Dataforward companies harness the power of their data by defining a set of “metrics”  or key performance indicators (KPIs)  and then analyzing them in different ways to make operational and strategic business decisions.
Related Articles:
 Sisu 101
 Defining a Metric
 Understanding & Defining Numerical Rate Metrics
 Understanding & Defining Weighted Average Metrics
 Understanding Impact
 Understanding Facts
Understanding Metrics
A Metric is an aggregated calculation of a column within a data set (table or saved query from a data connection). 
The Sisu workspace is centered around Metrics. Metrics can be defined at any time, and can be used by the entire company in creating Analyses.
Here’s a good way to think about Metrics:
Metric = Metric Column(s) + Aggregation Method
Using different Aggregation Methods (which are calculations such as average, sum, count, or rate) on a selected column in your data can produce different types of Analyses. For example:
 average order value (using an average calculation as the Aggregation Method on the column)
 total revenue (using a sum calculation as the Aggregation Method on the column)
 and more.
In the following example, in the dataset the “order_value” column has been chosen as the Metric Column to analyze. This column can be used to define different Metrics, using various Aggregation Methods .
Note that in this example, the “AVG of order_value” is the average order value, which is the KPI that is being analyzed by Sisu with this defined Metric IF “Average” is selected as the calculation type Aggregation Method. If “Sum” is selected as the Aggregation Method for the Analysis, Sisu will perform the analysis based on the total order value (“SUM of order_value).
With different combinations of Metric Column and Aggregation Methods, you can perform a variety of General Performance Analyses. When you add “comparison” to an Analysis, you can perform useful Time Comparison and Group Comparison Analyses.
Seven Metric Types
When defining a Metric, you’ll see that Sisu provides seven different types, as shown below:
Each Metric type is defined using one or more numerical or categorical columns in your data, as described below.
Metrics Defined by 
Metrics Defined by 
Metrics Defined by 

Average 
Weighted Average 
Categorical Rate 
Sum 
Numerical Rate 
Row Count 
Count 
The following charts provide further details about each Metric type.
NUMERICAL METRICS 

Average 
Sum 
Count 

Metric Calculation 
sum(Metric column) / count(row) 
sum(Metric column) 
count(row) 
Sample Equation 
(30+90+40+120)/4 
30+90+40+120 
4 
Sample Result 
65 
260 
4 
NUMERICAL METRICS 

Weighted Average 
Numerical Rate 

Metric Calculation 
sum(Metric column*weight column) / sum(weight column) 
sum(Metric column)/ sum(denominator column) 
Sample Equation 
( 30*2 + 90*4 + 40*12.5 + 120*2 ) / (2+4+12.5+2) 
(20+80+15+30) / (25+150+40+90) 
Sample Result 
56.6 
0.475 or 47.5% 
Refer to the following articles for more information about these two Metric types: 
CATEGORICAL METRICS 

Categorical Rate 
Row Count 

Metric Calculation 
count(condition) / n 
count(condition) 
Sample Equation 
3 recurring orders out of 4 
3 recurring orders 
Sample Result 
75% 
3 
How Metrics Are Used
Sisu directly connects to data warehouses. A Metric can be built one of two ways:
 On an existing table in the data warehouse
 Through a custom query used to define Metrics using the Query Editor.
(Any member of your organization can then investigate these metrics without needing to create or review the same SQL code every time.)
Refer to the “Connecting & Managing Data” section for details about uploading or connecting to your data. Refer to Creating & Managing Queries for details about creating a custom query on your data. 
With a metric defined, Sisu can run three types of Analysis:
 General Performance Analysis
 Time Comparison Analysis
 Group Comparison Analysis
Refer to Defining a Metric for instructions on selecting and defining a Metric to use in your Analyses. Refer to Understanding Sisu's Analysis Types for detailed explanations of each analysis type. 