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In the last decade, the Web has taken multiple long strides. From basic HTML, web pages developed a richer look and feel, and are getting more intuitive, user friendly and glossier every day.

Chart annotations can provide extra detail, highlight points of interest or simply be used for disambiguation purposes. However, filling a graphic with annotations can distract from the visual salience of the data itself, so it's important to find the right balance.

This post is the second part of my previous post where we discussed six tips to improve the performance of ASP.NET applications. In this post, we are going to discuss six more tips that could be another round a booster for your application performance. The link to the previous post is included.

Small multiples — collections of small (obviously) graphics where the same variables are plotted in each graphic but the data in each graphic are conditioned based on another variable (or two) — can be used for similar purposes, with some advantages and some disadvantages.

Building and hosting a web application on a web server is insanely easy with ASP.NET and IIS. However, many opportunities and hidden configurations can be tweaked to make it a high-performance web application. In this series post, we will discuss some of the most unused or ignored tricks that can be easily applied to any web application.

Drawing conclusion based on small samples is obviously problematic. At the same time, I also wonder whether the rise to prominence of "Big Data" can lead organisations to blindly collect as much data as possible rather than think logically about how much data is actually necessary to perform whatever analysis tasks are required.

Bar charts have a distinct advantage over chart forms that require area or angle judgements. That's because the simple perceptual tasks we require for decoding a bar chart - judging lengths and/or position along a scale - are tasks we're good at. But we also decode dot plots through judging position along a scale. Is there a reason to choose one over the other?

I've previously explained that it is essential that the bars of bar charts start at 0. The reasoning is simple: we use relative lengths of bars to compare values, so starting a bar somewhere else leads to false judgements. But what about line charts?

Scatter plots are a wonderful way of showing (apparent) relationships in bivariate data. Patterns and clusters that you wouldn't see in a huge block of data in a table can become instantly visible on a page or screen.

While the fundamentals of chart design largely concern the accurate and efficient visual representation of data, it is important not to forget the supporting structures - axes, tick marks, tick labels, and grid lines.