Building a Stronger Foundation for Project Data Quality

A Journey of Continuous Improvement

In the world of project delivery, data is more than just numbers—it’s the foundation for informed decision-making, strategic planning, and successful project outcomes. In our portfolio, and as part of our journey across the Project Portfolio Management (PPM) Scheduling Capability Maturity landscape, we are continuously refining and improving our project data quality practices to ensure greater accuracy, efficiency, and transparency.

This is not just about data cleansing—it’s about creating a structured, sustainable approach to maintaining reliable project data across our portfolio.

Read on to learn about:

? Why project data quality matters

? The role of Master Data vs. Transaction Data

? How project data evolves through its lifecycle

? Next steps in structured data quality management

Let’s dive in!

The Importance of Project Data Quality

Project data quality is the foundation of effective project delivery and portfolio management. Poor data quality can lead to misaligned priorities, inaccurate reporting, resource bottlenecks, and project risks. On the other hand, well-structured, high-quality project data provides:

? Clear visibility into project health and status.

? Accurate forecasting of resources, schedules, and budgets.

? Improved governance for decision-making and compliance.

? Reliable insights for continuous portfolio optimisation.

In our portfolio, and as part of our ongoing maturity journey, we recently launched a Project Data Cleansing initiative, where we introduced a taxonomy for key project data fields and highlighted data quality issues for correction. This was a first step toward embedding structured data quality management into our project delivery framework.

Master Data vs. Transaction Data: Understanding the Difference

To effectively manage data quality, we must first distinguish between Master Data and Transaction Data, as they serve different purposes within our PPM system.

?? Master Data – The Backbone of Project Information

Master Data consists of stable project attributes that provide structure and classification. These elements ensure portfolio consistency and inform high-level strategic planning.

Examples of Master Data:

- Project Identification & Categorisation (Project Name, ID, Sub-Portfolio, Program, Work Type).

- Baseline & Scope Information (Approved Budget, Approved Schedule).

?? Transaction Data – The Story of Project Execution

Transaction Data is dynamic and updated as projects progress. It captures real-time execution details and requires periodic validation to ensure accuracy.

Examples of Transaction Data:

- Schedule Updates (Planned vs. Actual Start/Finish Dates, Milestone Tracking).

- Financial Tracking (Actual Costs, Forecast Updates).

- Project Status Changes (Work Status, Change Requests, Risk and Issue updates).

Both Master and Transaction Data are essential, but maintaining high-quality data in these categories requires ongoing validation and governance.

The Project Data Lifecycle – Ensuring Continuous Data Accuracy

Project data does not remain static—it evolves throughout the project lifecycle and must be managed accordingly.

?? Data Creation (Initiation & Planning Phase)

- Master Data is defined and validated (Project Name, Portfolio Classification, etc.).

- Baseline scope, schedule, and budget are approved.

?? Data Usage & Updates (Execution Phase)

- Transaction Data is continuously updated (progress tracking, cost updates).

- Regular audits help detect inconsistencies.

?? Data Validation & Cleansing (Monitoring & Controlling Phase)

- Scheduled data quality reviews ensure accuracy.

- Anomalies are flagged for Project Managers to correct.

?? Data Archiving & Closure (Completion Phase)

- Final data validation occurs.

- Project data is archived for future reference and lessons learned.

By actively managing project data through its lifecycle, we ensure more accurate, insightful, and actionable reporting across the portfolio.

Next Steps: Structured & Periodic Data Quality Management

Our PPM Scheduling Capability Maturity journey continues! Moving forward, our portfolio will implement a structured and periodic approach to project data quality management, ensuring that:

?? Data validation processes are embedded into project governance.

?? Regular data reviews help maintain accuracy and completeness.

?? Project Managers and stakeholders receive support in improving project data quality.

This initiative is not just a one-time effort—it is a continuous improvement cycle that will enhance the maturity of our scheduling and project data management practices.