Minimum Viable Data (MVD) - Shotgun or Sniper approach
2 min read

Minimum Viable Data (MVD) - Shotgun or Sniper approach

Lean is in. Releasing early is the rage. Getting feedback as quickly as possible on your product has become an axiom of startup development.

One thing that this doesn't address though is what should be included in your first release. For your product itself, the typical answer here is the MVP - that is, the narrowest product scope that conveys your vision while allowing you to gather user feedback. This makes sense for the product itself as features are endless -- you can quickly get sucked into a black-hole of adding more and more without ever releasing.

However, if the core value of your product is to act as a conduit to data (hopefully in some new and innovative way), how do you define the MVP of the data set that is made available via your product.

There are three approaches that I see.

1) Shotgun - Include a small percentage of data about each entity within a set.

2) Sniper - Providing a lot of data about one entity within a set.

3) Shotgun & Sniper - Including all of the data on all entities within the set.

For a concrete example, lets say I have an awesome new way to visualize sport data. I made a product MVP - it implements 10% of my visualization idea, enough to let users know where the product will head but not nearly polished to my desire. Sounds right to release from a product MVP perspective to me.

What data should be included with this though? Using my three approaches from above, I could

1) Shotgun - Include one top line metric about every athlete in every major sport in America.

2) Sniper - Provide 80% of the data on one league (NBA).

3) Shotgun & Sniper - Get all the information on every athlete, team and sport out there. Obviously the best, but will take too long to do in any MVP type fashion.

I've been thinking about this a lot lately and am leaning towards the Shotgun approach. I think it's better from a feedback perspective in that you'll not only get feedback on your product idea/implementation but you'll also see which sport users are engaging with most (continuing the example from above). This will allow you to focus your energy on the most converting sport initially while you continue to build out your product and data set. This also allows for a more focused user acquisition strategy.

In essence, the Shogun approach allows you to more intelligently know where to Sniper next (Shotgun -> Sniper) as opposed to just picking the Sniper blindly. There are obvious alternatives here that probably should be done to aid this process (asking users, making different signup pages for each league and seeing which converts best etc…).

Also, the sports example isn't that great because the datasets are somewhat limited (probably a naive assumption but I'm going with it) and there's likely one source/API where you can pul all the data from. When the data set gets larger though (e.g. wanting to be a portal to all the products in the world - TVs, Guitars, Shirts, Cars), you can see how this problem grows quickly and solving it becomes even more relevant.