By now, the idea of agile development and a Minimum Viable Product or MVP is prevalent. The problem is, while most people have the minimum part down, people often haven't mastered the viable…. especially when it comes to analytics.
To quickly recap,a Minimum Viable Product is an approach, where you’re focusing on creating a product with a sufficient level of features to be able to solve a particular problem. This first iteration is used to collect user feedback and develop the complete set of features for the final product to be delivered.
That’s all nice and well, but you may be wondering what the benefits to this approach are as it concerns analytics projects...
Learning, and learning quickly
Is your solution actually delivering the value that you are trying to create? In a typical project, you may be months down the road before what you’re building is actually in front of users. This makes it difficult to determine its viability for solving the business case. The whole point is to prove or disprove your initial assumptions sooner.
What part of your users current process is really frustrating them?
Are the analytics we designed actually guiding them through their workflow and making their life better?
By getting a usable set of features in front of user’s earlier in the process, we can collect feedback and determine if we are in fact on the right track.
Allows you to get going faster
By starting with a subset of the features of the final product, you can get to a solution sooner, even if it’s not perfect. As highlighted by Henrik Kniberg (Crisp’s blog) on the topic of an MVP, if your final product idea is car, the whole point is to get somewhere faster; why start with a tire when you can start with a skateboard? As the minimum features we are creating are aligned to solve a particular problem, it’s a much faster and simpler process to determine if that is actually the case.
What isn’t an MVP
It shouldn’t have every possible feature and function that will show up in the “final” product. If you’re trying to build an analytics application based on 100 different user stories and a giant binder of waterfall requirements, this is not an MVP. While it is important to determine if the analytics are solving the outlined problem, it is also important not to get too hung up on “minor” issues such as text color, chart color or things like “I wish the report rendered in 10 seconds instead of 15 seconds….” Important feedback should definitely be recorded, but this can be used to develop the next phase of the product, don’t sacrifice the benefit of speed for issues of preference.
Have a plan in place
An MVP is also not picking a few features and winging it randomly, you need to have a plan in place.
Which of your products users is the MVP focused on and what are their top challenges or pain points?
What are the minimum set of features we can deliver to solve those problems and what data will we need to do so?
A Minimum Viable Product is a starting place for a larger analytics product strategy, if it’s successful, then what?
When creating an analytics MVP, we like to focus on three key areas:
User scope - Start with your user and their top challenges (as in agile development.)
Product scope - What is the minimum functionality we need to deliver to solve those problems?
Data scope - What data is necessary in order for them to make effective decisions?
Want to learn more?
In our recorded webinar, the experts in helping companies drive revenue through analytics at Looker and Keboola will go over how to launch your embedded analytics product quickly, successfully and with the greatest possible impact. We hope you enjoy it.