Evaluations can sometimes be an unclear way to form conclusions because we will come across a correlation that may not imply a causation. The following visualisations however, are about getting closer to a decision; or even getting to the right direction to complete a trial and come to a conclusion. In most situations, finding a second or third piece of evidence that informs the hypothesis can be valuable. Hopefully through exploration you have found out which variables you need to model out the effect of (or integrate out, which is the terminology used in Clarofy) decide which areas to exclude, and what the sensitivities might be when you draw a conclusion.
This is article 5/7 in our series focussing on the bread and butter visualisations our analytics team use.
To receive a copy of the whole series now go to, www.clarofy.ai/download
Critical in exploring the data and significantly useful in evaluation; insight gained from visualisations inform the whole analytical workflow. Some types of visualisations lend themselves better to one use over the other and, making the decision of which to use in each application is a learning process. At Interlate, our experience with analytics in Minerals processing plants has given us an appreciation for how to make these decisions, and we will outline some of our insights in the following series.
Bar charts, Stacked Bar charts and Waterfalls
Bar charts and there variations are great for comparisons given properly selected axis ranges, and often give a much better way to present and summarise any evaluations or value breakdowns.
- Bar charts are great for comparing KPIs between different categories
- Percentages of operating time (useful for annualising value).
- Summations or totals of events, metal produced, product made, feed processed.
- They are the best alternative to pie charts, there’s nothing a pie chart can do that a bar chart can’t do better.
- Stacked Bar charts are great to show how much of certain runtime belongs to a category. For example; creating a bar chart of a process variable and then stacking bar charts based on operating mode.
- A Waterfall variation allows the visualisation of the apportioned value to certain categories and is a great way to incorporate value into your analysis.
- Continuous data; which has to be binned first before being used in bar chart. This shouldn’t be a restriction however, most analytics platforms should allow binning.
- Data that has not been explored. By themselves, bar charts can be misleading and without the context available from other types of visualisations could lead to ambiguous, or worse, false conclusions.
Tips & Tricks:
- Overlaying Bar charts with a 3rd or 4th variable as a line chart can provide further context.
- If using stacked bar charts try stacking your variables in a different order
- Using seemingly redundant colouring on bar chart groups at the start of presentations are a great way to communicate which colour is associated with which variable.
What do you find is best practice with bar charts?
When would you use a pie chart instead of a bar chart?
This article series will focus on our key visuals, Interlate hope to share our experience to others and provide a robust understanding for their place in Clarofy our visualisation and data analytics application. No software installation required and runs straight from your browser. www.clarofy.ai/download