Business Need :

Our client was struggling with custom reporting capabilities of Service Now platform. There are limitations in the Service Now reports when it comes to data groupings, custom slicing and dicing, dates, trends over years and other complex reporting. Service Now provides an option to build custom reports, however, these custom reports sometimes require a significant amount of jelly and javascript knowledge to maintain.

Solution :

• Built Analytics platform for service now on Azure using metadata driven approach.

• Built ETL process to ingest and transform Service Now data in Azure data lake.

• Built Datavault model to store data in an organized and effective manner.

• Built Dimensional model on top of Datavault model to serve the reporting needs

Benefits :

• Metadata driven approach, code reusability.

• Better Slicing and dicing option on PowerBI reports.

• Addressed the custom KPIs need.

• Automated data pipeline and reporting refresh.

• Scaled the same solution to other service now entities.

• Low maintenance and Low operational cost

Unlocking Advanced Reporting in ServiceNow with Azure: A Case Study

Introduction

ServiceNow has become a cornerstone for IT service management in organizations worldwide, offering a robust platform to streamline operations. However, as businesses evolve, they encounter the need for more complex reporting capabilities than what is natively available in ServiceNow. This article explores a case where a client faced significant challenges in custom reporting within ServiceNow, and how a bespoke solution leveraging Azure transformed their reporting landscape.

Business Challenge

The client's main struggle was with the limitations inherent in ServiceNow's reporting capabilities. These limitations became apparent when dealing with data groupings, custom slicing and dicing, and analyzing trends over years. ServiceNow does offer custom report creation, but these require substantial knowledge of Jelly and JavaScript for maintenance, posing a barrier for users who may not have extensive programming expertise.

Solution Overview

To overcome these challenges, a multifaceted solution was devised and implemented on Microsoft Azure. The solution comprised the following components:

1. Analytics Platform on Azure

A dedicated analytics platform was built specifically for ServiceNow data on Azure. This platform was designed with a metadata-driven approach, ensuring flexibility and scalability. By abstracting the logic from the implementation, this approach facilitated code reusability and simplified future enhancements.

2. ETL Process

An Extract, Transform, Load (ETL) process was developed to ingest ServiceNow data into an Azure Data Lake. This step was crucial for preparing the data for analysis, ensuring it was clean, structured, and ready for the subsequent stages of the solution.

3. Datavault Model

The Datavault model provided a secure and efficient way to store ingested data. This method of data storage is highly organized, supporting the integrity and accessibility of the data. It served as a foundational layer for the dimensional model built on top of it.

4. Dimensional Model

On top of the Datavault model, a dimensional model was constructed to directly serve the reporting needs. This model facilitated easy access to data for reporting purposes, enabling complex queries and analyses to be performed with greater efficiency.

Key Benefits

The solution delivered a multitude of benefits, significantly enhancing the client's reporting capabilities:

Metadata-Driven Approach: This allowed for greater flexibility in report creation, with enhanced code reusability.

Enhanced Reporting Capabilities: The solution provided better options for slicing and dicing data within PowerBI reports, making it easier to derive insights.

Custom KPIs: The new system could accommodate custom KPIs, addressing a significant gap in the native ServiceNow reporting functionality.

Automation: Both the data pipeline and report refresh processes were automated, reducing manual effort and minimizing the potential for errors.

Scalability: The solution was designed to be scalable, allowing it to be extended to other ServiceNow entities as needed.

Reduced Costs: The approach resulted in lower maintenance and operational costs, providing long-term financial benefits.

Conclusion

The integration of Azure with ServiceNow for advanced reporting capabilities presents a powerful solution to the limitations faced by many organizations in their IT service management processes. This case study demonstrates how a metadata-driven approach, coupled with a sophisticated data handling and storage strategy, can unlock new possibilities in data analysis and reporting. By leveraging Azure's capabilities, businesses can achieve a higher level of flexibility, efficiency, and insight into their operations, leading to better decision-making and enhanced operational performance.