What are fact tables & why do data analysts use them?

What Are Fact Tables?

Not all data scientists use fact tables. But many do. Especially working in environments with data warehouses or business intelligence (BI) tools.

Here's a breakdown of why and when fact tables are used - taking a close look at Notitia's data and technology partners Qlik and Microsoft.

A fact table, in a data warehouse, is a central table that stores quantitative data, typically numeric measures.

Fact tables are invaluable for businesses who use, analyse and report on key performance indicators (KPIs), sales metrics, customer behaviour, and other quantifiable aspects that help to improve operations.

By centralising and organising this data, fact tables provide a foundation for extracting actionable insights.

These insights empower businesses to make informed decisions and support strategic planning, ensuring that operations are aligned with data-driven goals.

Fact tables are a core component of a star schema or snowflake schema in data warehousing. They typically store quantitative data for analysis and characterised by:

• Measures: Quantitative data such as sales revenue or number of units sold etc.
• Foreign Keys: References to dimension tables that provide context (e.g. date, product, customer).

When Data Scientists Use Fact Tables

Business Intelligence and Reporting: Data scientists working on BI tasks or with BI tools (such as Qlik or Microsoft Power BI) often rely on fact tables to pull aggregated data for dashboards and reports.

ETL Processes: In environments where data scientists are involved in designing or maintaining ETL (Extract, Transform, Load) processes, they may work with fact tables to ensure accurate and efficient data transformation and loading.

Data Warehousing: When data scientists are involved in building or querying data warehouses, they frequently interact with fact tables to retrieve historical data for trend analysis, predictive modelling, and other analytical tasks.

Performance Optimisation: Fact tables, with their indexes and pre-aggregated data, are optimised for query performance. Data scientists working with large datasets may use them to reduce query times and improve the efficiency of their analyses.

When Fact Tables Might Not Be Used

1. Non-Relational Data Sources: In projects involving non-relational databases, data scientists may not encounter traditional fact tables.
2. Unstructured Data: For tasks involving unstructured data (e.g. text analysis, image processing), fact tables are not applicable. Data scientists in these areas might rely more on data lakes or specialised storage solutions.
3. Small-scale Analysis: In smaller projects or startups, data scientists might use simpler data structures or direct analysis on raw data without the need for a complex data warehousing solution.
4. Real-time Data Processing: For real-time analytics, streaming data platforms might be used instead of traditional data warehouses, bypassing the need for fact tables.

Fact tables are a common tool in the data warehousing and BI landscape, but their usage depends on the specific context and requirements of the task. Data scientists working in traditional BI or large-scale data environments are more likely to use fact tables, while those in non-relational, unstructured, or real-time data contexts may not.

Fact tables are a fundamental component in the data warehousing and BI landscapes, playing a pivotal role in organising and analysing quantitative data. They enable businesses to monitor key performance indicators, track sales metrics, understand customer behaviour, and more.

By providing a structured format for data storage, fact tables facilitate efficient data aggregation, performance optimisation, and insightful analysis.

Data scientists working with tools like Qlik Sense, QlikView, Microsoft SQL Server, and Power BI leverage fact tables to create comprehensive reports and dashboards that support strategic decision-making.

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Qlik Sense and QlikView Users

Qlik users often use fact tables, particularly when working with Qlik's data integration and business intelligence tools such as Qlik Sense and QlikView.

Qlik and Data Warehousing

Integration with Data Warehouses: Qlik Sense and QlikView can connect to various data sources, including traditional data warehouses where fact tables are a key component. Users often pull data from these warehouses to create visualisations and analyse data.

ETL and Data Modelling: Qlik includes tools for data extraction, transformation, and loading (ETL). When setting up data models in Qlik, users typically define fact tables to store quantitative data and dimension tables for contextual information, mirroring the star schema often used in data warehouses.

