PLANADVISER - September/October 2018 - 46

business to highlight trends and challenges that are not
really visible on a case-by-case basis. "
The State of Big Data
Doran suggests it is not uncommon these days to have a
client request a report from a recordkeeper or another
provider-say an outsourced fiduciary investment manager
or third-party administrator (TPA)-and receive hundreds
of pages of data about a single retirement plan.
" That can be overwhelming, but
the most
important
message I can share is that plan sponsors and advisers
do not have to be data scientists
to be able to take this data and do
something actionable with it, " she
says. " When you look at data specific
to a given plan sponsor client, you
have to get granular. Work with your
providers to dig in and understand
what the various groups within the
plan are doing. It's no longer enough
to just look at general averages and to
use this to make decisions about the
design and structure of the plan. "
According to Doran, the introduction
of big data technology into
the defined contribution (DC) plan
space " changes the ballgame entirely "
for what is possible in progressive
plan design, education and targeted
communication-to name just a few
of the areas where the expanding
pool of plan data can guide plan
sponsor clients' decisions.
" Leading advisers and providers are
Making the Most of Available Data
Delaney and Doran agree on some broad questions that all
plan advisers and sponsors should ask as they grapple with
new and growing troves of data: What is the source of the
data being collected, and who is doing the collecting? How
are they doing this collecting? Who owns the data, and how
is it being polished and protected? Is this just data scraped
from the 401(k) plan in isolation, or is it data that participants
are interacting with and verifying in some way? Is it
data that the recordkeeper is supplementing and expanding?
" Leading
" While the data flows are improving so much, it's important
to always take data with a grain
of salt, " Doran notes, also pointing to
the appearance of older vs. younger
employees' preparedness.
" This
advisers and
providers are
taking the
huge volume
of information
and distilling it
into very distinct
and informative
points. "
taking the huge volume of information
and distilling it into very distinct
and informative points, " she says, suggesting advisers ask,
and answer, targeted questions such as: " 'What are the
participants who are 50 or older doing with their investments?'
and 'What are my 20-something new enrollees
doing in terms of setting an adequate deferral rate?' "
Barbara Delaney, founding principal of StoneStreet |
Renaissance, a registered investment adviser (RIA) in New
York City, agrees that the possibilities to better leverage
data to improve plan design are practically endless. But,
she points out,
the refinement of plan data has also
allowed plan sponsors to realize they have made some
false assumptions.
" Often, with the rise of automated plans and use of
target-date funds [TDFs] as the qualified default investment
alternative [QDIA], the high-level averages make it
look like the younger groups are doing wonderfully, while
the older groups, who likely have assets outside the plan,
are doing worse, " Delaney observes. " In this case and
others, it is important to be able to step back and assess
the quality of data and the scope of what the data is telling
us. It is also exciting to see greater sharing of data to help
build more accurate pictures of individual participants'
household wealth. "
is
why digging into the data, and linking
different sources of data, is so important.
At a minimum, you have to be
parsing data by job type, age group,
location, etc. This type of work adds
tremendous value to the data you
have on hand. "
Delaney and Doran have some
sympathy for plan sponsor clients in
this conversation, as the sheer volume
of data can be overwhelming-and to
leverage it appropriately to get the best
use will require a team effort by the
sponsor, the adviser and the providers.
" Using data to inform plan design is
a huge element of what we are doing
in collaboration with our plan clients
to improve outcomes, " Doran says.
In this sense, the whole conversation
about financial wellness and
addressing employees' needs beyond
just the 401(k) plan balance starts with the question of how
to better leverage big data.
" Another important point is that data can tell you things
that you might presume are obvious[-but aren't], " Delaney
says. She cites a recent example, where a plan sponsor client
wanted to make changes to its plan. " When we went back
and asked how we should communicate with the 10 or so
employees it has over age 70, the executives actually didn't
believe us at first that they had individuals in their work
force so far beyond traditional retirement age. There are
many data points you can use to help both the participants
and the company. "
Doran has a similar anecdote: " I recently met with a
new plan sponsor client, and I presented it with data about
target-date fund usage in its plan. [Someone] said, 'Wow, we
knew our average plan age is in the 40s, but as it turns out
we also have a third of our population getting ready to retire
in the next five to 10 years.' This sparked a whole additional
conversation about what the plan was doing-or not-from
a work force management perspective. "
Discussion of the data, in turn, led to specific changes
in compensation practices to attract new, younger talent, to
start to address the potential brain drain this employer faced.
46 | planadviser.com September-October 2018
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