We had the opportunity to sit down with the Founder and CEO  of https://www.deliverylayer.com/ Solomon Kahn to figure out where data sharing is going and why you should never underestimate the length of your sales cycle.

Background

Can you tell us about your background and what led you to start Delivery Layer?

I've had a long career in data leadership, starting in finance and moving through tech companies and consulting. I spent time at Nielsen leading product and analytics for their sports business. After seeing the challenges in data delivery across various roles, I launched Delivery Layer, a customer-facing data platform for companies that need to share data externally.

How do DaaS companies differ from broader SaaS or other industries?

In DaaS, your job is to squeeze as much value from your data asset as possible. Often, the same data can be packaged into different products for different markets. This requires more flexibility than typical SaaS businesses. For example, in sports sponsorship data, the same dataset could be valuable to teams, leagues, brands, and government agencies, each needing a different view of the data.

Solving the Problem:

What space does Delivery Layer operate in, and how do you differentiate yourselves?

Delivery Layer focuses on the underserved use case of getting data outside your company. Most data tools are built for internal decision-making, but when data needs to leave your company, the ecosystem falls apart. We're an alternative to both low-end solutions where companies hack together a solution and high-end custom development, offering a middle ground that's both powerful and efficient.

What challenges do businesses face when trying to share and monetize data?

Traditional market research businesses struggle to move from PowerPoint-style delivery to more modern, automated digital channels. Alternative data companies, which started by serving hedge funds, face challenges in scaling to serve corporate clients who need more processed and accessible data. Both types of companies need to adapt their delivery methods to meet evolving customer needs.

How do you view the role of data marketplaces?

I see data as sold more than bought. The challenge isn't people searching for data; it's data companies identifying potential beneficiaries and making a clear value proposition. Many companies still struggle to use their internal data effectively, let alone integrate external data.

Can you share a success story of how Delivery Layer has improved a company's data sharing?

We worked with a healthcare company called Prospecta, helping them create a customer-facing portal to show insurance companies the impact of their work on provider data accuracy. We quickly built a solution that combines BI tools, a website builder, and an API platform with robust permissions, allowing each client to securely access only their own data.

On Failure

If you were starting Delivery Layer again today, what would you do differently? Is there anything that’s been either an unexpected success or a spectacular failure?

I initially assumed that Delivery Layer's ability to create data products in six weeks, instead of the typical 2-3 years in large enterprises, would allow us to bypass lengthy sales cycles and budget approvals. However, I painfully discovered that even with this advantage, enterprise decision-making processes and budget constraints remained significant obstacles. This was my biggest false assumption when starting Delivery Layer, and while I wouldn't have done anything differently, I wish I had been mentally prepared for this challenge from the outset.

What trends do you foresee in the data sharing economy in the next 5 to 10 years?

I expect linear growth in data sharing, with potential exponential growth in AI-specific datasets. Customers increasingly want access to data, and people are recognizing the value of their datasets. We'll likely see many new data products emerge, as it's easier than ever for small teams to build and sell data products.

AI and machine learning will lead to more sharing and selling of datasets, particularly in bulk delivery and direct database connections. The market for datasets powering domain-specific AI models is an area to watch.