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Every decision—or non-decision—you make about data early on multiplies by a factor of two later in the project. That’s not a scare tactic. That’s just identity math.
Let’s say you’re kicking off a new identity program. You know you need user location to drive access policies, governance rules, or onboarding flows. But your authoritative source has location data in five different formats—some say “NY,” others say “New York,” and some list office addresses with zip codes and floor numbers.
You tell yourself: “We’ll clean it up later.”
What you’ve really done is commit your future self to a much more expensive project.
Because now, instead of solving the issue at the source, you’ve built three separate problems:
Data ingestion complexity: You need logic to normalize location at the point of ingestion.
Decision-making confusion: Anytime you want to trigger an action based on location, you need extra rules to guess what “New York” really means.
Change management chaos: When upstream location data shifts, you’re now in the business of data maintenance—with none of the authority to fix the real problem.
Responsibility Without Accountability
That’s the kicker. In IAM, we often end up responsible for the output of bad data, but powerless to fix the input. And that’s where identity projects spiral.
Bad data leads to brittle logic. Brittle logic leads to failed certifications, misfired provisioning, and AI models that make terrible decisions. It’s the silent killer of velocity.
Here’s the hard truth:
Identity is downstream of data—and downstream decisions are more expensive.
So what do we do?
What Smart Identity Leaders Do Instead
Clean early, not late: Push for authoritative sources to normalize and codify the data before it hits the IAM system.
Map and monitor: Build a source-to-consumption data map so you can track how bad data impacts logic downstream.
Push accountability upstream: Don’t accept ownership of fixing problems you don’t control. Instead, elevate the data issue to the right stakeholder (hint: HR, IT, or Legal).
Automate insights, not assumptions: Use tools that flag inconsistencies and prompt fixes—not patchy logic built on hope and regex.
Identity is the connective tissue of your enterprise. If the data that feeds it is broken, everything it touches will feel off. So before you launch that AI-powered access engine or roll out your next-gen onboarding workflow, pause and ask:
Are we building this on clean, accountable data—or are we inheriting a mess we’ll have to own forever?
Clean it now. Or pay for it twice (or more) later.