Unifying Multi-Region Sales Pipeline Analytics with Power BI and Power Query
Our 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.
READ MORE
Unifying Eight Disconnected Business Systems into Centralized Power BI Dashboards
Our client, a managed technology services provider (MSP), partnered with NeenOpal to replace a fragmented, siloed reporting process with a centralized Power BI dashboard environment. Business-critical data was scattered across multiple disconnected systems, from the CRM and PSA to the service desk, client surveys data, and network monitoring tools, leaving leadership without a single view of sales, customers, operations, or the workforce. NeenOpal designed and delivered a centralized Power BI reporting solution that unifies data from all of these sources into an automated, daily-refreshed analytics layer on AWS. By ingesting each source, staging it in a PostgreSQL warehouse, and modeling it into governed Power BI dashboards across every business function, the team turned manual, spreadsheet-bound reporting into interactive, self-service insight the client can filter and act on in seconds.
READ MORE
Microsoft Fabric Migration with Row-Level Security for Multi-Client Analytics
Our client, a B2B financial wellness and HR technology company, where we have worked on their data that delivers reporting to multiple business customers, partnered with NeenOpal to consolidate a fragmented reporting stack into a single, secure analytics platform. The client's finance and user-engagement data lived across MongoDB, AWS RDS, Google Sheets, and HubSpot, and was surfaced through standalone Power BI dashboards built on AWS. Serving each downstream client meant securely embedding a per-user filtered dashboard for external users logging into the client's portal, and app-owns-data embedding at that scale requires dedicated capacity. On shared, standalone Power BI the team had worked around this by building and maintaining a separate dashboard and semantic model for every client, which increased infrastructure cost and slowed delivery. NeenOpal designed and delivered a four-week Microsoft Fabric migration that unified every source into one governed semantic model with dynamic row-level security. By rebuilding the pipeline on a medallion architecture inside a Fabric Lakehouse and enforcing access with a single Power BI security role, the client replaced many duplicate dashboards with one unified report that automatically shows each user only the data they are permitted to see.
READ MORE
Unified Cross-Platform Intelligence Across 20+ Data Sources
A 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.
READ MORE
Turning Foot-Traffic and POS Data into Real-Time Venue Intelligence
Our client operates a network of nightlife and hospitality venues, where each location generates massive volumes of foot-traffic and point-of-sale (POS) data every day. Without a unified way to translate this data into meaningful insights, decisions around staffing, promotions, and operations were largely reactive, cross-venue performance comparisons were nearly impossible, and revenue opportunities remained hidden inside fragmented data systems. NeenOpal partnered with the client to build a scalable, multi-tenant venue intelligence platform on AWS that transforms raw POS and footfall activity into real-time, decision-ready insights at the venue, region, and network level.
READ MORE
How a Global Agricultural Equipment Manufacturer Built a Trusted Web Analytics Platform with GA4, Snowflake, and Power BI
Our client is a leading manufacturer of agricultural and industrial equipment operating across international markets, with a B2B sales model distributed through an authorised dealer and reseller network. The client's website serves as a primary discovery and research channel for prospects and distributors, where meaningful conversion actions centre on product brochure and playbook downloads rather than direct online transactions. Despite access to Google Analytics 4, the client had no reliable mechanism to trust or act on its own web data, with sampled GA4 reporting, undefined conversion events, and the absence of a structured data pipeline meaning internal stakeholders were making website and content decisions without confidence in the underlying numbers. NeenOpal partnered with the client to rebuild the analytics data foundation from the ground up, engineering a modern pipeline from GA4 through BigQuery into a multi-layer Snowflake data warehouse, and delivering a suite of Power BI dashboards that provided trusted, unsampled website intelligence for the first time.
READ MORE
How a Healthcare Education Organisation Replaced Manual Survey Scoring with a Fully Automated Analytics Pipeline and Real-Time Leaderboard
Our client is a healthcare and medical education organisation that runs competitive, survey-based learning programmes for clinicians, residents, and academic medical professionals across the United States. The organisation conducts multiple educational event series, structured as races with individual laps, where participants respond to clinically oriented survey questions under time pressure, and performance is ranked across both individual questions and cumulative laps with top performers recognised and rewarded at the close of each event cycle. Prior to NeenOpal's engagement, every aspect of survey data collection, aggregation, scoring, and leaderboard generation was handled manually through Excel, an approach that introduced systematic risk of error and created significant operational burden for the internal team. NeenOpal designed and delivered an end-to-end automated pipeline, migrating the client from a fragmented, manual process to a fully governed, cloud-based analytics platform powering daily leaderboard refreshes across all active events.
READ MORE
Predicting Video Revenue Through Marketing Mix Modeling and Bayesian Forecasting
Our client, a media and entertainment company, sought to establish a scalable and repeatable process to predict video revenue based on varying marketing spend inputs. With multiple upcoming releases and limited visibility into which marketing channels drove the most impact, media planning decisions were largely manual and difficult to optimize. The client partnered with NeenOpal to operationalize a Marketing Mix Modeling (MMM) framework, enabling data-driven revenue forecasting, dynamic sensitivity analysis, and interactive scenario planning through a Tableau dashboard.
READ MORE
Custom CI/CD Pipeline for Multi-Tenant SaaS Delivery on Microsoft Fabric
Our client, a SaaS product company built on Microsoft Fabric, needed a reliable and scalable way to deploy analytics content across multiple customer tenants — each with different service tiers, feature sets, and upgrade preferences. After evaluating Fabric's native deployment pipelines, critical limitations made it unfit for a multi-tenant SaaS model. NeenOpal designed and implemented a fully custom CI/CD pipeline using GitHub Actions and the Fabric REST APIs, giving the team complete control over what gets deployed, to whom, and when.
READ MORE
How a Leading Gold Loan Provider Eliminated Retention Guesswork with ML-Powered Churn Prediction
Our client is a leading financial institution based in Sri Lanka, providing specialised gold loan products across a retail network of more than 50 branches and serving an active customer base estimated in the hundreds of thousands. The business model is built around short-term, collateral-backed lending, where customer retention at loan maturity directly determines portfolio revenue. Despite years of accumulated transactional data across five core SQL Server tables, the organisation had no mechanism to convert that data into customer-level insight. NeenOpal partnered with the client to build and deploy a machine learning churn prediction system, alongside a customer scoring model and Tableau dashboards, enabling the organisation to shift from reactive, instinct-driven outreach to precision-targeted retention.
READ MORE
