Automated Insights
This page describes the Automated Insights capability.
Sometimes, a graph alone is not sufficient to convey all the granular meanings hidden under data and digging into it can be very time consuming and requiring specific technical skills.
For this reason, alongside with the Topic Chart, each Topic always comes with a set of additional information, called Automated Insights, that are directly related to it.
They represent information that can be extracted from the dataset underlying the Topic but cannot be represented on a graph.
Therefore Crystal doesn't just answer to your question providing punctual graphic information, but also helps you understand them better providing a complete and accurate description of what data are actually telling below the surface.
In particular, Automated Insights are based on a Data Analysis technique called Descriptive Analytics.
Pills of Data Analysis
Descriptive Analytics are called like this because they describe the dataset, especially focusing on what has happened in the past and on what is happening at present.
Discover The Hidden Insights
Accessing the Automated Insights of your Topics is straightforward:
first of all, make your question to Crystal
notice the Right Sidebar of the Advisor Workspace
there, you will find the list of all the Insights available for your Topic
At this point, you can scroll through the Insights provided and let them guide your analysis: the information provided will help you understand better which are the most relevant aspects of the Topic and how to continue your analysis.
Please Note
The number of available Insights for each Topic can change from a minimum of one Insight to twelve Insights maximum, based on the Topic's underlying data
Insights are dynamic: since they are always related to the Topic you just asked, they will always change based on your questions
How Do They Work?
As Descriptive Analytics, the Automated Insight can shed light of a number of information related to what happened to your data in the past and what is happening right now.
The number of Insights available for a Topic depends on the type of Objective and Visualization, because each Objective / Visualization has specific information that can be extracted from it.
However, the actual number of Insights shown for a requested Topic depends on the specific requirements needed to calculate each Insights.
Therefore, whenever a specific Topic is asked, Crystal detects the type of Objective / Visualization and, from the list of available Insights linked to it, tries to calculate as more applicable Insights as possible based on the specific data underlying the Topic.
The final result of displayed Insights can be a number between 1 and 12 Insights.
Insights Catalog
Following, you can find the complete list of the Automated Insights that Crystal is able to extract.
Please Note
This list reports the general purpose and logics of the analysis done by each Insight.
However, in practice, the specific calculations done vary slightly to adapt to the Objective / Visualization they are applied to.
| N. | Insight | Logic |
|---|---|---|
1 | Average and Variability | Calculates the Mean and the Standard Deviation of an Entity. |
2 | Major Mean | Calculates the Entity with the highest average of all entities. |
3 | Minor Mean | Calculates the Entity with the lowest average of all entities. |
4 | Highest Sum | Calculates the total sum of the relevant values associated with a specific Entity. It then groups these values according to a category and identifies which category has the highest total sum, thus highlighting the Entity with the highest value within the dataset. |
5 | Lowest Sum | Calculates the total sum of the relevant values associated with a specific Entity. It then groups these values according to a category and identifies which category has the lowest total sum, thus highlighting the entity with the highest value within the dataset. |
6 | Extreme Values | Calculates the Minimum Value(s) and the Maximum Value(s) of an Entity. |
7 | Unique Values | Calculates the number of distinct values of an Entity. |
8 | Focus on Last Timeframe | Calculates the Entity Value at the beginning and at the end of the last consolidated timeframe, based on the Time Aggregation configured for the Topic (Months, Quarters, Years, ...). Calculates the Average of the Entity across the Time Range. |
9 | Global Change | Identifies the Entity Value at the beginning and at the end of a Time Series, based on the Topic’s Time Range. Calculates the difference between the starting point and the end point of the Time Series. Calculates the percentage difference between the starting point and the end point. |
10 | Highest Difference | Calculates the highest difference, both in absolute value and in percentage, among consecutive values. |
11 | Lowest Difference | Calculates the lowest difference, both in absolute value and in percentage, among consecutive values. |
12 | Interesting Event | Searches for statistical anomalies. If multiple anomalies are identified, selects the most “anomalous” anomaly. |
13 | Timeframe Comparison | Compares the values of on Entity in two different time periods, then highlights the variation. |
14 | Top Contributor | Ranks all Entity Values from highest to lowest. Identifies the one with the highest value. Calculates the percentage of this highest value against the Total Sum of the Entity across all Values. |
15 | Bottom Contributor | Ranks all values of the Entity from highest to lowest. Identifies the one with the lowest value. Calculates the percentage of this lowest value with respect to the Entity's total sum of all values. |
16 | Best Performer | Groups by one category, then identifies an entity that is first for an entity in the other category and at least third for another entity. |
17 | Worst Performer | Groups by one category, then identifies an entity that is last for an entity in the other category and at least third last for another entity. |
18 | Percentage Difference | Calculates the difference between two percentages. |
19 | Cluster Comparison | Finds the total average, then the average for the top N, and the average for the bottom N. |
20 | Distribution of Data | Calculates how many positive and negative values are present. |
21 | Mean of Positive and Negative Values | Calculates the mean of positive and negative values. |
22 | Total of Positive and Negative Values | Sums the positive and negative values. |
23 | Correlation | Calculates the correlation between two variables. |
24 | Highest Similarity | Calculates how many times the time series overlap with each other. |
25 | Value of Progress | Highlights the value of the progress towards a target. |
26 | Percentage of Progress | Highlights the percentage of target reached. |
27 | Most Common Value | Highlights the most common value found. |
Keep analyzing your data!
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