Building A Million Dollar Data Product

A Continuation Of Building A Million Dollar Data Analytics Service

My belief surrounding monetizing data is that the lowest value is selling raw data to other companies. Think credit card companies selling all of our purchase histories to everyone, from hospitals to advertisers. I say that, and of course, companies like Mastercard are likely doing just fine.

Truthfully some of this is biased provided my background working at companies that had some form of data product, but I do believe the companies that figure out how to take said data and put a layer in between it and those that want to access it can in turn create something that could be even more valuable.

However, it’s considerably harder to do (which is also why it is generally more valuable) because instead of simply throwing over the data and hoping the other company finds value in the data, you need to build a product. 

This means you must actually understand how the data can be useful to your external partners, even if they are advertisers looking to pay for improved targeted marketing for healthcare providers wanting to ensure they are exceeding benchmarks for patient experience.

So in this article, I wanted to discuss some of the lessons I have learned on building a data product customers want to pay for.

Data Products Vary

Before digging into some tips on building a million dollar data product, I do want to clear out at least one point.

The term data product means a lot of different things to a lot of different people.

For example

A data product is a logical unit that contains all components to process domain data and provide data sets via output ports for analytical use. - Data Mesh Manager

or

A data product is a data-driven, end-to-end, human-in-the-loop decision support or idea generation solution that’s so valuable that customers would pay for it or exchange something of value to use it. - Designing For Analytics

I think of a data product more like the later quote, at least for this article. Below, I have also outlined five traits that the data products I have either worked on or used had that I believe made them rock solid(I am sure there are more).

So how do you make a data product that end-users are willing to pay for?

Stop Thinking Dashboard As The Only End Result

Dashboards are always an easy first step when it comes to building a service layer you can monetize. It makes sense.

First off, as data people, we are taught dashboards are the end state of all data (despite all the articles announcing their death). But if you’ve worked long enough in data, you also know that most dashboards quickly become ignored. 

You can create far more lucrative data monetization platforms. For example, it's well understood at this point that Facebook is essentially a marketing apparatus.

Yes, it provides the ability to connect its users, but the way it makes money is by taking the data it has built on its users and helping advertisers better target and segment. So much so that I recall reading that in the NYT 2019 10-k risk factors they listed Facebook, Google, and Amazon.

Large digital platforms, such as Facebook, Google, and Amazon, which have greater audience reach, audience data, and targeting capabilities than we do, command a large share of the digital display advertising market, and we anticipate this will continue. - NYT 2019 10k - ITEM 1A. RISK FACTORS