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Knowledge Base Articles

Choosing the Right Data Model for You

Introduction Customers often run into the question of what data model they should use (an Elasticube or a Live Model). The following article presents a few of the aspects you should consider when choosing between them. Sisense recommends you discuss ...

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Modelling Aggregate Facts

Analytical Need There are cases where our data arrives in aggregate form, like in Google Analytics (where the lowest granularity level is daily). If we wish to free up clutter in the dashboard and prevent the user from figuring out which field comes ...

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Build A Flattened Hierarchy From 2 Levels

Analytical Need In many cases a hierarchy appears in the data structured as 2 levels - A parent and a child. The business user would like to see the entire hierarchy and analyze the data according to the different levels, but can't do that since they...

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Calculate Average Time Between Transactions

Introduction Calculating transaction frequency for a given dimension is a common requirement across many verticals. e.g. Average Time Between Purchases by a Customer. Average Time between support tickets etc. While this can be calculated in SQL it ca...

Calculating Values For The Most Recent Date

The point of this article is to provide a method to a value associated with the most recent data in a date selection. Implementation Step 1: in the Elasticube, create a field transforming the date field into an integer using the formula below. Then r...

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Elasticube Modeling With Data Security: Best Practices

Adding Data Security to an existing Elasticube Model can have little to great effect on the performance of the system. It is important to already have planned the Elasticube Model with the intention to apply Data Security in the future. This article ...

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Formulas Creating "Random Paths" Between Dimensions

Problem Advanced formulas can sometimes return wrong results (or no results at all) when combining dimension-affiliated calculations** with dashboard or widget filters based on a second dimension. Certain calculations may include backstage joins betw...

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Creating 'From-To' Date using BloX

Analytical Need Attached below is a BloX template. A common request is to have a 'From To' input at the top of the dashboard to affect a calendar range selection of dates. To achieve this need we can utilize BloX's form capabilities. Picking a date r...

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Fiscal Year Adjustments

Analytical Need Customer's fiscal year does not comply with the Georgian 12-month calendar and starts on a different date than January 1st. Example: Customer’s fiscal year is from April 1st to March 31. This means that April should be flagged as the ...

Many to Many by Design (Relationship Table)

Analytical Need There are cases where we need to define an association between two entities. This association is comprised of the entities and not by each of them separately. For example, students & courses. We need to have a list of each student, a ...

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Modelling Different Granularity Levels

Analytical Need There are many cases where our data comes in with different granularity levels. For instance, Actual vs. Target where the actuals are at the product level and the targets are per sub category (higher hierarchy of the product). How can...

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Analyze Period over Period - MoM / WoW / DoD

Analytical Need There are cases where we want to analyze our measures as a comparison between periods of time. For instance, we want to know how did our measures behave on January this year vs. previous year. This means that we have to put down in ou...

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Calculate MTD, QTD and YTD Values

Analytical Need A common requirement in data analysis is to be able to view and compare slices of data from different time periods to current date. Business applications for this vary, from identifying trends to predicting future behavior, requiremen...

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Market Basket Analysis

Analytical Need Market Basket Analysis is a data mining technique that is widely used to identify consumer patterns, such as which items are purchased together, or how the purchase of one item affects the likelihood of another item being purchased. O...

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Data Relationships: Introduction to a Many-to-Many

A relationship between tables specifies the field/s which the 2 tables have in common. In Sisense, we create relationships between tables in the ElastiCube. Poorly modeled data relationships can lead to one or more of the following: Complex data sets...