# Automated Insights

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 require specific technical skills. For this reason, along with the Topic Chart, each Topic always comes with a set of **additional information**, called **Automated Insights**, which are directly related to it. This set of information can be extracted from the dataset underlying the Topic but cannot be represented on a graph. Therefore Crystal doesn't just answer your questions providing punctual graphic information, but also helps you understand them better by providing a complete and accurate description of **what the data is actually revealing below the surface**. In particular, Automated Insights are based on a Data Analysis technique called **Descriptive Analytics.** {% hint style="success" %} **Pills of Data Analysis** Descriptive Analytics are called this way because they **describe the dataset**, with a special focus on **past** and **present** events. {% endhint %} ## Discover The Hidden Insights Accessing the Automated Insights of your Topics is straightforward: 1. First, ask your question to Crystal 2. Spot the Right Sidebar of the Advisor Workspace 3. 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 get a better understanding of the most relevant aspects of the Topic and how to continue your analysis. {% hint style="info" %} **Please Note** * The number of Insights available for each Topic can range from *a minimum of one Insight to a maximum of 12*, depending on the Topic's underlying data. * Insights are *dynamic.* Since they are always related to the Topic you just asked about, they will *always change based on your questions* {% endhint %}

## How Do They Work? As Descriptive Analytics, the Automated Insight can shed light on **an amount 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 Insight. Therefore, whenever you ask about a specific Topic, Crystal detects the type of Objective / Visualization, and from the list of related Insights available, calculates as many 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** Below is a complete list of Automated Insights that Crystal is able to extract. {% hint style="info" %} **Please Note** This list reports the general purpose and logics of the analysis for each Insight. However, in practice, the specific calculations vary slightly to adapt to the Objective / Visualization they are applied to. {% endhint %}

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 in one category, then identifies an entity that is first for an entity in another category and at least third for another entity.
17	Worst Performer	Groups in one category, then identifies an entity that is last for an entity in another 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 up the positive and negative values.
23	Correlation	Calculates the correlation between two variables.
24	Highest Similarity	Calculates how often 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! ***