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TREATING DATA AS A PRODUCT
The same applies for data being used in real time. If your product
recommendations engine isn’t using the freshest data, then your users are
going to be served outdated recommendations, negatively impacting the
user experience and harming your bottom line.
The need for data observability
The challenge outlined above is the exact problem that data observability
aims to fix. Data observability gives you transparency and control over the
health of your data pipeline, such that when an issue does occur you can
quickly understand:
1 Where is the problem?
2 Who needs to resolve it?
Knowing this information makes it possible to find and resolve issues far
quicker and minimize data downtime.
But, how is data observability any different to monitoring?
The best way to describe the difference is that monitoring covers the ‘known
unknowns’, whereas observability covers the ‘unknown unknowns’.
MONITORING
Known unknowns
Monitoring tells you when
something is wrong
Assumes you know
what questions to ask
OBSERVABILITY
Unknown unknowns
Doesn’t assume that
something is wrong
Assumes we don’t know what
all the questions are to ask
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