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Version: Early Access

Introduction to Attributes

Attributes are the business concepts reflected in your data. Attributes provide a context in which to report on and analyze business facts or calculations. An attribute on a dossier serves as a label for a group of metrics.

For instance, consider the sales figures of your company. If you were informed that your company had sales of $100,000, you can gather little useful information. To make the sales figure meaningful, you would need to know more about the source of that sales figure, such as:

  • A time frame for the sales
  • Who and how many people contributed to the sales total
  • What products were sold from which departments
  • The scope of the sale, such as national, regional, local, or a single store

Attributes such as Month, Year, Department, or Region can provide the analytical depth necessary to understand your company sales figures. Use a Day, Month, Quarter, or Year attribute to view sales data summarized at daily, monthly, quarterly, and yearly levels.

Attribute Creation

Digital.ai Intelligence Applications automatically creates attributes when you import data. Attributes appear with an attribute icon on the Datasets and Editor panels.

You can also create new attributes, based on existing attributes in your dataset , while viewing a dossier.

For instance, you can create a Year derived attribute from a Date attribute in MMDDYYYY format.

Attribute Elements

Attributes are comprised of attribute elements. An attribute element is a value of an attribute.

For instance, the City attribute includes the New York and Dallas attribute elements. January, February, and March are elements of the attribute Month.

Attribute Forms

Attribute forms are additional descriptive information about a business attribute. Most attributes only have the forms ID and Description. However, an attribute can have many other forms.

For instance, the Customer attribute contains the First Name, Last Name, Address, and Email Address forms.

Derived Elements

You can group data from multiple attribute values into a single item, called a derived element. You can combine attribute values that have been selected from a list or a visualization into a single derived element. You can also combine data from attribute values and derived elements into a calculation that defines a single derived element.