# ElastiCube for Advanced Business Scenarios

> examples of more advanced scenarios, and the recommended methods for implementing the required business logic.

*Source: https://docs.sisense.com/main/SisenseLinux/elasticube-for-advanced-business-scenarios.htm*

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Last updated: June 11, 2026

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This section provides examples of more advanced scenarios, and the recommended methods for implementing the required
business logic.

The examples are categorized into the following data manipulation methods: Integrating, Reformatting and Enhancing.

## Integrating Data

Integrate and merge data from different sources into a single ElastiCube structure by identifying common keys between
the different tables. Proper planning is important for merging the data. It is important to avoid creating unnecessary
relationships, but at the same time, make sure you do not have any many-to-many relationships. Examples include:

- [Creating a Common Date Selection](https://docs.sisense.com/main/SisenseLinux/integrating-data.md#Creating): Create a common date field from
  multiple date sets (from multiple data sources), and still keep the ability to use each original date field
  individually.
- [Financial Reporting](https://docs.sisense.com/main/SisenseLinux/integrating-data.md#Financia): Bring in an additional data source to help
  analyze data from transaction systems. For example, Financial GL data will include all transactions, but may not
  have all the income statement or balance sheet reporting definitions.
- [Looking Up Values](https://docs.sisense.com/main/SisenseLinux/integrating-data.md#Looking): Look up a value from one table and bring it into
  another table. For example, knowing how much a marketing campaign costs versus the sales opportunity amount is an
  important KPI to measure.

## Reformatting Data

Reformat field data to free space, and make fields more readable and usable. For example, convert a date field to
numeric, or reduce the precision of real numbers. You can reformat fields within the ElastiCube using a custom SQL
expression.

- [Numeric Representation of Date Fields](https://docs.sisense.com/main/SisenseLinux/reformatting-data.md#Numeric): Create a date table that is
  represented by a numeric representation instead of a date field to improve the query performance, as well as
  provide more flexibility, including the ability to filter a date range.

## Enhancing Data

[Enhancing Data](https://docs.sisense.com/main/SisenseLinux/enhancing-data.md) by adding attributes / records that did not exist in the original
data source.

- [Enhancing Data](https://docs.sisense.com/main/SisenseLinux/enhancing-data.md): Derived facts are additional facts that we calculate while
  importing or delivering the data.
- [Calendar vs. Fiscal Year](https://docs.sisense.com/main/SisenseLinux/enhancing-data.md#Calendar): Align a fiscal calendar with a Gregorian
  calendar.
- [Time Zone Conversion](https://docs.sisense.com/main/SisenseLinux/enhancing-data.md#Time): Use a source table to convert dates and times from
  different time zones into a uniform data set.
- [Currency Conversion](https://docs.sisense.com/main/SisenseLinux/enhancing-data.md#Currency): Convert one currency into another using custom
  fields and a currency exchange rate table.
- [Current vs. Previous Period for Specific Date Range](https://docs.sisense.com/main/SisenseLinux/enhancing-data.md#Current): Compare data
  such as sales between a current period and a past period.
- [Calculating the Number of Open Orders per Day](https://docs.sisense.com/main/SisenseLinux/enhancing-data.md#Calculat): Check open sales
  orders where the order has been placed, but has not yet been delivered.
- [Slowly Changing Dimensions](https://docs.sisense.com/main/SisenseLinux/enhancing-data.md#Slowly): Transactional data does not usually
  change, however the data that describes the associated dimensions may change. See how to manage dimensions that
  may be updated with new values within the data warehouse at different points in time.