Best Practices for Designing Models

Best Practices for designing dashboards

Visualization and usability best practices

• Use conditional formattings to improve KPI visualization
• Use on-screen settings for settings that often users want to change, as they are easier to use than opening the settings. They also guide users to change parameters that might be relevant from the analysis viewpoint.
• Check that each measure and dimension has a descriptive unit. For example, the generic terms "cases" and "events" might not describe the counts best, and cases might be orders, incidents etc.
• Define a custom label text if they describe the measures and dimensions better. Still, in many measures and dimensions, the automatically generated title is suitable.
• Defined custom title for the chart, if the automatically generated title is not descriptive.
• Note also how the special values , such as null and empty strings, are presented and set a descriptive label name. For example, when showing duration between events, cases which don't have the events, are shown as nulls. Instead of empty label, the label can be "Events missing".
• If there are no meaningful filters that can be created from the chart, disable creating filters from chart. When filtering is disabled, the cursor doesn't indicate clickable items which would otherwise attract users from making clicks leading to dead end.
• Limit the shown case/event attributes and event types, if there are some that are not needed. This will make the dropdown lists shorter making it easier for users to find the important selections. This doesn't have performance impact, though.

Performance optimization best practices

• When choosing the Analyzed objects setting, prefer Cases over Events, as calculating from events is notably slower than cases, because in usual models there are lot more events than cases. Some KPI's, even though using the event level data, can be calculated from the cases. Also Variations, Event types and Flows are generally fast. On the other hand, Flow Occurrences is slow, as the number of the for occurrence objects is even more than the event count.
• Prefer ready-made measures and dimensions, and create a custom when there no ready-made available. This is because the ready-made measures and dimensions have been optimized for performance. When creating a custom measure or dimension, you need to be careful not to write the calculation logic in an non-optimal way.
• For some simple calculations, the Statistical calculations may be used instead of writing a custom expression. Also the Adjustment expression is useful in avoiding custom expressions in certain cases..
• Limit number of returned rows: The Max rows setting determines the number of returned rows. The less there are rows the better performance. Usually in dashboards, limiting the amount of shown data is desired also for usability, e.g., show only the top-20 items etc.
• The more there are charts in the dashboard, the more it takes to get the dashboard opened and all chart shown. This is because each chart requests a calculation which all need to be done at the same time in the server. If the dashboard opens slowly, charts could be divided into multiple dashboards and links created to navigate between them.
• Sorting the data affects performance, so use sorting only when it's relevant for the analysis.
• Group Rows Exceeding Maximum goes through all rows that otherwise would be left out of the calculation, which has an impact on the performance, so use it only when the information is useful for the analysis.
• Alternative to chart filter is Analyzed objects containing filtering, e.g. ... might improve performance
• Don't use dimensioning when it's not needed. When desire is to have a row for each root object, dimensioning is unnecessary. For example, Cases as Analyzed objects and dimensioning by case id will lead to a row for each case, but the same result can be achieved by disabling dimensioning.

Advanced performance optimization best practices

• For slow charts, use the Benchmark Performance to find the fastest settings. Usually settings up a working chart is the first thing to do, and if the chart appears too slow, you can try to find another, faster way to calculate the same chart.
• Avoid making same calculations multiple times in different measures. If there are repeating expressions, create a separate measure for the repeating part, and define the measure as a variable, which can then be referenced from other measures.
• Different models can be used in the same dashboard, and for example filtering still works if the models use same case/event attribute and event type names. This allows to create models optimized for specific charts, which might improve performance.
• Sampling improves performance in cases when it can be used. The idea of sampling is to pick only a portion of the root objects for the dimension and measure calculation to improve performance. Unfortunately, in most cases, it cannot be used, as it affects the analysis results, for example in object counts.

Other best practices

• Use preset as basis: The preset contain commonly needed analysis, so in many cases, you find what you are looking for from the presets. It's easy to take a preset as a basis, and continue modifying the chart settings for your customized needs.
• Mappings can be done freely, so dimensions don't always need to go to the X-axis and measures go to the Y-axis.
• Avoid Custom layout settings: Avoid Custom layout settings as their compatibility with future QPR ProcessAnalyzer versions might not be maintained. Use Custom layout only when it's absolutely necessary for the visualization.
• For large data exports use CSV: When there is need to export large amount of data, prefer the CSV export over Excel export, because the CSV performs better for large data.

Best practices for creating models

• Use the most suitable datatypes for case and event attributes. If there are only two possible values, boolean is the best. The true and false values can be mapped into a textual presentation, so it's not needed to use strings to get desired texts for visualizations. If numerical data cannot contain decimals or precision containing decimals is not required for the analysis, integer should be used over float. If the attribute value contains a numerical score (such as number between 1 and 5), integer is better than string. Usually string is the slowest.
• All datatypes support null values to mark missing or some kind of special values. The null value can be freely used to mark anything - it's just a matter of decision.
• Include only case and event attributes that are needed by the dashboards. For analysis, more attributes maybe useful, but they are not needed for dashboards. Loading model is slower, when there are more attributes.
• Note the Load Model on Startup setting. When to use it correctly.
• Include only events that are needed by the dashboards
• Shorter event type names are easier to read in the UI and provide slightly better performance. This is also true for case and event attributes values.
• Use calculated attributes, to pre-calculate case level KPI's from measures. It cannot be used when there is event type filtering applied. On the other hand, don't use calculated attributes unnecessarily because they are stored into memory, and thus they consume memory list the normal attributes. Don't calculate anything from the entire model level in the calculated attributes expression, because it will lead to very slow performance in model loading.
• Use the model description to document the necessary details regarding the model for other users.