In an era driven by data and AI, businesses across industries are seeking ways to efficiently collect, analyze, and visualize data for strategic decision-making. This case study explores how a multinational company tackled the challenge of integrating and deriving value from diverse data sources across multiple cloud platforms using the Pirl platform.

Problem Statement

The company (IT System integrator with a presence in more than 50 countries) faced the issue of fragmented data sources across six different cloud platforms (databases, data lakes, API), leading to minimal value extraction due to a lack of correlation between systems. With real-time and historical data, including AI-generated insights and video analytics, the company sought a solution to unify, analyze, and derive actionable insights from these sources.


Diverse Data Sources: Data was collected from various cloud platforms with distinct applications, different login IDs, and analytics, leading to disjointed insights.

Integration Complexity: Integrating real-time and historical data with varied data formats posed technological and execution challenges.

High Costs and Risks: Traditional integration methods involved significant costs, risks, and extended timelines while existing solutions lacked comprehensive capabilities.

Pirl Platform Solution

The company implemented the Pirl platform, which offered a holistic solution for data ingestion, analytics, and visualization.

Data Pipeline and Integration with Pirl Labs
Data Pipeline and Integration with Pirl Labs
Seamless Data Ingestion:

Pirl’s REST API engine facilitated data collection through both push and pull mechanisms from various platforms. Pirl’s data connectors with various databases enabled correlation with historic data. Data from different sources, including images with threat metadata, were configured to flow into the platform efficiently. Real-time audit logs were established to ensure data integrity and completeness.

Real-Time Stream Analytics:

Pirl’s streaming analytics capabilities enabled real-time data validation, outlier detection, and data interpolation. Alerts and notifications were set up for critical events, ensuring prompt actions by users.

Data Correlation and Insights:

Pirl’s data analytics engine correlated data from multiple platforms, facilitating advanced insights. Computed results were stored in a separate database (Business Warehouse), allowing visualization of real-time and non-real-time data.

Dashboards and Visualizations:

Using Pirl, the company created over 300 charts, 80 chart types, and 51 dashboards for users at different organizational levels. The platform’s user management portal facilitated user access control and dashboard assignment according to roles.

Data Audit System:

Pirl’s data audit system was implemented to identify missing data and alert administrators for corrective actions.

Scheduled Reports:

Pirl enabled the generation of scheduled PDF reports, enhancing data communication among users.


Pirl’s parallel processing capabilities ensured performance optimization for data analytics tasks.

No-Code Data Applications:

Pirl’s no-code approach allowed non-technical team members to create and deploy complex data applications easily.

Results and Benefits

Pirl’s platform facilitated rapid integration, analytics, and visualization. Data literacy increased among users due to the intuitive interface. The support team’s proactive assistance ensured seamless implementation and operation.

A complex application involving multiple platforms and data types was built with a small team of eight individuals within four months. 

Data-driven decision-making became a norm, driving the company towards becoming a data-centric organization.


The case study highlights how the Pirl platform empowered the company’s digitization journey, overcoming challenges associated with diverse data sources, integration complexities, and high costs. Pirl’s holistic approach to data ingestion, analytics, and visualization transformed the company’s ability to make informed decisions and thrive in a data-driven environment.

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