Data Quality
Quality matters more than
quantity. A smaller
dataset that is clean,
consistently labeled, and
representative of the full
range of outcomes you
care about will
outperform a large messy
one every time.
A useful test: If two
people on your team
pulled the same record
independently, would
they interpret it the same
way? If not, that
inconsistency will be
absorbed directly into
your model's outputs.
Data Quantity
How much you need
depends on what you're
predicting and how
complex the patterns are.
Simple classification
problems can be
approached with a few
hundred well-labeled
examples. Image analysis,
multi-variable prediction,
and anomaly detection
typically require
substantially more. If you
don't have enough, there
are legitimate paths
forward — covered in the
next section.
Accessibility 
& Governence
A model can only learn
from data it can reach. If
your data is fragmented
across disconnected
systems or instrument-
specific formats, that
becomes a project
constraint before it
becomes a technical one.
In regulated
environments, data
lineage — knowing where
training data came from
and how it was collected
— isn't optional. It's the
foundation everything
else is built on.
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