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May 13th, 2021
By Blake McLaughlin
When asset allocators turn to benchmarking to assess performance, nothing is more important than the underlying quality of the data. Four key attributes determine data quality for benchmarking:
In short, the data must be comprehensive, reliable and provide good coverage across all plan types, sizes and universes. Then, and only then, can it inform highly accurate, insightful peer-to-peer comparisons.
The underlying data should cover all plan types, plan sizes, investment styles and universes. It should also provide a large representative data set of like peers. This will allow for accurate performance benchmarking and insight into the factors that lie beneath. The bigger and broader the peer-data data set utilized, the less bias there will be in the data, the more opportunities to make true apples-to-apples comparisons and the greater the ability to drill down for greater relevance.
Some data sets represent a lot of AUA. However, they may also comprise or skew toward the largest plans. Plans of different sizes may have different requirements and investment policy guidelines. Asset allocations may differ and need to adjust accordingly. Plans of different sizes often operate under different policies and expectations. True peer-to-peer comparisons require a data source with adequate coverage for plans with similar characteristics, including size.
To assure that the data is accurate and reliable, it should be sourced from live institutional portfolios. Timeliness is critical as well. Getting statistically meaningful data as soon as possible is critical to gaining actionable comparative views. Historical data is another factor to consider. There are times when it can be invaluable to compare against plans that may have already navigated certain scenarios. Examples include:
It’s important to be able to constrain the data to compare against like peers. A source that provides a vast number of pre-built universes may help to get answers fast. But it’s also valuable to be able to tailor universes for a more refined apples-to-apples comparison—such as to include more plans for a broader sample or to narrow the universe to a tighter set of parameters. Look for the flexibility to drill down to the details required and easily integrate benchmarking data into reporting.
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