Avantages
It's tragically common to find a deep lack of understanding of the value well-formed, usable data architecture provides--even within large enterprise teams. Working at EIS, there is a clarity and long-sited view of how good IA enables good experiences and good tech. More, there is a manifest push to ensure that IA provides enduring value and opportunity. This is exciting stuff for a professional in the space.
The work is challenging, fast paced, and diverse. Much of it is cutting edge or novel, and can variously be exciting and terrifying for lack of established practice. The team however is unfailing in providing support, and work is highly collaborative (as it should be).
There is a world of opportunity to grow professionally as work is conducted with all sorts of companies, from fortune 1000 to startups, on information challenges unique to circumstances using a variety of methods.
A genuine appreciation (budget!) for professional development.
Inconvénients
As others mentioned, there can be a lot of ambiguity in discovery around and defining solutions for big data problems--so those challenged with time and stress management may be out of their comfort level.
Most work (99% excluding rare client on-sites) is remote, which poses its own challenges, though I always found team support and assistance when needed.
PTO is open, and need for time off respected, but constrained by the realities of client schedules and the way consulting works.