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Table Of Contents

Data Quirk

AgileData.io uses the term Quirk to describe data that falls outside the pattern of what is expected.

There are many other words for Quirks, such as anomalies, outliers, deviations, oddities, exceptions, irregularities and many more. The term that you are familiar with might be based on the type of analysis you do, or the type of data you use, or even just the preference of your organisations or the people that you work with.

Humans easily recognise patterns and spot things that aren’t like the others, our senses provide this information for us. If the pattern is simple, and there is only a little data, you can easily scan a table of data to spot the problem or make a graph to highlight the Quirks in the data.

Some simple examples include the following:

  • A purple cow in a group of brown ones

  • A race-car that is far ahead of the others

  • A kid who isn’t paying attention during class

  • A day when online orders are up, but payments are down

  • A person who got well, where others didn’t

Some Quirks represent a potentially significant issue, and others represent a random occurrence. Identifying and investigating Quirks is important to understand if we need to take action and rectify something or not. Questions are an essential pattern to understanding the reason behind the Quirk.

  • Why did it happen?

  • What’s it related to?

  • Did it happen only once or many times?

  • Has it happened before?

  • Is it likely to happen again

  • What can we do to encourage or discourage more like it?

Manually understanding how and why a Quirk happened, and whether there is a pattern in the Quirk requires a lot of effort. AgileData uses Augment Analytics to remove the complexity and manual effort in identifying these Quirks and their causes.

AgileData.io’s Augmented Analytic powered Quirk detection identifies causations and correlations of the Quirk to enable you to decide if you need to take action or not.

Other Quirks

AgileData.io has a number of other Quirk Sensors that are baked in to identify behaviors we may want to investigate, for example user login’s, system uptime, system performance etc. These sensors are all part of the automagical things we do, so you don’t need to worry about them, just sleep easy knowing we are always watching and managing AgileData to make sure your data is available and safe.

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