Data Jujitsu: The Art of Turning Data into Product

by D.J. Patil

Smart data scientists don’t just solve big, hard problems; they also have an instinct for making big problems small.

The skill of be simple

Before investing in a big effort, you need to answer one simple question: Does anyone want or need your product?

Lean startup

The key is to start simple and stay simple for as long as possible. Ideas for data products tend to start simple and become complex; if they start complex, they become impossible.

The point is to have a conversation rather than just a form. Engage the user to help you, rather than relying on analysis. You’re not just getting the user more involved (which is good in itself), you’re getting clean data that will simplify the work for your back-end systems.

“People who viewed this product also viewed,” Amazon built a similar experience into the web page. In essence, they “grounded” their virtual experience to a similar one in the real world via data.

Give a product to your customers

As data scientists, we prefer to interact with the raw data. We know how to import it, transform it, mash it up with other data sources, and visualize it. Most of your customers can’t do that. One of the biggest challenges of developing a data product is figuring out how to give data back to the user.

But the data isn't the product we should return value to customer

An “inverse interaction law” applies to most users: The more data you present, the less interaction. The best way to avoid data vomit is to focus on actionability of data.

I think, the visualization and ux are the keys

Take heed not just to demand data. You need to explain to the user why you’re asking for data; you need to disarm the user’s resistance to providing more information by telling him that you’re going to provide value (in this case, more valuable recommendations), rather than abusing the data. It’s essential to remember that you’re having a conversation with the user, rather than giving him a long form to fill out.

You’ve probably recognized some similarities between Data Jujitsu and some of the thought behind agile startups: Data Jujitsu embraces the notion of the minimum viable product and the simplest thing that could possibly work.

Yes, Lean startup

80% of the work in any data project is in cleaning the data. If you can come up with strategies for data entry that are inherently clean (such as populating city and state fields from a zip code), you’re much better off. Work done up front in getting clean data will be amply repaid over the course of the project.

The key aspect of making a data product is putting the “product” first and “data” second. Saying it another way, data is one mechanism by which you make the product user-focused. With all products, you should ask yourself the following three questions: What do you want the user to take away from this product? What action do you want the user to take because of the product? How should the user feel during and after using your product?