Table of Contents
What is the importance of information granularity?
The greater the granularity, the deeper the level of detail. Increased granularity can help you drill down on the details of each marketing channel and assess its efficacy, efficiency, and overall ROI.
What is the granularity of a table?
The granularity of a table is the finest level of detail it contains, while creating a fact table it is of vital importance to understand which dimensions will form that fact table or in other terms what is the lowest level of detail that can be fetched from the fact table.
What does granularity of the fact table refer to?
The GRAIN or GRANULARITY of the fact table refers to the level of detail of each row in the fact table.
Why defining the grain of a fact table is important?
Fact tables provide the (usually) additive values that act as independent variables by which dimensional attributes are analyzed. Fact tables are often defined by their grain. The grain of a fact table represents the most atomic level by which the facts may be defined.
What is dirty data in data analytics?
In a data warehouse, dirty data is a database record that contains errors. Dirty data can be caused by a number of factors including duplicate records, incomplete or outdated data, and the improper parsing of record fields from disparate systems.
Why granularity is an important factor to be considered during the design of a fact table?
Therefore, when you define the granularity of the fact table, you identify the dimensions of the data model. The granularity of the fact table also determines how much storage space the database requires.
What is low granularity data?
Granularity refers to “the level of detail or summarisation of the units of data in the data warehouse”. The low level of granularity contains high level of detail and the high level of granularity contains low level of detail. A diverse category of analytical processing uses various levels of granularity.
What may happen if the grain of the fact table was not designed properly?
If the grain isn’t clearly defined, the whole design rests on quicksand. Discussions about candidate dimensions go around in circles, and rogue facts that introduce application errors sneak into the design. Declaring the grain means saying exactly what a fact table record represents.
Why is dirty data such a big deal?
The Impact of Dirty Data Dirty data results in wasted resources, lost productivity, failed communication—both internal and external—and wasted marketing spending. In the US, it is estimated that 27\% of revenue is wasted on inaccurate or incomplete customer and prospect data.
What is wrong data called?
Dirty data, also known as rogue data, are inaccurate, incomplete or inconsistent data, especially in a computer system or database.