DFT Modeling for Generic CRMs

Introduction

DFT (Dimension, Fact, Time) modeling decouples the quirks of transactional sources from the analytics layer.

The aim is to keep reporting, analytics, and AI models stable as the company upgrades, replaces, or outgrows OLTP systems.

Within DFT, a SPOT (Single Point of Truth) defines the authoritative dataset for each critical business concept. Each SPOT is implemented as a data-warehouse table or view and uses SQL to produce clean, clearly named columns — primary keys, foreign keys, and analysis attributes — so downstream content doesn’t break when sources change.

Example — Customer SPOT: it contains all attributes for customer dimension analysis. Some fields might come from the CRM (Customer ID, Name, Address, Lead Source), while enrichment can come from ERP (e.g., Credit Limit, Payment Terms), spreadsheets, or other systems. The Customer SPOT can also harmonize and deduplicate customers across sources, enabling consistent reporting through consolidations and migrations.

DFT Stage

Stage 1 — Source Mirroring: Creates a clean, fast, low-impact copy of raw source data, preserving lineage and keys.

Stage 2 — Data Warehouse DFT: Builds the primary SPOTs (dimensions/facts) via SQL (or Python), applying cleansing and conformance, and defining clear columns plus surrogate/foreign keys..

Stage 3 — Analytics DFT: Leverages analytics engines modeling (e.g., Domo, Looker, Power BI, Tableau), optimizing joins and aggregations where those tools perform best.

Stage 4 — Report, Dashboard, and KPI Templates: This is an extensive and customizable library of reports, dashboards, and KPIs that plug-and-play on Stage 3.

Stage 1 - Source Mirroring

DataSelf ETL+ is a powerful, easy-to-use tool for source mirroring.

You can design this Stage from scratch (click the following list of ETL+ 430+ No-code Data Sources), or accelerate optimized mirroring with our pre-mapped ETL+ Pre-mapped Source Systems.

Stage 2 - Data Warehouse

Stage 2 is housed in a SQL data warehouse (usually SQL tables/views). It transforms CRM-specific structures into the standardized model used by reports and dashboards (as in DataSelf Stages 3–4 templates).

Changing Stage 2 logic automatically propagates to all downstream reports and dashboards. For example, changing the Gross Profit calculation to absorb freight in Stage 2 will automatically show Gross Profit with freight absorbed in all downstream modeling, reports, dashboards, and AI models.

Stage 3 - Analytics

Implemented in tools such as Power BI and Tableau, the Analytics DFT turns Stage 2 data into fast, ready-to-use datasets.

Stage 3 - DFT Star Schema Example

The following exemplifies how the Sales Invoice DFT tables are linked in DataSelf’s out-of-the-box templates in a star schema arrangement. This model works in reporting tools such as Power BI, Tableau, and Excel.

Stage 3 - DFT Galaxy Schema Example

The following exemplifies how DFT tables are linked in DataSelf’s out-of-the-box templates in a galaxy schema arrangement. This model works in reporting tools such as Power BI, Tableau, and Excel.

A key benefit of a galaxy schema is easy reporting across multiple fact tables in a single report. Click the image to zoom in.

Stage 4 - Report, Dashboards, and KPI Template

This is an extensive and customizable library of reports, dashboards, and KPIs that plug-and-play on Stage 3.

Click the following links to learn more:

• Power BI DFT Templates DFT Templates

• Tableau DFT Templates DFT Templates

Key Words: single source of the truth, ETL templates, data warehouse templates, analytics templates, tds, tdsx, twb, twbx, pibx.