The organization struggled with inefficient and fragmented reporting across its sales, retail, and customer operations. These gaps slowed decision-making, reduced confidence in insights, and limited the ability to scale analytics across channels.
Fragmented Salesforce data:
Sales, product, and customer data was spread across multiple Salesforce objects, with no centralized analytics layer to unify it.
Manual, spreadsheet-driven reporting:
Reporting relied heavily on manual exports and spreadsheets, leading to delays, inconsistencies, and repeated rework.
Limited real-time visibility:
Leadership lacked live views into sales trends, product performance, and customer behavior, often working with outdated information.
Manual data preparation and reconciliation:
Data cleaning and reconciliation were handled manually, increasing the risk of errors and mismatched numbers.
No unified dashboards:
Sales, store performance, and customer analytics were tracked in silos, with no single source of truth.
Limited advanced analytics:
The lack of a consolidated data foundation restricted capabilities such as forecasting, segmentation, and deeper customer insights.
To address these challenges, our data engineering and BI teams worked closely with the client and their internal teams to implement a robust, enterprise-grade analytics environment using Salesforce and Azure services.
Centralized Salesforce data:
We brought together all Salesforce objects into Azure Data Lake Storage using automated Azure Data Factory pipelines, creating a single, dependable data foundation.
Structured data for analytics:
The data was organized using the Bronze–Silver–Gold model to improve quality, consistency, and readiness for analytics.
Optimized reporting layer:
Transformed datasets were loaded into Azure SQL to support fast, reliable, and scalable reporting.
Retail-focused data marts:
In collaboration with business stakeholders, curated marts were built for retail analytics, including sales performance, product trends, and customer behavior.
Interactive Power BI dashboards:
We delivered intuitive Power BI dashboards with real-time insights, drill-downs, and self-service exploration.
Governance and compliance:
Azure AD, Azure Monitor, and Microsoft Purview were implemented to ensure secure access, monitoring, and data governance.
Future-ready AI foundation:
Databricks notebooks and model orchestration were set up to prepare the platform for upcoming AI and machine learning initiatives.
We executed a scalable, cloud-native technical architecture custom to retail analytics:
Reduced manual reporting effort
Automated pipelines and dashboards cut manual reporting and data extraction work by nearly 85%, freeing teams from repetitive tasks.
Faster access to insights
Automated, real-time dashboards enabled about 65% quicker access to sales and product insights for everyday decision-making.
Improved data accuracy and trust
Standardized data transformations improved overall data accuracy by roughly 75%, increasing confidence in reports and KPIs.
Quicker leadership decisions
With real-time, centralized KPIs in place, leadership decision-making became nearly 50% faster, supported by consistent insights.
Stronger retail visibility
Unified dashboards increased visibility into retail performance, customer trends, and product movement by around 70%.
Higher operational efficiency
By eliminating spreadsheet-based processes, operational efficiency improved by approximately 60% across teams.
Lower dependency on technical teams
Self-service reporting reduced reliance on technical teams for recurring reports by nearly 80%, enabling business users to move faster on their own.
These improvements made in the system helped the client move from manual reporting to a trusted, self-service analytics environment. With Salesforce data centralized on Azure and insights delivered through Power BI, teams now operate with greater speed, while laying a strong foundation to scale analytics further.
Retail & E-commerce
US
End to End Project Lifecycle Management
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