One-to-one (1:1) relationships in data modelling are relatively uncommon, as they often indicate that two tables could be combined into one. This blog explores the reasons behind the rarity of 1:1 relationships, including redundancy, efficiency, and the lack of clear data segmentation. While they can be useful for isolating sensitive information, most scenarios benefit more from one-to-many or many-to-many relationships. At KPDI Analytics, we help businesses optimise their data models for better performance and accuracy.

Parent and child tables are essential in data modelling, defining relationships that keep data organised and accessible. A parent table holds unique records, while a child table contains detailed, linked data. This blog explores how these structures work and why they’re crucial for accurate analytics. At KPDI Analytics, we help businesses design reliable data models that turn raw data into meaningful insights.

Orphaned records—entries without a matching reference—can disrupt data accuracy and mislead insights. They often occur due to data entry errors, system migrations, or a lack of referential integrity. At KPDI Analytics, we help businesses prevent orphaned records through data validation, referential integrity solutions, and seamless system integration, ensuring your data models are accurate and insights reliable.

Orphaned records can disrupt data accuracy, leading to incomplete reports and slower dashboards. Referential integrity helps by ensuring that relationships between tables remain valid, preventing orphaned records and enhancing data quality. In this post, learn how enforcing referential integrity in Tableau keeps your data consistent and insights reliable. At KPDI Analytics, we specialise in building structured, accurate data models that empower smarter business decisions.

Referential integrity is a key aspect of data modelling, ensuring that table relationships remain valid and accurate. Setting referential integrity in Tableau helps prevent orphaned records, optimise performance, and produce accurate insights. In this guide, learn when to enforce or leave referential integrity unenforced, how it impacts data aggregation, and why it’s essential for reliable reporting. At KPDI Analytics, we create efficient data models that deliver fast, actionable insights, supporting smarter business decisions.

Cardinality is a key concept in data modelling that defines relationships between tables, like one-to-many or many-to-many. Properly setting cardinality in Tableau ensures data accuracy, optimises performance, and prevents issues like duplicate records. In this guide, learn how to identify, set, and validate cardinality in your data model to unlock reliable insights. KPDI Analytics provides expertise in structuring data models that support clear, actionable analytics for smarter decision-making.

Creating a robust data model in Tableau is the key to unlocking reliable, clear insights from complex datasets. This guide covers essential steps, from structuring data sources to optimising performance, ensuring your Tableau dashboards are both accurate and efficient. At KPDI Analytics, we specialise in transforming data into a strategic asset, empowering your business with insights that drive smarter decisions.

Ever wondered why data isn’t stored in one big table? In our latest post, Pi Buddy and Pia Maven’s unique workspaces illustrate how structuring data across multiple tables brings clarity, consistency, and efficiency to data modelling. Discover how separating data into tables with relationships, like our Character and Workspace tables, keeps information organised and ready for growth in the Pi Analytics Lab. Dive in to see why this approach is essential for scalable data management!