From Business Data to Decision-Ready Analytics
DataNorth Analytics combines data ingestion, cloud data platforms, transformation, dimensional modeling and Tableau to turn fragmented business data into reliable, decision-ready management analytics.
One governed analytics foundation. Multiple management perspectives.
How an Analytics Solution Is Delivered
A practical four-stage approach designed for SMEs that need better management visibility without building a large internal data team.
01
Understand
Define the management questions, KPIs, reporting requirements and key business decisions.
Business goals · KPIs · Reporting needs
02
Connect
Bring together existing operational, sales and financial data from spreadsheets, databases and business systems.
Excel · CSV · ERP · Accounting · CRM · APIs
03
Build
Create the cloud data platform, transformation pipelines, dimensional model and data quality controls.
Python · PostgreSQL · AWS · SQL
04
Deliver
Publish management dashboards that translate reconciled business data into decision-ready insights.
Tableau · Executive Analytics
End-to-End Analytics Architecture
From Raw Data to an Analytics-Ready Model
The dashboard is only the final layer. Behind it is a structured pipeline that converts raw business data into governed analytics.
01
SOURCE
Excel · CSV · ERP · CRM Operational Databases
02
INGESTION
Python · Pandas · SQLAlchemy
03
DATA PLATFORM
AWS RDS · PostgreSQL · Raw Data Layer
04
ANALYTICS MODEL
Dimensional Model · Dimensions & Facts · Business Logic · Curated SQL Views
05
DASHBOARD
Tableau · Management Dashboards
One governed data foundation. Multiple management perspectives.
Sales, Finance and Operations analytics use the same modeled and reconciled data platform, reducing conflicting definitions across management reporting.
raw_client_xxx
├── customers
├── products
├── sales
├── orders
├── invoices
├── payments
├── finance_monthly
├── opex_monthly
└── inventory_monthly
Raw tables preserve the incoming business data as closely as possible before management logic is applied.
mart_client_xxx
├── dim_date
├── dim_customer
├── dim_product
├── dim_salesperson
├── dim_opex_category
├── fact_sales
├── fact_finance_monthly
├── fact_opex_monthly
└── fact_inventory_monthly
The mart applies consistent business definitions and creates a dimensional model optimized for management analytics and reporting.
Reconciliation and Data Quality
Management reporting should be traceable back to underlying business transactions. The ETL process validates key financial and operational relationships before data reaches Tableau.
Sales Revenue
↕
Finance Revenue
✓
Finance revenue reconciles to the aggregated sales transactions.
Detailed OPEX
↕
Total OPEX
✓
Itemized operating expenses reconcile to the consolidated financial summary.
Gross Profit − OPEX
↓
EBITDA
✓
EBITDA is derived consistently from the same reconciled finance layer.
Why this matters: executives see one consistent set of numbers across Sales, Finance and Operations.
Designed for Governed Data Separation
Analytics environments can be structured to maintain clear separation between raw source data and curated reporting layers, with controlled access to approved analytics datasets.
AWS RDS PostgreSQL
raw_client_xxx
↓mart_client_xxx
↓approved_reporting_views
Each client receives access only to their approved analytics layer. Raw ingestion data remains separated from the curated reporting layer.
From Platform to Management Decisions
Sales Management
- Revenue growth
- Customer performance
- Product profitability
- Sales execution
Financial Management
- Profitability
- Budget performance
- Cost control
- Liquidity
DataNorth Analytics 