everywhere, why is so little of it used?" 

This question forms the foundation of Demyst's mission: reducing pain points in data access and mobilization without becoming a data owner themselves.

Hookey elaborates on the complexity of the data landscape: "It's this never-ending tapestry of every combination of customer touchpoint, data source, business application, division of the bank." This complexity creates a fundamental market challenge, as the gap between what buyers and sellers understand about data's value continues to widen.

Why So Little Data Gets Used

One of the most significant challenges in the data industry is understanding the true value of data assets. The fundamental issue lies in the asymmetry of information. As Hookey puts it, "You need to have symmetric information about the value of a good in order to trade." In the data world, the gap between what buyers and sellers understand about a data asset's worth is actually widening, not narrowing.

This complexity creates a market challenge where enterprises struggle to determine the precise value of external data. Hookey notes that water flows "downhill" - meaning enterprises will always consume the data set that is "cheapest, best, easiest, most pragmatic, most impactful." Any data that requires significant work to structure or validate will inherently be less attractive.

The tipping point, according to Hookey, may come when external data becomes "cheaper, better, and more compliant and easier to access than internal data." Until then, most enterprises haven't even fully utilized the structured data readily available to them, making complex data integration a lower priority thus not taking advantage of the data readily available. 

Build compelling tech, but don’t neglect service

Reflecting on his journey, Hookey acknowledges that one of his biggest challenges was not focusing on a specific niche early on. The temptation to solve diverse problems for different customers led to less repeatability and slower growth than might have been possible with a more targeted approach.

"Had we gone the other way and said, 'Look, all we're doing is fraud for Malaysian banks and that's it,' ... I think we would've grown faster," Hookey admits. 

Contrary to popular startup wisdom, Demyst has embraced a services-heavy approach. A product with solid tech is key, but too many in the space view tech and service as mutually exclusive. Hookey argues that obsession over service is crucial for enterprise success, pointing out that even Salesforce had a high level of services pre-IPO. This customer-centric approach has been key to Demyst's ability to work with tier-one financial institutions from the start.

"Services means people are paying for someone's time to help build a custom solution and solve a business problem that really does matter." Hokey criticizes the anti-services dogma prevalent in certain tech circles, arguing that it can be damaging to customer relationships and problem-solving.

Is data a core competency? 

As the industry continues to evolve, Demyst's journey offers valuable lessons for other data-focused startups. The importance of customer-centric services, the need for focus in product development, and the patience required to navigate complex enterprise sales cycles are all crucial elements of success in this space.

Despite slower than anticipated adoption of data-centric operating principles, Hookey remains optimistic about the industry's future. "The industry of mobilizing data and AI to solve high value problems in society and business is just getting started," he affirms. 

Organizations need to be realistic about whether data management is truly one of their core competencies. As Hookey suggests, while every customer-facing enterprise is excited about data and AI, they often resist outsourcing these capabilities due to a belief that it must be a core competency. The reality is that specialized data service providers may be better positioned to evaluate, consume, and leverage data effectively when compared against internal initiatives.

As regulatory complexity increases, particularly around data privacy and compliance, external data providers may gain an additional advantage. As Hookey suggests, the increasing sophistication of compliance requirements could make external data sources more attractive than maintaining internal data warehouses, particularly when considering the comprehensive compliance frameworks that major data providers can offer.

The goal is to shift from a centralized, opaque data management model to a more dynamic, federated approach where data becomes a strategic, fluid asset.

By treating data as an asset and investing in teams that can navigate the complex data ecosystem, enterprises can transform information from a static resource into a powerful strategic advantage.

This shift in mindset could lead to what Hookey calls a "strategic Valhalla" where specialized units focused on data consumption capture a disproportionate share of value in the industry value chain.