Project Overview

Empowering a Global Market Research Leader with Lightning-Fast, Scalable Data Insights
A prominent player in the market research and media analytics space, the client needed to overhaul their legacy analytical infrastructure to meet the growing demands of data volume, speed, and insight depth. Their existing system, though functional, was becoming a bottleneck as new advertising channels and data formats emerged. The goal was clear — build a modern, cloud-native analytics system that could perform complex cross-channel analysis at speed and scale.

Impressed with eSparkBiz’s experience in enterprise-grade big data solutions, the client entrusted us with full responsibility for architecture modernization, cloud deployment, and analytics performance optimization.

The Problem

Legacy Bottlenecks in Performance and Scalability

The client’s legacy analytical system was no longer equipped to handle the growing complexity and scale of modern marketing data. As the volume of data surged across campaigns, audiences, and channels, the system’s outdated architecture led to frequent performance bottlenecks. Reporting processes were slow and resource-intensive, significantly limiting the organization’s ability to respond to evolving market dynamics. The lack of scalability also posed challenges in onboarding new data streams and users, putting the client’s long-term data strategy at risk.

Inadequate Support for Multisource Data Integration

With marketing data flowing in from disparate sources such as TV viewership logs, mobile browsing activity, website traffic, and survey responses, the client required a system that could handle complex, heterogeneous data formats—including TXT, XLS, and compressed archives. However, the existing system could not parse, normalize, and correlate this data at scale. This hindered comprehensive audience mapping and limited the accuracy and granularity of cross-channel insights.

Lack of Real-Time Analysis and Insight Generation

Marketing teams needed access to timely, interactive analytics tools to make informed decisions on campaign allocation, channel optimization, and audience targeting. However, the legacy platform failed to support ad hoc querying, real-time data visualization, or custom reporting features. Delayed processing and rigid data workflows made it difficult for business users to explore trends, run simulations, or derive actionable insights on demand, ultimately impacting the effectiveness of their advertising strategies.

The Solution

To deliver a future-proof analytics platform, eSparkBiz worked closely with the client’s BI architects, aligning on architecture and executing the full migration and implementation effort.

Cloud-Native Infrastructure

We deployed a robust hybrid cloud environment using AWS and Azure, ensuring scalable storage and compute power to support big data workloads.

Data Pipeline Modernization

Raw data ingestion pipelines were rewritten using Python and Spark to handle 1,000+ data types. We built multi-stage ETL processes to support transformation, merging, parsing, and loading across data warehouses.

Modular Data Architecture

The modular data architecture included a staging layer built on Apache Hive, which structured raw data from diverse sources without applying immediate mappings. In Data Warehouse 1, respondent IDs were mapped across channels, such as radio, TV, internet, and surveys, based on predefined business rules using Python-based ETL processes. Data Warehouse 2 leveraged Apache Hive and Apache Spark to perform real-time data transformations, incorporating role-based access filters and dynamically computing KPIs using Spark SQL and Scala-driven ETL.

Interactive Desktop Application

Using .NET and WPF, we developed a powerful MVVM-based desktop application that enabled users to perform deep cross-analysis of 30,000 attributes. Interactive charts, custom report generators, and forecasting modules helped users visualize key metrics like Reach Ranking and Share of Time effortlessly.

The Result

The newly implemented analytics system delivered a dramatic improvement in query performance, processing complex data requests up to 100 times faster than the legacy solution. This leap in speed allowed the client to run multiple concurrent queries and generate insights in near real-time, drastically reducing waiting times for marketing analysts and decision-makers. By seamlessly integrating diverse data sources and enabling on-the-fly computations, the platform unlocked the ability to perform comprehensive, cross-channel advertising analysis, encompassing TV, mobile, web, and survey data across multiple markets.

The system’s modern, modular architecture ensured scalability and flexibility, allowing the client to easily incorporate new data streams and analytics capabilities as their needs evolved. Furthermore, the intuitive desktop application offered a user-friendly interface with rich visualization tools and customizable reports, empowering business users to explore vast datasets independently and uncover actionable insights without relying heavily on IT support. Altogether, these advancements enhanced operational efficiency, improved the accuracy of marketing strategies, and helped the company maintain a competitive edge in the fast-paced media analytics industry.

Transforming Query Speed: 10x Faster Performance with Cloud Scalability

Advanced Big Data Analytics Platform for Cross-Channel Advertising Insights

About The Project