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Many-To-Many / Multivalued Dimension

There are situations when your data needs to represent a many to many relationships such that your dimension members are at a lower grain than related facts; aka multivalued dimension.  In these cases, a single fact record should relate to multiple d...

intapiuser by Community Team Member
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Count Daily Workload / Open Tickets Per Date

Overview:Many times, we'd like to analyze workload over time. The following article describes a modeling solution approach to an often asked question: "How many active items are there per day?" Analytical Need:Counting the number of open tickets per...

intapiuser by Community Team Member
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Using "Now" In A Dashboard Function Date Comparison With R

OverviewThis post offers food for thought in regards to how R can be used to retrieve the real-time datetime with the RDouble function. It shows how to run date comparisons using the R syntax. ChallengeNormally if you want to compare a datetime to "...

intapiuser by Community Team Member
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Cohort Analysis

Data PreparationOrange: Borrowed from another table via lookup() Blue: Custom columnIf the first visit date is available in the dimension:Lookup the user’s First Visit Date from the dimension table in a custom column of the fact table based on the us...

intapiuser by Community Team Member
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Automatically Sort Values Into Ranges Of Known Size

If you know the size your range/bucket will increment by, this quick method will help you automatically sort your values into their respective range.This is useful when you cannot create or maintain ranges using a CASE statement. In the below example...

intapiuser by Community Team Member
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Convert Quad Decimal IP Addresses To IP Integers

This expression will convert IP addresses in the quad decimal format of 'www.xxx.yyy.zzz' to integers that can be used for further analysis (eg. cross referenced to a geo-location).For example, it would convert the string '66.249.66.1' to the integer...

intapiuser by Community Team Member
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Cheat Sheet - Detecting Incorrect Data

Data is a beautiful mess. As data heroes, we spot the mess and get rid of it to highlight the beauty. Here are a couple useful tricks to detect incorrect data. We will assume a table that describes the number of meows made by each cat in each box, o...

intapiuser by Community Team Member
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Creating Records For Non Existent Transactions

IntroductionSometimes when your analyzing sales you may want to look at products that are not being sold. Or maybe for a service provider you may want to see territories where you have no subscriptions. Unfortunately, data typically does not contain ...

intapiuser by Community Team Member
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Calculating Week Number Of Quarter

IntroductionThis Post Explains how to create a new column that will calculate the week number of a Quarter.Business CaseIn many cases Business Analyst would like to track trends over time aggregated by quarters but broken down to the week within each...

intapiuser by Community Team Member
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Filter dashboard on latest year based on the Elasticube data

 Analytical NeedFact tables may include a wide date range. For instance, a table can have historical dates without any transaction of the current year,The Need : filter the dashboard based on the latest year in the data   Modeling Challenge We need t...

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The Invisible Many to Many

For general information on types of data relationships, potential complications and how to resolve them, see the following articles:  Data Relationships: Introduction to Many-to-ManyData Relationships: Check Type of relationshipData Relationships: O...

intapiuser by Community Team Member
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Analyzing Measures Between Two Dates

Analytical Need In many use-cases, there’s a need to measure entities throughout their life cycle which spans from their ‘Start’ date to ‘End’ date. For example insurance company wants to count how many active policies there are on any given day. ...

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Data Relationships: Many-to-Many Relationship Resolutions

For general information on types of data relationships, potential complications and how to resolve them, see the following articles:  Data Relationships: Introduction to Many-to-Many Data Relationships: Check Type of relationshipData Relationships: ...

intapiuser by Community Team Member
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Creating Cohort Analysis With Sisense

 Analytical NeedCohort is a group of items who share common characteristic over a certain period of time. The Cohort analysis helps us identify the relationship between their characteristic and their behavior along relative periods of time.  Modeling...

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Generating a Numeric Key

Introduction  Surrogate Keys are mainly used for creating a single identifier for multiple dimensions, mainly when building a Key Table. A text key can be easily generated by concatenating the text and delimiters, however, text can become very long a...

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Advanced Data Security - Organizational Hierarchy

 Analytical NeedData security is implemented when there is a need to prevent the users from seeing the entire set of data. Each user only needs to see a data set based on permissions that is defined in Sisense Web. Here we will discuss how to solve a...

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Service Level Analysis

 Analytical Need  When we want to measure the service level we provide our customers we can do that using many different KPIs. These differentiate between industries and management decisions. This modeling use case is focused on employees in stores...

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