Microland Enhanced Visitor Experience for Singapore-based Wildlife Reserve by Unleashing Data-driven Operational Insights through Azure Cloud

Overview

Microland's client, a prominent wildlife reserve in Singapore with an annual footfall of 1.9 million visitors, grappled with the limitations of a legacy data warehouse, leading to unreliable decentralized analytics. In response, Microland implemented a transformative hybrid cloud strategy for the vital park operations system. Centralizing data improved access and control, fostering a seamless, self-serving approach that significantly elevated productivity across both business and IT functions. This strategic overhaul not only addressed immediate challenges but also aligned the client with its goal of achieving analytical leadership by 2025.

Challenges

The end client was facing the challenge of relying on a legacy Data warehouse system that did not serve data from all sources. As a solution, the client adopted localized analytics in several cases. However, this approach required business users to exert significant effort in gathering data from various systems to analyze financial and operational aspects. The localized analytics proved to be unreliable at times, leading to erroneous outcomes. The existing data warehouse system did not provide direct and seamless access to generated reports, requiring manual sharing via email or other mechanisms creating challenges for business users in self-serving the reports. Additionally, the data warehouse system was unable to scale effectively to accommodate the current and future data growth. It also failed to meet the required Business/Operations/Reports Service Level Agreements (SLAs).

Business users were also mandated to have technical skills, adding to the difficulties. Maintaining the system carried overhead due to manual intervention at various stages and managing system upgrades and compliances posed significant challenges. Furthermore, managing data governance, regulatory compliance, and adhering to the necessary security standards expected by both the industry and the client became increasingly challenging under the decentralized approach. The system fell short of meeting the client’s aspiration to become an analytical company capable of providing predictive analytics by 2025.

Solution

Microland partnered with a Japanese multinational information technology and electronics corporation to drive the client’s vision. Microland put forth a hybrid cloud strategy to tackle the difficulties at hand by executing a solution for their vital business system, overseeing all park operations and management. This was achieved by establishing a centralized and easily expandable enterprise data platform through Azure Cloud, Azure Data Factory, Azure Data Lake Storage, and Azure Databricks. This framework enabled the consolidation of data from diverse systems into a singular repository, fostering thorough data analytics and reporting capabilities. The platform was designed to accommodate real-time data processing using Azure IoT Hub and Azure Stream Analytics for capturing, analyzing, and acting upon real-time data from IoT devices, such as ticketing systems, retail outlets, etc.

By integrating On-Prem servers with Azure Cloud, a self-service reporting and analytics environment was created within the enterprise data platform, empowering business users to access centralized data, build dashboards, and generate reports independently, eliminating the need for localized analytics. This supported and facilitated the client’s aspiration of becoming an analytical company capable of providing predictive analytics by 2025.

Strict data governance measures were addressed by implementing role-based access controls, encryption, and data anonymization techniques, ensuring compliance with data privacy regulations, and protecting customer data.

Value Delivered

By centralizing the data, an efficient and streamlined approach was established, allowing for easy access to the required information. This led to significant productivity improvements in both business and IT operations, as data could be self-served in a seamless and controlled manner.

• The implementation of a centralized data platform brought about a reduction of 40% in overall business and IT operations costs, along with a 30% decrease in system running costs.
• The improved operational efficiencies resulted in overall cost reduction, while enhancing data quality, enabling better business intelligence, and ensuring enhanced compliance.
• With efficient operational intelligence and improved business processes, the organization could access information seamlessly, enabling an accelerated pace of launching new products and services.
• Finally, the platform played a significant role in promoting the business and educational aspects of the wildlife attraction in Singapore, supporting its growth while enhancing user experience.