I’ve blogged several times over the years on various aspects of how we use Business Intelligence tools to visualise the mountains of data we accumulate in the course of our Performance Intelligence practice assignments. Even the practice name of “Performance Intelligence” reflects the vital role that such tools and techniques play in deriving insights from all of that data to allow us to get to the bottom of the really hard system performance problems.
Business Intelligence reporting solutions are built to report and analyse data from data warehouses. They can vary in size and complexity depending upon the needs of the business, underlying data stores, number of reports and number of users.
As an experienced Data Architect, I have started using Tableau software to visualise data. Tableau has been used extensively at Equinox in the past for visualising performance testing data, and you can see some of our experience with this tool in the various posts from Richard Leeke in this blog.
Recently I’ve been having a bit of fun exploring ways to visualise spatial data more effectively. I’ve long been an enthusiastic advocate of data visualisation techniques and I also have a lot of background with spatial data, having spent several years as the architectural lead on the Landonline project, which captured all of New Zealand’s land records, so combining the two interests seemed a natural thing to do.
In a previous blog entry (Calculating Percentiles with Tableau.) I discussed ways to visualise the distribution of transaction response times and showed how a percentile chart and a scatter plot can provide a very effective and complementary pair of views of the same data. In particular, I described how to make that viable with large datasets.