Case Study 1

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The Chief Data Office (CDO) required a centralized analytics capability to unlock value from Salesforce data lake outputs. While large volumes of data were available, business leaders lacked timely, actionable insights to support sales performance, operational efficiency, SLA tracking, capacity planning, and cost management.

• Salesforce data existed in the data lake but was underutilized and difficult for business teams to consume
• Reporting was fragmented, manual, and inconsistent across functions
• Stakeholders needed trusted, near-real-time dashboards aligned to business KPIs
• No single owner existed for end-to-end analytics delivery and lifecycle management

I led the data analytics function within the Chief Data Office and owned the full solution lifecycle, partnering closely with sales, operations, and technology teams.

Key activities included:

• Conducted stakeholder interviews and requirements workshops to define KPIs and reporting needs
• Translated business requirements into scalable analytics and dashboard designs
• Modeled and transformed Salesforce data lake outputs into curated, analytics-ready datasets
• Designed and delivered executive and operational dashboards covering:
  
  • Sales performance and pipeline health
  • Operational metrics and SLA performance
  • Capacity planning and workforce utilization
  • Cost management and efficiency tracking
• Led UAT cycles, incorporating stakeholder feedback to ensure accuracy and usability
• Managed deployment and established processes for continuous improvement and enhancement

Delivered a standardized suite of interactive dashboards that converted complex Salesforce data lake outputs into clear, decision-ready insights. The solution provided a single source of truth across sales and operations, enabling leaders to monitor performance, identify risks, and proactively manage resources.

• Improved decision-making by providing real-time visibility into sales, operational, and SLA performance
• Reduced manual reporting effort and eliminated inconsistent metrics across teams
• Enabled data-driven capacity planning and cost optimization
• Increased stakeholder trust in enterprise data through governed, high-quality dashboards
• Established a repeatable analytics delivery model within the Chief Data Office

• Data analytics leadership
• Stakeholder management and requirements capture
• Business analysis and solution design
• Data modeling and dashboard development
• UAT leadership and production deployment
• Continuous improvement and analytics governance