DFT+ for eCommerce / Retail - Source Agnostic

Overview — DFT+ for Distribution Companies

Distribution companies operate across multiple operational systems — ERP, WMS, CRM, eCommerce, EDI, logistics, and supplier platforms. Each system captures transactions differently, creating inconsistencies that make reporting fragile and difficult to scale.

DFT+ (Dimension, Fact, Time) modeling separates operational system complexity from the analytics layer, providing distributors with stable, trusted data for reporting, planning, and AI-driven decisions.

Within DFT+, a SPOT (Single Point of Truth) defines the authoritative dataset for each core distribution concept — such as Customer, Item, Warehouse, Vendor, Sales Order, Inventory Position, and Revenue, Cost of Sales, GP, and Quantity metrics.

Each SPOT is implemented as a curated data-warehouse table, view, or calculation that uses SQL to deliver clean, clearly named fields — primary keys, foreign keys, and analysis attributes — ensuring dashboards, KPIs, and AI models remain stable even when source systems change.

Example — Customer SPOT

The Customer SPOT centralizes all attributes required for customer and channel analysis.

Data may originate from multiple sources:

• ERP → Customer ID, credit terms, pricing class, billing information

• CRM → industry, lead source, account owner

• eCommerce → channel classification and digital behavior

• EDI systems → trading partner identifiers

• External datasets → territories, market segmentation, or enrichment data

The SPOT harmonizes and deduplicates customers across systems, enabling consistent analysis of:

• customer profitability

• channel performance

• sales territory reporting

• acquisition vs. retention trends

• consolidated reporting after acquisitions or system migrations

Why DFT+ Matters for Distribution

Distribution businesses constantly evolve:

• ERP upgrades or replacements

• Warehouse expansions

• Multi-company consolidations

• New sales channels (eCommerce, marketplaces, EDI)

• Supplier and logistics integrations

One of the primary goals of DFT+ is to keep reporting, analytics, and AI models stable through these changes.

Instead of rebuilding dashboards every time systems evolve, DFT+ preserves a consistent analytics foundation — allowing distributors to focus on improving operations, inventory turns, service levels, and margins rather than fixing reports.

Business Outcomes for Distributors

DFT+ enables reliable analysis across:

• Inventory availability and turns

• Fill rate and service performance

• Gross margin by item, customer, or channel

• Supplier performance and lead times

• Order fulfillment efficiency

• Demand trends and forecasting

By establishing SPOT datasets, distribution companies gain a durable analytics foundation that supports growth, system change, and data-driven decision making.

DFT+ Stages

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

Stage 2 — Data Warehouse: 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: 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 ERP-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 reports, dashboards, and AI models.

Stage 2 DFT+ - A SQL Statement Example

Example from ETL+ transformation: mapping ERP tables to the target _D_Item DFT+ table.

Stage 3 - Star and Galaxy Schemas

Implemented in conjunction of MS SQL, Power BI and/or Tableau, the DFT+ turns Stage 2 data into fast, ready-to-use datasets.

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.

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 - Downstream (Report, Dashboard, and KPI Templates)

This is comprised of KPI+, our extensive and customizable library of report, dashboard, and KPI templates that plug-and-play to Stage 3.

Click the following links to learn more:

• Power BI KPI+ Templates

• Tableau KPI+ Templates

• Packaged BI with AI by source system.

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