Minimally Viable Data Visualization

I received an email a few days ago from the founder and CEO of a new analytics software company that led to an interesting revelation. In his email, this fellow thanked me for sharing my insights regarding data visualization and shared that he has acquired several of my books, which are “nearing the top” of his queue. He went on to provide a link to his website where I could see his attempts to incorporate visual analytics into his product. After taking a quick look at his website and noting its poor data visualization practices, I wrote him back and suggested that he make time to read my books soon. It was in his subsequent response that he revealed what I found most interesting. In response to my concern about the poor data visualization practices that I observed on his website he wrote, “The site content has been delivered with a minimally viable product mindset.” My jaw hit the floor.

This fellow apparently misunderstands the concept of a minimal viable product (MVP). According to Wikipedia, “a minimal viable product is a product with just enough features to satisfy early customers, and to provide feedback for future product development.” When you initially introduce a new product, it doesn’t make sense to address every possible feature. Instead, it usually makes sense to provide enough features to make the product useful and put it on a trajectory, through feedback from customers, to become in time a product that is fully viable.

This misunderstanding reminds me of the way that product companies have sometimes misapplied the Pareto Principle (a.k.a., the 80/20 rule). Years ago when I worked for a business intelligence software company, it was common practice for managers in that company to encourage designers and developers to create products that only satisfied 80% of the customers’ needs, which they justified as the 80/20 rule. This has nothing to do with the Vilfredo Pareto’s observation that 80% of the property in Italy was owned by 20% of the people in Italy, a ratio that he went on to observe in the relative distribution of several other things as well. Pareto never promoted this ratio as a goal. It’s amazing how concepts and principles can be perverted in silly and harmful ways.

The concern that I expressed to this fellow about his fledgling product was not a lack in the number of features but a lack in the quality of the features that he included. Shooting for minimally viable quality is not a rational, ethical, or productive goal.

My exchange with this fellow continued. I pointed out that “the analytics space is filled with minimally viable products.” This was not a compliment. To this, however, he enthusiastically responded:

Certainly, agreed – which is one reason we believe we can be successful. I’m using MVP in the context of product development; the quicker we deliver functional capabilities the more quickly we receive feedback and iterate through enhancements. In terms of mature client solutions we stand for, and strive to deliver, an exceptional standard of quality – rare in the analytics space.

The notion that quick iterations can make up for sloppy and inexpert development is nonsense, but this philosophy has nevertheless become enshrined in many software companies. Is it any wonder that most analytics products function so poorly?

There is absolutely no justification for producing an analytics application that at any stage during the development process chooses inappropriate data visualizations and designs them poorly. Best practices can be incorporated into each stage of development process without undue or wasted effort. Not only are ineffective data visualization practices at any stage in the process inexcusable, they do harm, for they expose and thereby promote those bad practices.

This fellow used the “minimally viable product mindset” as a justification for the fact that his team doesn’t understand data visualization. This is all too familiar. To complete the story, here is my final response to this fellow’s mindset:

You are not exhibiting the “exceptional standard of quality” that you claim as your goal. Every single player in the analytics space claims to strive for “exceptional quality,” but none exhibit a genuine commitment to this goal. To seriously strive for this goal, you must develop the required expertise before beginning to develop solutions. Slow down and take time to get it right. The world doesn’t need any more “minimally viable” products.

What are the chances that he will accept and follow my advice? My experience suggests that odds aren’t good, but I’d be happy for this fellow to become an outlier. We don’t need more bad analytics products. A few that are well designed are all that we need.

One Comment on “Minimally Viable Data Visualization”


By Andrew. May 23rd, 2018 at 7:59 am

Interesting how his response to your feedback is a defensive claim that they are deliberately diminishing their product in order to develop it based on feedback.

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