Benefits of Using Fact Tables in Qlik

1. Performance Optimisation: Fact tables are optimised for query performance, which is crucial when dealing with large datasets. By using fact tables, Qlik applications can efficiently handle large volumes of data and complex queries, providing faster response times for dashboards and reports.
2. Data Aggregation and Analysis: Fact tables aggregate large amounts of transactional data, making it easier to perform analysis. Qlik’s associative model benefits from the structured format of fact tables, enabling users to perform drill-downs and detailed analysis quickly.
3. Simplified Data Relationships: In Qlik, maintaining a clear separation between fact tables and dimension tables simplifies data relationships and reduces complexity in the data model. This leads to more intuitive data exploration and accurate reporting.

When Fact Tables Might Not Be Used in Qlik

1. Direct Data Analysis: In some cases, Qlik users may choose to analyse data directly from the source systems without extensive preprocessing or the use of fact tables, especially in smaller-scale deployments.
2. Non-Relational Data Sources: If Qlik is used to connect to non-relational or NoSQL databases, the traditional concept of fact tables may not apply. However, Qlik’s associative model can still effectively handle such data structures.

Typical Use Cases for Fact Tables in Qlik Ecosystem

Financial Reporting and Analysis
Storing comprehensive financial data such as revenues, expenses, and profits, which are analysed in Qlik Sense and QlikView to generate detailed reports and insightful dashboards.

Sales and Marketing Analytics
Holding transactional data on sales volumes, customer interactions, and marketing campaign results, enabling users to track product performance, monitor sales targets, and evaluate marketing effectiveness.

Microsoft SQL Server, Azure & Power BI users

Here’s how data analysts using Microsoft's data management and business intelligence tools use fact tables:

Microsoft Tools and Fact Tables

1. SQL Server: Microsoft SQL Server is a relational database management system widely used for data warehousing. Fact tables are a fundamental component of data warehouses built on SQL Server, used to store and manage large volumes of transactional data.
2. Azure Synapse Analytics: Azure Synapse Analytics (formerly SQL Data Warehouse) is Microsoft's cloud-based data warehousing solution. Fact tables play a crucial role in organising data for efficient querying and analysis in Synapse.
3. Power BI: Power BI is Microsoft's powerful business intelligence tool. While Power BI itself doesn’t create fact tables, it connects to data sources that include fact tables, such as SQL Server and Azure Synapse. Fact tables provide aggregated data that Power BI uses for visualisations, reports, and dashboards.

Benefits of Using Fact Tables in Microsoft Tools

1. Optimised Query Performance: Fact tables are designed to handle large volumes of data and complex queries efficiently. This optimization is crucial for performance when using tools like SQL Server and Power BI.
2. Data Integration and ETL Processes: Microsoft tools such as SQL Server Integration Services (SSIS) and Azure Data Factory facilitate ETL processes where fact tables are used to consolidate and transform data from various sources into a unified format for analysis.
3. Structured Data Analysis: Fact tables, often part of a star schema, provide a structured way to store and access data. This structure is beneficial for analytical queries, reporting, and BI tasks, enabling users to drill down into data and derive insights.

Typical Use Cases for Fact Tables in Microsoft Ecosystem

1. Financial Reporting: Fact tables store financial metrics such as revenues, expenses, and profits, which are then analysed in Power BI for financial performance reporting.
2. Sales and Marketing Analytics: Fact tables hold sales data, customer interactions, and marketing campaign results. Analysing this data helps in understanding market trends and customer behaviour.
3. Operational Analytics: Fact tables are used to store data on operational metrics such as production rates, inventory levels, and supply chain logistics, aiding in operational efficiency and decision-making.

When Fact Tables Might Not Be Used in Microsoft Tools

1. Small-Scale or Ad Hoc Analysis: For smaller datasets or one-time analyses, users might work directly with raw data in Excel or other tools without the need for fact tables.
2. Unstructured Data: For tasks involving unstructured data (e.g. text, images), fact tables are not applicable. Microsoft tools like Azure Data Lake Storage or Azure Cosmos DB might be used instead.

Real-Time Analytics: In scenarios requiring real-time data processing and analytics, solutions like Azure Stream Analytics might be used, where the traditional data warehousing approach with fact tables may not be suitable.

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