Based on the GRP, CEM developed a scorecard to assess how closely each dataset adheres to the principles. The scoring methodology differs between investments datasets and pension administration datasets. But the assessment is similar. The scorecard measures the completeness of investment and administration cost and performance reporting, as well as the timeliness of data submitted to CEM. 

All CEM subscribers are now scored against the GRP-based rubric and datasets are awarded gold, silver and bronze rating. Datasets that don’t meet CEM’s quality thresholds are excluded from the database.  

The scoring is designed to become more demanding over time. For instance, the timeliness threshold will become more stringent in future cycles to drive earlier reporting. Similarly, the criteria for reporting private market fees will become more rigorous, with the goal of pushing external managers toward more transparent and timely disclosure practices. 

CEM helps you identify areas where you score low and share best practices in behaviours across subscribers .If you have not yet received your data scorecard and would like to see it, please reach out to your CEM relationship manager or contact us at production@explorecem.com.

Based on the GRP, CEM developed a scorecard to assess how closely each dataset adheres to the principles. The scoring methodology differs between investments datasets and pension administration datasets. But the assessment is similar. The scorecard measures the completeness of investment and administration cost and performance reporting, as well as the timeliness of data submitted to CEM. 

All CEM subscribers are now scored against the GRP-based rubric and datasets are awarded gold, silver and bronze rating. Datasets that don’t meet CEM’s quality thresholds are excluded from the database.  

The scoring is designed to become more demanding over time. For instance, the timeliness threshold will become more stringent in future cycles to drive earlier reporting. Similarly, the criteria for reporting private market fees will become more rigorous, with the goal of pushing external managers toward more transparent and timely disclosure practices. 

CEM helps you identify areas where you score low and share best practices in behaviours across subscribers .If you have not yet received your data scorecard and would like to see it, please reach out to your CEM relationship manager or contact us at subscriptions@explorecem.com.

CEM’s data principles and data governance framework, which constitutes the Data Governance Committee, is outlined in CEM’s Data Policy. The policy is an official document that is reviewed annually and includes the foundational principles that CEM upholds for data integrity, the responsibilities of data owners and decision makers, and the parameters under which responsibilities cascade down at CEM.

CEM upholds data integrity, the responsibilities of data owners and decision makers, and the parameters under which responsibilities cascade down at CEM.

The policy builds on a structured RACI framework (RACI stands for Responsible, Accountable, Consulted, and Informed). The framework defines who makes decisions, who executes them, and who needs to be kept in the loop. It eliminates ambiguity and ensures that no material decision about your data is made unilaterally or informally.

Every data point is validated through our proprietary tools and expert judgment. Your data is first reviewed by our automated rules engine, a proprietary tool that compares your current data against prior years. An analyst assigned to your account then reviews your data using our in-house tools, comparing it against your peers and our broader universe. Where data does not meet our quality threshold, entire data sets can be rejected. In the coming weeks, we will cover our full seven-step validation process.

The series will take you through the full scope of our work. We will start with data governance, covering our principles, global reporting standards, and the role subscribers play in the process. From there, we will move into how we deliver data, including our quality process, peer group selection, and how we handle exceptions. We will then go deeper into investments and administration, walking through our taxonomies, methodologies, and how we make comparisons across peers. Later chapters will cover our research and analytics work, the tools and systems we use, including our rules engine, and finally, how all of this flows into the deliverables you receive. 

Our goal is to give you a clear view into our data governance approach, our validation processes, and the taxonomies and methodologies that shape our work. We want to give you the knowledge to understand your results with confidence and to ask sharper questions of the insights we deliver. We also welcome your curiosity. The more you understand our work, the more valuable the conversation becomes.