Intro

We recently sat down with the Co-Founder and General Manager of the data discovery, monetization platform Revelate.co Marc-André Hétu to discuss data liquidity, building a DaaS company and how to take advantage of an evolving world where almost every company is in one way or another a data company. 

Background

Before we dive into the details, could you please tell us a little about your background and what led you to start Revelate?

My background is in software engineering, mostly in the capital markets vertical. My co-founder Francis had access to a 360 terabyte trove of data from exchanges around the world, and we wanted to create a big data company to manage and sell that data. However, we found that hedge funds were not interested in buying 10 terabytes of data, as they struggled to manage even 1 terabyte. So we pivoted to focus on solutions rather than just selling the data, and ultimately decided to create a product out of one of our solutions - the data marketplace.

Given your background, you initially targeted the financial services industry. Did you find that to be a challenging market to crack?

Yes, absolutely. The financial institutions are very entrenched in using traditional data distribution channels like Bloomberg and Refinitiv. It's not an easy market to penetrate. So we decided to explore other verticals where the maturity around data monetization is lower, and we feel the product can have more impact. 

Who do you come up against in this space, and how does Revelate differentiate itself from them? 

Well, there's a lot of build versus buy competition that we still come up against. Others vendors have platforms that allow clients to create their own data boutique marketplaces. In terms of differentiation, I think the key is that our platform is focused on enabling the easy creation and management of data marketplaces, rather than just being a marketplace itself. Unlike other vendors, Revelate never takes ownership of the data which is appealing to our customers for obvious security reasons.

How do you view build vs buy?

It’s dying with the knowledge that platforms like ours exist. Prior to that - we had a lot of internal heroes building their own systems. Our latest signature is a client who is actually using us to replace a home built solution. The era of the home built hero is fading because they may be able to build a solution but do not consider the cost of maintenance in the long term. 

Solving the Problem

What are the main challenges businesses face when trying to monetize their data assets?

The first and foremost challenge is data maturity. The data maturity of a firm will dictate its success in monetizing or even sharing data externally. Things like data quality, governance, and having the right processes in place are crucial. If a company doesn’t have a data product manager, somebody who takes care of the commercials, they will not be a successful data vendor.  Beyond that, discovery is a big challenge - it's not as simple as just putting a data product on a shelf and expecting it to sell. You need to do marketing and ensure the data answers a real need.

How do you help facilitate data discovery and relevance through your platform?

We have a lot of tools to help describe data products, like our Advanced Data Product (ADP) pages. These allow data providers to build out detailed product pages with things like samples, definitions, and the key questions the data answers. This makes the data more discoverable and marketable.

You are one of the few companies out there that provides a cross listing service on multiple data marketplaces (Snowflake, AWS, Databricks etc) Two questions here:

Has this been a successful part of the business? 

Every client is talking about the operational nightmare that listing on multiple marketplaces poses. 

What do you think of the data marketplaces themselves - where do they succeed and where do they fall short?

I see those marketplaces as more like a mall - you can find everything, but not necessarily what you're looking for. The discovery experience is lacking, as it's more of a list of data providers rather than a way to search by topic or question. The procurement process is also extremely challenging on those platforms, both for the data provider and the consumer.

If you could design the ideal data sharing experience from start to finish, what would that look like?

In an ideal world, our data marketplace platform would allow data providers to easily list and manage their data products, without having to give up ownership or control of the data. The data could remain in the provider's own storage, with our platform handling the scanning, organization, and delivery to consumers. And on the consumer side, they could access the data in whatever format or location works best for them - whether that's downloading it, having it delivered to an S3 bucket, or querying it in their Snowflake. 

Looking Forward

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

We are moving to a self-serve model like everything else. Right now we are at the beginning and our goal is to democratize data sharing and data monetization. Anyone who wants to put forth the effort will be able to build their own data marketplace and run it. 

Just like Shopify was a revolution for the sale of physical things, we will be creating the same thing for data sharing. 

On the topic of AI, there’s been a lot of talk about data scarcity limiting the rate of development for LLMs. Do you think it’s overblown or do you expect companies with data exhaust assets trying to finally monetize their data to meet his demand?

People with data will figure out that AI needs the data, but as LLMs evolve they will require less raw data. If you want to check out a company called Solid State of Mind - they are training models with 10000x less data and having better outcomes, so I think that concerns of data running out are a bit overblown. They become more human-like and can do more with less both from a data standpoint and from an energy consumption standpoint. 

On Failure

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

Product market fit - learn your market beyond a single vertical to understand where your product will fit. That said, I am very proud of what we have built having been at the forefront of building out data sharing technologies. 

Closing Remarks

What are you working on next and what is the best way to keep updated about the latest developments? 

Cross Listing is a pet peeve of mine and I want to tackle that further and a completely source/destination agnostic world with ability to source from and deliver data everywhere.