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Insight Advisor analysis types

Insight Advisor provides results using a wide range of analysis types. These analysis types provide best practice visualizations for generated charts.

Different analysis types are used depending on the inputs of the query and the characteristics of your data. The Qlik cognitive engine determines the best analysis type for your query depending on the available data. The following table describes the analysis types. Not all conditions for each analysis type are listed. The table also lists charts potentially available as alternatives when editing an Insight Advisor analysis.

Analysis types
Analysis type Description Dimensions Measures
Breakdown View nested dimensions of data that provide a breakdown of relative contributions to a measure. 2-3 1
Breakdown (geospatial) Group data by simple and hierarchical geographic divisions. 1-2 1-2
Calculated measure (KPI) Summarize performance in a given business segment or dimension using a key performance indicator (KPI). 0 1-2
Cluster (k-means)

Cluster data points aggregated by similarities from 2 measures over a dimension using a machine learning k-means algorithm.

1 2
Comparison Compare two measures for a dimension. 1 2
Correlation

Identify complementary and inverse relationships between two data values.

0-2 2
Mutual information

Create a measure of certainty between pairs of values using a machine learning algorithm that applies random data distributions.

The dependency indicator ranges between 0 percent (no dependency) and 100 percent(strong dependency).

Mutual information selects one field (measure or dimension) as the target and then selects 1 to 10 dimensions or measures as drivers.

Results for this analysis type for the same fields or selections might vary due to the random data selection.

variable variable
Overview

Describe how data ranges relate to each other in terms of an absolute measure.

1-2 1
Period changes

Build a sheet with measures, rank, and comparison analysis for dimensions across different time periods.

Requires a default calendar period set for the group containing the measure in the logical model.

1-2 1
Period changes (detailed)

Build a sheet with measures, rank, and comparison analysis for a hierarchy of dimensions across different time periods.

Requires a default calendar period set for the group containing the measure in the logical model.

1 1
Period over period

Compare dimensions across time periods.

Requires a default calendar period set for the group containing the measure in the logical model.

1 1
Period over period (selected)

Compare dimensions across time periods. It includes a filter pane for selecting dimension values.

Requires a temporal fields with autoCalendar-derived field selected as a part of the query.

1-3 1
Process control (mean) Monitor data against expected statistical ranges based on mean values. 1 date/time dimension 1
Process control (rolling mean) Monitor data against expected statistical ranges based on nearby values. 1 date/time dimension 1
Ranking Rank dimension values by relative importance with a measure. 1-2 1
Ranking (grouped) Rank hierarchical dimension values by relative importance with a measure. 1-2 1
Relative importance

Show the size of dimension values that contribute to the whole. Can also be used to perform Pareto or 80-20 contribution analysis.

1 1
Trend over time Show data trends over time, optionally broken down by a dimension with low cardinality. 1 date/time dimension and optionally 1 other dimension 1-3
Values (table) Show data organized in rows and columns that show measures and dimensions. 0-10 0-10
Year to date

Compare dimensions over the same period in a previous year.

1 1

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