Created by @sbalnojan for www.thdpth.com.

What is a data-heavy product?

A product that delivers value to the user mainly through data.

<aside> 💡 I’d love to call it “data product,” but the data mesh ruined that for all of us.

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The three kinds of data-heavy products:

1. Infrastructure, products that help others build things with data
   1. Examples: dagster, SQLMesh, dbt, Meltano
2. End-user products built by software engineers.
   1. Examples: Netflix’s recommendation engine, Amazon's “What others liked”, the Twitter API, the GitHub API, GitHub trending repositories
3. End-user products built by data engineers, data scientists, or machine learning engineers.
   1. Examples: Recommendation systems, Machine Learning solutions, dashboards, “data products” in the Data Mesh sense of the word

<aside> 👉 Of course, there’s no real difference between 2 & 3, but we all like to believe there is. For the end-user, there isn’t; For product management, there shouldn’t. The point of listing them here explicitly is to remind you of that.

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<aside> 💡 The difference between 1 and, 2 & 3 is material. While 2 & 3 turn data straight into value, you usually consider the value of (1) in the context of building 2 & 3.

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Let’s get into the checklist!

FYI:

• We’re using customers & users exchangeably.
• You do NOT need all checks, but the more, the better, and all “yes” is possible.
• 26/26 is a perfect score. It’s all yes/no. Aim for more than 13 points.

Click on expand on the bullets below to get the details!

1) Data Questions (X of 9?)

<aside> 💡 The data questions help you understand the future value of the product you’re building.

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<aside> 💡 The idea is simple: The future of data is forecasted to be dominated by all of the topics discussed below.

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• If the amount of data available externally is growing fast, does your product become more valuable to an individual customer?
• If the number of externally available data sources grows fast, does your product become more valuable to an individual customer?
• If companies value recency tomorrow more than today and expect most data to be “recent,” is your product becoming more valuable?
• How about your products' value if the amount of data sources an individual company taps internally grows?
• If the amount of data targets an individual company pushed data into internally grows, how about your products' value?
• If companies (or end-users!) value dealing with unstructured data more, is your product becoming more valuable?
• If the amount of unstructured data grows fast, does your product become more valuable to an individual customer?
• If customers use more data on their phones, gadgets, and IoT devices tomorrow than today, what about the value of your product?
• How about the sheer amount of data? If my data amount grows, do I automatically get more out of your product?

2) Decision Cycle Questions (X of 8?)

<aside> 💡 The decision cycle questions help you understand the value of your product today!

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<aside> 💡 The idea is simple: the more you support, the less you have to integrate; thus, you provide more of a whole experience.

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• Does your product help to collect data?
• Does your product help to move data around?
• Does your product help to transform multiple data pieces into "information"?
• Does your product help to derive a decision from information?
• Does your product help to take action based on a decision?
• Does your product support the tools & people collecting data?
• Does your product support the tools & people moving & transforming data?
• Does your product support the tools & people using data?
• Example
• Resources

3) Mkt & Positioning Questions (X of 4?)

<aside> 💡 The mkt & positioning questions help you understand whether you already have a clear product. Strong products can sell without good packaging, but average ones can’t sell without. And all sell better with it.

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• Is your product messaging clear & authentic?
• Is your messaging simple?
• Is your messaging tested?
• Are you going to be the biggest fish in a small pond quickly?
• Resources

4) Big Picture Questions (X of 5?)

<aside> 💡 Big Picture questions help you determine whether you have a product fit for the data market.

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• Is your product solving a problem or working on a system?
• Are you building a community around your product?
• Are you focusing on unlearning more than you’re learning?
• Are you focusing on pie making over pie taking?
• Is your product integrated into a large vision for the whole data space?
• Resources