How data modeling works

NOTE   Data modeling is a complex undertaking. Modeling your data so that it produces the reports your company requires takes expertise. Work with Calabrio Professional Services or a Calabrio partner for the best results. This overview is a brief introduction into what goes into the process of data modeling.

Data modeling is the process creating a relationship between your source data held in a data set and Data Explorer. The data modeling tool allows you to map the columns in a data set to the fields in your data library so that information can be used to create reports.

Data modeling can be performed at any time, such as when you first add a new data set to your data library. As your data and reporting needs grow, you can revisit your data model to add, remove, or refine it.

Identifying the inputs and outputs before you start data modeling can help you identify what is required and what data can be ignored. Determine what your data is about—for example, it could focus on sales. In that case, sales data is your input.

Then consider the outputs—the reports that your users will want to create, for example, quarterly sales by product or sales by region. Listing the basic outputs before you start modeling can help you focus on the data that will be required to produce those outputs.

What is a Data Model?

A data model is a structure made up of data sets, mappings, field definitions and metadata, formulas, relationships, and other key constructs on which a data library is based. The data model provides the foundational structure for the data library.

The data model organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model might specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner.

The Data Modeling Process

Step 1: Add the data set to your data library

All new data sets require data modeling if their signatures do not match those of existing data sets. Data modeling is done using the data modeling tool on the Design Data Set page.

Step 2: (Optional) Filter the data set

Once the data set has been added to your data library, you can limit the scope of the data set by applying filters. You can also deselect columns in the data set and show only data from specific data set contributions.


Your new data set contains data about employees. It has five columns:

  • FirstName
  • LastName
  • EmployeeNumber
  • EmailAddress
  • Location

You want to limit the data in this data set to employees working in the Minneapolis location, so you add a filter on the Design Data Set tab:

This tab also gives you information about the quality of the information in the data set. It identifies if there are any rows that are invalid so you can correct them in the source file.

Step 3: Map data set columns to data library fields

Next you map the columns in your data set to the fields in your data library on the Map Fields tab. The Data Library Fields pane contains all fields in your library. When you select a column from the Data Set Columns pane, the data library fields are divided into compatible fields and other fields. The compatible fields contain the same type of data as the column does. You can map the column to either a compatible field or other field. There is hover text for each column and data library field so you can view their data properties.


The columns are mapped to compatible fields in the data library.

This Data Set Column… Maps to this data library field:
FirstName EmpFirst
LastName EmpLast
EmployeeNumber EmpID
Location Office

You can also create new columns that contain calculated data. This is much like calculating data in a spreadsheet. Any computation you make across a single row in a spreadsheet you can also use to create a calculated column (or data library field). Perhaps your data set contains sales transaction lines and there is a quantity sold and a price but not a sales total that multiplies quantity by price. This calculation can also be done as part of a report, however, there are some advantages to including them as part of your data model. These calculations are preprocessed, and thus a report could be generated more quickly using that preprocessed data.

Step 4: Organize your data

Finally, you organize the data set data on the Organize Data tab. The fields in the data library are organized into measures, subjects, and time. These elements are the business terms you want people to see when they create reports.

  • Measures—what is being reported on, for example, a service queue or team
  • Subjects—how the report groups information, for example, by the agents on a team
  • Time—a time associated with an element of the report, for example, the duration of calls the agent handled

Data Explorer automatically sorts the fields in your data library into the appropriate column, but you can rearrange them if necessary.

At the top of each section where the terms are listed is a drop-down field that allows you to filter the terms in the column by data set. You can also search for strings in the Search field. These fields allow you to quickly find the terms you want to work with.

When you hover over a term you view a summary of the term’s properties, and see three icons. These icons enable you to edit the term’s properties, hide or show it in the Reports editor, or delete it from the data library.

NOTE   How you configure term properties and organize them on this tab controls to a large extent the success of your reports. As with all things to do with data modeling, these tasks are complex and usually done primarily by Calabrio Professional Services and Calabrio partners when your data library is created.