Building a Business Analytics Dashboard for Multi-Location Operations
Overview
Connect Aura Tech built a centralized analytics layer for a confidential multi-location business: scheduled pipelines, a data warehouse, standardized semantic metrics, and 4 dashboards across 4 departments, with automated alerts. The published role attribution is limited to CFO. Individual dashboard names are not published.
Project Snapshot
The Business Challenge
The business had reporting information spread across multiple operational and marketing systems. The objective was to centralize data, standardize business metrics and provide role-based dashboards with automated alerts.
Data Architecture
The architecture is an ELT / data warehouse design with standardized semantic metrics and role-based BI dashboards. The system tracks pipeline health, data freshness, KPI status, and dashboard usage.
The Solution
Connect Aura Tech implemented an ELT / data warehouse architecture with standardized semantic metrics and role-based BI dashboards. Source systems feed scheduled data pipelines, data is cleaned, transformed and modelled, then published to role-based dashboards with automated alerts.
Data Workflow
Source systems
Data is drawn from multi-location POS systems, TallyPrime, Zoho CRM, Meta Ads, and Google Ads.
Scheduled data pipelines
Scheduled pipelines ingest source data into the warehouse layer.
Clean / transform / model
Ingested data is cleaned, transformed and modelled into standardized semantic metrics.
Role-based dashboards
The modelled metrics are published as 4 dashboards across 4 departments.
Automated alerts
Alerts can be sent by email, WhatsApp, and Slack.
Results
| Metric | Result |
|---|---|
| Manual reporting | ~20 hours/week saved |
| Data refresh | ~60-minute refresh cycle |
| Dashboards | 4 dashboards across 4 departments |
| Standardized metrics | 25+ |
Dashboards and Reporting
The delivery includes 4 dashboards across 4 departments. Individual dashboard names are not published. Reporting is role-based, with automated alerts over email, WhatsApp, and Slack.
Technology Stack
Data work was implemented in Python. Ingestion and orchestration used Airbyte and Apache Airflow. Google BigQuery served as the data warehouse, with PostgreSQL as the operational database and dbt for transformation and modelling. Microsoft Power BI provided the business-intelligence dashboards. Integrated source systems were multi-location POS systems, TallyPrime, Zoho CRM, Meta Ads, and Google Ads. Alerts used email, WhatsApp, and Slack.
Technologies
Limitations / Measurement
Results on this page are limited to the supplied project figures: about 20 hours a week of manual reporting saved, a ~60-minute refresh cycle, 4 dashboards across 4 departments, and 25+ standardized metrics. No revenue, profit, ROI, rupee cost-saving, lead, customer, or conversion figure is added. A named-client testimonial is not published because publication permission is not established in this repository.
Client Anonymity
This is an anonymized verified-client case study. The client is a confidential multi-location business and is not named. The published role attribution is limited to CFO. No written client-publication authorization for named identity is held in this repository.
FAQs
How many dashboards were delivered?
4 dashboards across 4 departments. Individual dashboard names are not published.
How long did the project take?
9 weeks.
Which data sources were connected?
Multi-location POS systems, TallyPrime, Zoho CRM, Meta Ads, and Google Ads.
Is the client named on this page?
No. The client is a confidential multi-location business. The published role attribution is limited to CFO.
Related Services
Discuss a Similar Analytics Dashboard
Talk with Connect Aura Tech about centralized analytics and role-based dashboards. Results on this page are limited to the supplied project figures.
Get Free Strategy Session