Case Study: Leading Technical Projects Without the Data Headaches

Role: Project Manager
Context: Large-scale technical consultancy engagement requiring coordination across engineering, finance, and delivery teams


🔪 Challenge

The project manager needed to:

  • Track work-in-progress against project milestones and budgets
  • Report progress to multiple stakeholders—including engineering leads and finance
  • Access consistent data on effort, cost, and risk exposure from multiple systems (e.g. time tracking, ERP, proposal documents)

Problems included:

  • Manual collation of data from spreadsheets and SharePoint files
  • Inconsistent naming conventions and project codes across systems
  • Difficulty reconciling time bookings with actual budgeted work
  • Misalignment between proposal expectations and delivery reporting

“I spent hours each week trying to make sense of data from five different places—and still couldn’t guarantee it was accurate.”


🚀 Solution

The Data Engineering team:

  • Built a Project.Value and Project.Details_Plus view, aligning cost, time, and delivery data into a single, consistent model
  • Created clear mappings between proposal estimates, ERP project IDs, and timesheet codes
  • Implemented automated updates via scheduled pipelines—removing manual reporting
  • Integrated key fields such as PRU, Risk Status, and Estimated vs Actual Cost into self-serve dashboards
  • Provided documentation and support through the KnowHow portal and regular walkthroughs

🌟 Outcome

  • ✅ Saved over 10 hours per month in manual data prep
  • ✅ Reduced reporting errors and duplication
  • ✅ Enabled earlier identification of budget risks and scope changes
  • ✅ Increased stakeholder confidence in project data
  • ✅ Freed up time to focus on delivery, not data wrangling

“Now I spend my time leading the project—not chasing down data or second-guessing the numbers.”


📊 Key Data Engineering Value Areas

  • Alignment of disparate data sources into one unified view
  • Automation of data preparation, reducing human error
  • Access to self-serve, timely data without needing IT intervention
  • Support through documentation, QA, and collaboration

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Have a project challenge you’d like to explore? Reach out to the Data Engineering team—we’re here to help.

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