Analytics & AI Transformation: Real-World Success Stories
Discover how NeenOpal empowers businesses with AI & Data-driven transformation.
12 case studies
AI-Augmented ETL for Automating Multi-Portal Retail Reporting on AWS
Madhumita Giribabu
A consumer beauty and personal care brand needed to bring its Walmart and Amazon supplier and performance data into a single AWS RDS PostgreSQL database. Walmart's data was split across a standard supplier API and three separate reporting portals (Retail Link, Scintilla, and Supplier One), each with its own authentication, file formats, and update cadence. NeenOpal designed a hybrid ETL platform with two ingestion paths running in parallel: an orchestrated API pipeline for the data Walmart exposes directly, and an AI-driven report retrieval pipeline for the data that fell outside the scope of the client's existing API access. Both paths converge on a shared Glue transformation layer, a common S3 staging area, and a single PostgreSQL database, with a separate one-time Spark job bringing in Amazon Vendor Central's historical data. The result is a single, config-driven system rather than a set of one-off integrations.
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Tableau Dashboard Development for a US Insurance Organization: 4 Data Sources Unified
Vishal SharmaOur client, an insurance organization with field teams spread across every US state, partnered with NeenOpal to replace a manual, spreadsheet-driven reporting process with a consolidated Tableau dashboard environment governed by row-level security. Operational data lived in four separate systems covering Airtable, Salesforce, Google Analytics 4, and Google Search Console, and bringing them together meant exporting and stitching the data by hand, which left leadership without a single reliable view of performance. NeenOpal consolidated all four sources into an Azure SQL Server layer, reviewed and corrected the data quality issues that surfaced along the way, and delivered three Tableau dashboards across seven screens that refresh automatically every day. Row-level security was implemented across all fifty states and the independent districts, driven by an entitlement model the client maintains through a single Google Sheet, so every representative sees the states they are responsible for and nothing more.
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Unifying Multi-Region Sales Pipeline Analytics with Power BI and Power Query
Kunwar ChawlaOur client, a global healthcare company with commercial operations across Africa and Western Europe, partnered with NeenOpal to build a unified sales pipeline dashboard spanning more than six regional teams across two continents. Pipeline data was managed in isolation by each region, reported in inconsistent formats, and pulled from different systems including Salesforce and SAP, leaving leadership without a consolidated view of opportunities, orders, and revenue. NeenOpal designed and delivered a centralized sales pipeline dashboard in Power BI, powered by an automated Excel and Power Query ETL layer, that standardizes data from every region into one live view. The solution tracks each opportunity from lead to shipped order, benchmarks performance against multiple internal targets, forecasts revenue from existing customers, and lets non-technical users refresh the entire report in one to two clicks.
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Unified Cross-Platform Intelligence Across 20+ Data Sources
Urwah FarooqiA leading dental industry media company partnered with NeenOpal to build a centralized marketing intelligence infrastructure from the ground up. With audience data, campaign performance, CRM activity, webinar engagement, and learning platform metrics scattered across more than a dozen disconnected systems, the client lacked a unified view of marketing performance and customer engagement. NeenOpal designed and delivered a fully automated, cloud-native data platform that ingests data from 20+ marketing, CRM, and engagement platforms into Google BigQuery enabling reliable, weekly-refreshed reporting and cross-platform analytics from a single source of truth.
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Cross-Tenant Semantic Model Migration
Preetibarna PandaReplicating a semantic model across Microsoft Fabric tenants was a major roadblock for our Edtech client. Since semantic models are tightly coupled to their tenant, workspace, and Lakehouse, migration became highly complex, especially when advanced DAX logic and metadata had to be preserved. To overcome this, we built a reusable solution using .bim files and a Fabric Notebook. This approach automated the process and enabled seamless transfer of the model from Lakehouse A in Tenant A to Lakehouse B in Tenant B.
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Automated Ticket Invoice Processing & Analytics Using AWS Textract and Redshift
Livin LarsanA media and event-driven organization managing high-volume ticket sales receives periodic ticket invoices via email from multiple vendors and booking partners. These invoices arrived in semi-structured and unstructured formats (PDFs, scanned images, email attachments), making manual processing time-consuming, error-prone, and non-scalable. To address this, we designed and implemented a fully automated, serverless data pipeline on AWS that ingests invoices from email, extracts structured data using OCR, Custom Scripts, and NLP, stores curated datasets in Amazon S3, and loads analytics-ready data into Amazon Redshift. This enabled descriptive analytics and downstream ticket sales forecasting with strong governance, observability, and cost efficiency.
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Real-Time Data Ingestion & Transformation Using Microsoft Fabric Streaming
Akshat JainA leading EdTech company partnered with our data engineering team to modernize their data pipeline infrastructure. The client faced delays in data availability, limited real-time insights, and difficulties scaling analytics and machine learning workloads due to their traditional batch-oriented ETL pipelines. To overcome these challenges, they aimed to implement a streaming-first, unified data architecture using Microsoft Fabric and its Streaming capabilities to Onelake Lakehouse.
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Transforming Retail Operations with Forecasting & BI on AWS QuickSight
Anish GangwalA leading Sri Lanka-based retail chain was looking to take its first step towards Business Intelligence (BI) and data-driven decision-making. Operating across multiple locations, they faced inefficiencies in inventory planning, vehicle utilization, and order fulfillment. With no centralized data repository or analytics framework, they lacked visibility into operational bottlenecks and customer demand trends. To optimize their retail operations, they partnered with NeenOpal to implement a data warehousing solution integrated with predictive forecasting and an interactive BI dashboard using Amazon QuickSight.
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Seamless Migration from Snowflake to Google BigQuery
Akshat Agrawal
Our client, a leading US solar services provider enhanced their data ecosystem by switching from Snowflake to Google BigQuery for improved scalability, performance, and centralized access. Over six months, 2000+ tables and 100+ procedures were migrated, overcoming challenges such as text handling and tooling limits. Robust ETL workflows and validations provided a smooth, timely transition.
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Optimizing Solar Reporting with Power BI to Tableau Migration
Akash GuptaOur client, a leading solar energy company in the USA, uses advanced technology to improve efficiency and decision-making. After being acquired by a parent company using Tableau, they needed to migrate all reports from Power BI to Tableau. They also faced challenges in tracking the usage and adoption of both new and existing dashboards and needed a solution to monitor these metrics effectively.
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