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TDWI Upside - Where Data Means Business

Executive Q&A: Exploring the Market for Selling Enterprise Data

Monetizing data is emerging as a way for enterprises to profit from the data they collect and store. Upside asked Nick Jordan, founder and CEO of Narrative, about the viability of marketing and selling the information your company collects.

Data is one of your enterprise’s most valuable assets, but can you make even more money by selling it? Nick Jordan, founder and CEO of Narrative, explains what you need to know about marketing and selling the data your company collects and the challenges you’ll face.

For Further Reading:

A Framework for Data Monetization

Data Monetization: A New Way of Thinking

Data Monetization Requires Balancing Data Availability and Governance

Upside: What’s driving enterprises to want to sell their data?

Nick Jordan: There are a number of things. First and foremost, there are companies where data monetization is their primary mode of revenue, so it’s their business. As the information economy gets more robust and companies get creative in this space, they will continue to plow ahead. Second, more companies are looking at their data as an asset and treating it like other assets on their balance sheets, much like empty desks in an office or the fact that they have an office building sitting on valuable land.

As a purely financial decision, if you can monetize data to maximize the value of that asset, it’s a no-brainer decision. Data monetization can be thought of as a direct transaction where you receive money for the data. You can also think of monetization in the context of commercial relationships, where two organizations are sharing data because they aren’t competitors, and the combination of their data creates more value than either on its own.

How viable is selling data as a profit center for an enterprise?

Data is incredibly viable as a profit center. Unlike a physical item (such as a television set), data has the unique attribute that you can sell the same data as many times as you like. Selling data may have lower marginal costs than selling physical goods, but if you are selling your own data, you will have close to 100 percent profit.

I think a lot of businesses are looking for ways to expand their revenue lines. Data can be sold multiple times and tends to be at a high margin, which is attractive from a pure P&L perspective.

What data can an enterprise sell without giving away the store? Will the value of the data to a competitor provide a potential competitive edge that might outweigh the monetary gain for the seller?

For starters, don’t sell your data to a competitor! This is what is wrong with the data broker model where companies can’t control where their data goes. It’s a great question to ask a data broker.

What mistakes do enterprises make when they try to sell their data?

Enterprises think that selling data just turns into free extra money for their bottom line. That’s not true -- they need to market this data as they do any other product. They need to understand that building a data business takes a good deal of effort. Enterprises need to focus on building data as a revenue line in order to make it an appreciable part of their overall business.

What effort is required to prepare an enterprise’s data, given strict regulations such as the GDPR and CCPA?

Not all data is consumer data, so not all of it falls under the purview of the GDPR and CCPA. That’s an important point. If an agriculture technology company is collecting data about soil quality in a particular location, for example, that doesn’t fall under the GDPR or CCPA.

It all goes back to seller autonomy. We encourage companies considering selling their data to look hard at the data they have collected, pay close attention to the methods in which it was collected, understand the consent or notice to users as they collect that data, and make the appropriate decision about what can be made available for sale.

For Further Reading:

A Framework for Data Monetization

Data Monetization: A New Way of Thinking

Data Monetization Requires Balancing Data Availability and Governance

If you de-identify or aggregate data that isn’t specific to users, it no longer falls under the purview of the GDPR or CCPA. Ultimately, it’s up to the data sellers to determine how to comply with local laws and regulations that are germane to their data set.

Beyond the GDPR and CCPA, are business transactions likely to fall under government trade regulations by the FTC and other U.S. federal government entities?

Great question! Will the FTC regulate certain types of consumer data? Potentially. Will other government organizations (such as the SEC) regulate types of financial transaction data? Absolutely. Will there be data types in industries that will largely go unregulated? Without a doubt.

There’s no definitive answer to this question. Data sellers and buyers must have full transparency and control into what they are buying and selling -- this allows them to ensure they’re compliant with whatever those future regulations will be. This does not happen with the data broker model where the data supply chain is opaque -- it doesn’t allow participants to do the due diligence they need to be compliant. That’s why we feel the data broker model is broken.

What market research led you to believe there’s a market for a data sales service?

We do a lot of market research at Narrative. I’ve worked at companies that have both bought and sold a lot of data. Certainly, when I was at those companies we were at the leading edge of this trend, but every enterprise is going that way.

The other trend we've seen is the consumerization of B2B products. Most technologies start out focused on power users and experts. For markets to get to scale, you need to be able to -- in the B2B realm -- support your average business user, someone who isn’t steeped in the subject matter at hand -- in this case data. The question we asked ourselves before launching Data Shops was what does the average person know how to do digitally?

We came up with categories of things most folks could do online.

They know how to search for things -- Google has proven that out. They know how to scroll through social media; the Facebooks and Twitters of the world have shown that. People know how to buy things online -- Amazon’s stock price is a great proof point.

People know how to swipe left and right on dating apps. The real genesis of Data Shops was whether we could take one or all of these concepts and bake it into a place where we can make it easier for data to be bought and sold. Ultimately, e-commerce is where we landed although there are elements of both search and social, and in the future, there may even be elements of dating apps that become part of the core offering.

What expectations should enterprises have about making a profit from their data?

When it comes to profit expectations -- in the same way data is diverse, the value is also very diverse. We encourage clients to develop a business plan around monetization of data, as they would to add any business P&L and look at the value of the data over a 6-, 18-, 36-month period. Smaller businesses could make six figures annually on their data, larger businesses could make nine figures annually. It depends on their scale, the value, scarcity of the data, and how much demand there is for the data.

Tell us about the most important features of Data Shops.

Our platform is designed to allow anyone to buy and sell any type of data. True, there are data sets such as satellite imagery that on their own would not be a good fit because the data is binary, but even in those examples, there is associated metadata with the images. There is latitude/longitude data or when the image was taken. Those things can become sellable data.

Data Shops offers marketing. Your storefront on Narrative is like having an e-commerce solution for the products you’re selling. You can use social media, earned media, paid search, PR, and more - Data Shops gives you a place to point everyone to.

On the security side, we’ve built a very secure platform, separating all of our customers’ data into their own separate data lakes and adhering to best practices and standards in security and making sure that the data gets pushed to only the places that are determined by the seller.

I think the most impressive feature of Data Shops is that we can onboard suppliers with no engineering intervention on their side or our side. It’s a no-code product that lets people spin up their Data Shop within 24 hours. Data Set Manager -- the Data Shops app that does the onboarding -- literally brings it down to a task that takes minutes.


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