PLANADVISER - September/October 2018 - 44
feature
Narrowing
theScope
How to parse
big data from
the recordkeeper
to measure plan
and participant
achievements
F
rom her position as senior vice president on the retirement plan advisory
team at Sentinel Benefits and Financial Group in Boston, Julie Doran has
watched big data's trajectory. She estimates that the amount of actionable
data produced by the typical defined contribution (DC) retirement plan has
roughly doubled every two years for at least the last 10.
" All the signs say this data flow is set to increase even more dramatically in
the next decade as the financial services industry catches up with the latest big
data capabilities already in use in other sectors, " Doran says.
According to Doran, her company has moved to embrace big data in significant
ways, particularly in regard to raising questions about how best to measure
and promote plan success.
With the rapidly increasing visibility of real-time plan mechanics, Doran and
others say, retirement industry practitioners are concluding it is not enough to
just look at the overall statistics on a retirement plan-i.e., participation rate,
average deferral rate, average balance and asset allocation. Using such mean
data points, while informative from the employer's perspective, can hide wide
discrepancies among individuals and groups of participants, and so retirement
plan advisers need to work closely with recordkeepers and potentially other
third parties to utilize the data at their disposal.
Experts across the financial services industry as a whole are challenged with
similar issues. Speaking at the 2018 DataDisrupt conference, Craig Snodgrass,
chief data officer of Cardlytics, said " aspirational and intent-driven data is not
the same as data derived from real decisions. " Another way to put this, he said,
" is that stated behavior is not always the same as demonstrated behavior, so
it is important to understand how data was generated and what exactly it is
telling you. "
However, when that real data is gathered, " you can take these streams of
data, clean them up and extract valuable insights that look across the book of
44 | planadviser.com September-October 2018 Art by Simone Virgini
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PLANADVISER - September/October 2018
Table of Contents for the Digital Edition of PLANADVISER - September/October 2018
Valuable Partnerships
Pension Risk Transfer
How to Explain CITs to Sponsors
Open MEP Opportunities
Appropriate Benchmarking
Investigations Intensify
PLANADVISER - September/October 2018 - Cover1
PLANADVISER - September/October 2018 - Cover2
PLANADVISER - September/October 2018 - 1
PLANADVISER - September/October 2018 - 2
PLANADVISER - September/October 2018 - 3
PLANADVISER - September/October 2018 - 4
PLANADVISER - September/October 2018 - 5
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PLANADVISER - September/October 2018 - 41
PLANADVISER - September/October 2018 - 42
PLANADVISER - September/October 2018 - 43
PLANADVISER - September/October 2018 - 44
PLANADVISER - September/October 2018 - 45
PLANADVISER - September/October 2018 - 46
PLANADVISER - September/October 2018 - 47
PLANADVISER - September/October 2018 - Valuable Partnerships
PLANADVISER - September/October 2018 - 49
PLANADVISER - September/October 2018 - Pension Risk Transfer
PLANADVISER - September/October 2018 - 51
PLANADVISER - September/October 2018 - How to Explain CITs to Sponsors
PLANADVISER - September/October 2018 - 53
PLANADVISER - September/October 2018 - Open MEP Opportunities
PLANADVISER - September/October 2018 - Appropriate Benchmarking
PLANADVISER - September/October 2018 - Investigations Intensify
PLANADVISER - September/October 2018 - Cover3
PLANADVISER - September/October 2018 - Cover4
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