No, Living Analytics isn’t a new lifestyle trend, like the 9-second diet that allows you to eat whatever you want, however much you can consume in under 9 seconds every 3 hours**. Living analytics are analyses that grow and evolve over time, using the same or similar data structures; in other words, the same data structure, different data, different insights.
Consider the simple table below:
You might take your favorite analytics tool and make a few charts out of this data: maybe a bar chart with
Department as the dimension and
Sum(Sales) as the measure. Then maybe you add a scatterplot with
Product as the point dimension,
Sum(Sales) as the (x) measure and
Sum(Margin) as the (y) measure. Then to complete your view, you add a line chart with
Date Sold as the dimension and
Sum(Quantity) as the measure.
This is a very simple view of some very simple data via a couple of charts, and it probably doesn’t provide you with a ton of insight, but as you feed these visualizations with data over time, more and/or different insights may emerge. This is why dashboards have become so popular; seeing the same structure with different/updated data can yield actionable insights that lead to better reactive capabilities.
The more complex a data structure is, the more possible permutations of dimensions exist, which is where the concept of Living Analytics starts to shine, and tools that embrace that concept will become more and more valuable. This complexity is what dashboards today don’t really account for. A dashboard is inherently defined as a minimally configurable visualization or set of visualizations that tell a specific story. Some tools allow you to create more configurable dashboards, but at the end of the day, it’s still a dashboard.
This limitation of dashboards is one of the things that drove us to create Knarr, in which we aim to create a living document that is comprised of the entire history of analysis that has been done using a given data structure or set of data structures. This allows for a huge amount of flexibility to continuously iterate on the structure of a dataset, never losing what you’ve created before and being able to restore any snapshot with live updated data.
Combine these capabilities with Collaborative Analytics and you’ve really got something!
**Please note that the 9-second diet is not a real thing and you should 100% not try it.