First Principles: The Foundation of a Great Data Product

To kick-off our new series about creating data products, we decided to write a white paper. This sounds simple, but this time it was a little more difficult than we expected.

Specifically, where do we start when we want to explain the difficulties data product teams face and how to overcome the critical obstacles? Should we begin with user personas and how to design data products that engage users? Do we kick things off with a piece about pricing data products and the finer points of ensuring future up-sell paths? How about a few words explain why data products that don’t use Keboola are doomed to fail and bring shame upon their product teams and ultimately their entire company? Hmmm... All possibilities, but none of these seemed the best way to start our series.
 
After much thought and coffee, we decided to start at the beginning with “first principles”—those foundational attributes which distinguish successful analytical applications from those that don’t quite meet their objectives. Our white paper would discuss these principles that make a data product truly great.
 
Wait—isn’t that a little vague, a little “fluffy”? Not at all. We felt compelled to start with these principles because, while not as mathematical as pricing or as black and white as dashboard design, it can be hard to know where to begin when you’re part of
a product team charged with building an analytics product. First principles act as guide post to help you stay on the right path.
 
These guide post are essential for product team because it isn’t easy trying create analytics that have a positive impact both for users and on your company’s bottom line. Do you start by setting revenue targets and determining the cost structure that
needs to be achieved in order for the data product to be profitable? Maybe you start by defining the various reports and information that you need to put in the hands of your customers to solve their problems and reduce the deluge of “more data” requests. Or perhaps you could start by brainstorming a list of all of the features that might make users engage with the analytics—requests you’ve received or functionality that is present in your competitors’ products.
 
Each of these paths is a reasonable starting point, but are any of them the best way to begin the process of building a great data product? That's where first principles come into play.
 
First principles don’t have anything to do with bar charts versus pie charts or even technology selection. Instead, they are a set of guiding beliefs about what makes a data product great. They are foundational truths and from them, everything else—features, pricing, and product strategy—follow.
 
As we start our series on creating data products, we felt that our first principles were a great place to begin and so, we’d like to share them with you in a white paper. Before you start to think that these principles will be a rehash of all the modern catchphrases such as “embrace the change” or “empower each other”—these are directly targeted at creating successful data products. They are a collection of elements that we’ve seen in great, successful analytics-based products and are the place where we always begin when considering each project.
 
We hope that you find these elements of a great data product useful in your journey to deliver analytics to your customers and, as always, we’re here to help if you’d like to build a data product together.
 
 
Thanks!