Background: A financial services company operates a nationwide call centre to handle customer inquiries, process financial transactions, manage loan applications and disbursements, and provide customer support. The call centre requires a robust Business Intelligence (BI) platform to integrate data from multiple operational systems and provide actionable insights. The platform must comply with strict regulations for handling sensitive customer information, such as GDPR, CCPA, or other financial data compliance standards.

Objective: The goal is to create a unified BI platform that:

🔹Integrates data from multiple sources (Financial Systems, HR (Workday), Quality (Qualtrics), Salesforce CRM, Microsoft Dynamics 365, and other operational systems, like Five9, Genesys, Verint, Nice and other proprietary systems etc.

🔹Provides real-time and historical analytics for operational efficiency, employee performance, customer satisfaction, and financial outcomes.

🔹Ensures customer data privacy and compliance with relevant regulations.

🔹Enables secure, role-based access to dashboards and insights for different stakeholders.

Key Challenges:

🔹Data Integration Complexity:

•      • Consolidating data from diverse systems (financial systems, HR platforms, quality monitoring tools, CRM platforms like Salesforce, and MS Dynamics 365) into a single platform.
•      • Data silos and inconsistent formats across different data sources.
•      • A lot of workflow data (email response related) collated on excel files manually updated
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🔹Real-Time Insights:

•      • Delivering real-time analytics for call centre metrics, such as average handling time, first call resolution rates, customer satisfaction scores, and agent productivity.
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🔹Compliance with Data Privacy Regulations:

•      • A lot of customer data is housed in local UK, US and Europe based systems and governed by strict privacy and data protection guidelines, compliance to which had to be ensured.
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🔹Scalability and Performance:

•      • Handling large volumes of data generated by daily call center operations, including call logs, transaction records, and customer feedback.
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🔹Role-Based Access:

•      • Providing granular, role-based access to dashboards to ensure that employees only see data relevant to their job functions.

Solution Architecture:

🔹Data Integration: A robust ETL (Extract, Transform, Load) pipeline is implemented using tools such as Apache NiFi, Informatica and Azure Data Factory:

•      • Data Lake: Azure Data Lake is used to store raw, semi-structured, and structured data from various sources. Data from all sources is cleansed, transformed, and standardized.

🔹Data Warehouse and BI Tool

    • Data Warehouse: Azure Synapse Analytics is used to store aggregated data optimized for analysis.

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•     • BI Tool: Power BI is used to create interactive dashboards and reports for end- users. Dashboards include all relevant KPIs benchmarked against the best in class in the industry
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🔹Real-Time Data Streaming: API used to process real-time data from call centre systems, enabling dynamic dashboards and instant alerts.

🔹Data Privacy and Compliance

•     • Since our development team was based out of India, we did not have access to a lot of data that involved customer/member’s PI, that was required to be a part of the BI solution, the team used python codes on synthetic data created using AI that mimicked the actual data to carry out the required transformations. These codes were shared with the onshore teams that implemented on actual data.
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•     • Role-Based Access Control (RBAC):
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  •        • BI dashboards and reports enforce strict role-based access, ensuring that employees only access data they are authorized to view.
  •           For example:
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    •                     • Agents only see their performance data.
    •                     • Team leads view metrics for their teams.
    •                     • Executives access aggregated KPIs without detailed PII.
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🔹Scalability and Performance

•      • The system uses auto-scaling cloud resources to handle fluctuating data loads and maintain performance during peak times.
•      • A combination of in-memory computing and pre-aggregated datasets ensures fast query responses.

Benefits:

🔹Improved Operational Efficiency: Managers can monitor real-time call centre performance and address bottlenecks proactively.

🔹Enhanced Employee Performance: HR and quality teams can use detailed metrics to identify training needs and reward high performers.

🔹Better Customer Experience: Insights into customer interactions help identify pain points and improve service quality.