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2 minute read
Perfect data does not exist.
Every dataset I've seen over the years contains what I call noise. These are quality issues that don't affect insights as long as you account for them properly in your analysis.
Here's what gets me: organizations burn months chasing perfection while opportunities slip away.
I worked with a broker years ago who spent way too much time cleaning his dataset. In the end, the exercise contributed almost nothing to overall quality. We still derived solid insights from the "dirty" data.
Someone told me once: never let perfect data be the enemy of good data. I watch for this trap all the time.
The Real Cost of Perfection
Organizations spend up to 27% of employee time correcting data issues. More than a quarter of your workforce polishing data instead of using it.
The obsession with perfection leads to lost time. Lost time cascades into lost opportunities, longer sales cycles, missed client engagement windows, new patient care initiatives, and more. This translates into real financial losses long term.
Meanwhile, teams are stuck validating data fields that don't matter in the end.
Fear Drives the Perfection Trap
Why do most healthcare data teams struggle to assess if data is "good enough"? Organizational culture. Lack of experience. Lack of skill.
Often all three.
A culture of chasing perfect data looks like this: mistakes get punished instead of becoming learning moments. People are afraid to speak up for fear of reprimand. Communication and collaboration suffer.
Fear makes people spend months cleaning data instead of risking being wrong.
Decisions taking weeks now take quarters. The choice to launch a program, build a product or target a market segment gets delayed. Teams are paralyzed by imperfect information.
The irony? Only 20% of healthcare organizations trust their data. Months of perfection work, and trust still doesn't show up.
What Matters
You need to keep the endgame in sight. What do you want to achieve with the data? Which data fields support your analytical objective?
If there are enough data points to support the analysis you need, the rest gets disregarded. If gaps exist in the data and another dataset fills them, don't waste time. Fill those gaps and go.
Data works best broken into chunks and used for specific purposes. This approach enables faster analysis, quicker insights, and getting to action sooner.
Action gets things done. Not pristine datasets.
The Simple Fix
If you're watching your team spend months on data perfection projects, ask one question first: what's the objective?
Define the objective. Then narrow your data to fit.
Why do healthcare organizations start with data instead of the objective? Lack of experience. Or the way they've done things for years.
Both are fixable.
Never let perfect data be the enemy of good data. Define your objective, see if available data fits, close any data gaps, and go.
Speed beats perfection.