Granularity
Mustafa Qizilbash
‘Open for New Opportunities (Globally), Author, Data & AI Practitioner & CDMP Certified, Innovator of Four 4s Formula, DAC Architecture, PVP Approach
I am sure, most of us know this term but not sure how accurate you are in terms of its importance.
Let’s decode it….
‘The granularity of data means, the levels of data breakdown.’
Generally, people think having all relevant details about an entity is Granularity, but the question is what level of details?
Image: https://www.talon.one/glossary/granularity
For example, details can be name, email, contact number, date of birth, color, blood group, home address, company, role, salary, father name, mother name and so. Is this the lower level of granularity? NO, when we say the lowest level of granularity, we can break down.
·????????Name into First Name, Middle Name and Last Name
领英推荐
·????????Address into Apartment Number, Building Name, Street, Area, City, District, Region, Country, Postal Code etc.
In OLTP systems, granularity must go as low as possible and in OLAP systems, it can depend on the business use case.
Now, when we have established what is the Granularity, let's discuss when it's needed. Data Wrangling, Data Mining, Data Science etc., all use raw data for analysis and analytics. And when we say RAW, the lower the level of granularity can be, the better the model result will be for DSS.
Master Data Management, Metadata Management, Data Lineage, Data Quality, Data Integrity, Data Cleaning/ Cleansing/ Scrubbing, Business Intelligence Dashboards, Data Modeling, Data Classification and Data Clustering etc., all produce results based on the level of granularity in the data model.
The first step towards creating a data model should be to understand the level of granularity the systems or business use case is expecting like date vs DateTime datatype can change the level of granularity. The lower the granularity level is, the more the number of rows will be, and the more drilling and slicing/ dicing can be planned.
Don’t miss to go through topic i.e., Cardinality.
Cheers.
Head of Operations at Ortelius, Transforming Data Complexity into Strategic Insights
2 年Another great post. A major challenge for most people (and systems) is to understand a) that different levels of granularity exist b) how to move between them. Product is a neat example of this. Everyone understands what you mean by product. Product is a stableware in all systems, yet the level of granularity and the type of data and attributes you keep in each system differs widely. ?? Conceptually a product is a product, ?? In a PLM setting you look at Product from an Engineering perspective ?? In a ERP setting you look at Product from a Finance perspective ???? In a PIM setting you look at Product from a Sales perspective and so forth. The key is to understand in which level of granularity you are and what levels (taxonomy) exists with a higher and lower level of granularity.