Ecosystem Analytics: it's about context
The analytic lifecycle Starting with Data Data can be very annoying. There can be too much or too little, there can be gaps and inconsistencies, there can be anomalies and errors. Often it's all of them which makes the pursuit of data quality a constant challenge. Working through the Visualisation Why is data quality required? Well, if you put bad data into a visualisation, you get a bad visualisation. And, if you were planning on making any decisions based on that visualisation then your chances of being right are diminished. Delivering the Narrative But let's assume you have just the right data quality for your purposes. Now you need to present it in that perfect visualisation. A nice bar chart, a flowing sankey, or a complex radial dendrogram. It's finding the ideal visualisation to... ummmm... do something. So, what is it you are wanting to do with the data and its nice new visualisation? Often, by the time you get to this point you've spent so much time