Is your PMO sufficiently data-driven?

by Jan Schiller

Most project management offices (PMOs) offer services to executives seeking to make informed decisions about the best approach to achieve their strategies. The key to effective data-driven PMO decision-making is actually becoming a data-driven PMO. Project portfolio data makes that possible and improves the respect and power of your PMO.  How does your project data stack up when it comes to data-driven PMO decisions? Let’s take a closer look at how to do this.

Predictive analysis, automated risk management, and data modeling are critical techniques required to support informed decision-making. These techniques are useless without data. Project management offices (PMOs) must have a comprehensive, thoughtful approach to developing and leveraging a measurement system to meet and exceed expectations and deliver stakeholder information requirements.

Consider ten foundational characteristics of data that are required to ensure your PMO provides valuable insights:

  1. Accurate. The right data is better than more data. Accurate data is the basis for trusting the insights produced by your PMO. Measuring the ROI of your project portfolio requires exact and precise data. Most importantly, project data is accurate if it is obtained directly from the source (such as a work plan) and not from secondary sources (such as a status report).
  2. Aligned. Scheduling, critical path, project interdependencies, and resource allocations are effective when you have given thought to which ideas should become projects, the priority of those projects, and when to start those projects. Referencing data in alignment with the business processes they support reduces the need to guess about risk exposure assessment because the risks are inherently aligned with the criticality of those processes.
  3. Analyzed. Analytics can tell you what happened, why it happened, what will happen, what should happen, what might happen, or how you can make it happen. Variance analysis and performance indicators for cost and schedule describe the gaps that require further investigation to determine why actuals are different from the plan.
  4. Clean. Normalize your data to maximize its potential by improving data quality, utilization, and relevance. Solutions utilizing artificial intelligence absolutely will not deliver the expected benefits without clean data. Non-structured data from projects must be harvested, digitized, and standardized.
  5. Clear. The right data brings more clarity to your project portfolio. Project portfolios create a large volume of data that is generated internally and externally. Consumers of that data should have no questions about what data was used (or not used) to produce the information and insights shared by your PMO.
  6. Interpretable. A good enterprise project management solution makes the most of your data efficiently and with the least amount of effort (after the initial acquisition and installation). A continuous and direct flow from the project portfolio makes it possible to transform the data into information, which is the basis for making informed decisions and taking action. Automated solutions help validate that data and ensure transparency.
  7. Optimized. Data forms information. Optimizing information results in better and more predictive insights that will propel your project portfolio to success.
  8. Owned. Data must have owners so that progress can be measured and the risks associated with business requirements delivered by projects can be understood. Ownership increases accountability and enables data enrichment.
  9. Trusted. Organizations need to trust the information supporting their decision-making, especially when one wrong decision has lasting, significant consequences. An organization’s best practices and processes for testing data must be consistently applied. Expect your customers to enjoy graphs and to want to see the data behind the graphs.
  10. Visible. Organizations need to see the information supporting their decision-making. The high-quality data that results from meeting the previous nine characteristics will be vastly underappreciated if the data is not visible. Dashboards describe the health of your organization in a continuous fashion rather than only at specific points in time.
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