How the 1937 Washington Quarter DDO FS-101 ‘Cherrypick’ Teaches Us to Unlock Hidden Business Intelligence in Development Data

Development tools generate a mountain of data – but most companies let it gather dust. What if you could mine this data like a rare coin collector? Let’s explore how the hunt for the 1937 Washington Quarter DDO FS-101 can teach us to spot hidden patterns in our data that actually move the needle.

From Coin Collecting to Code: The Hidden Data Goldmine in Plain Sight

When hobbyists talk about “cherrypicking,” they’re describing a skill we desperately need in data work. Finding the 1937 Washington Quarter DDO (FS-101) wasn’t luck. It was methodical observation in a sea of common coins.

The coin’s telltale doubled die obverse – that subtle doubling on “IN GOD WE TRUST” – stayed hidden for years. Not because it was impossible to see, but because most people didn’t look closely enough.

Sound familiar? In data terms, we’re looking for:

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• A sudden spike in user drop-off after a seemingly minor UI change
• A microservice that keeps failing in production – but only on Tuesdays

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• A developer whose PRs take 3x longer to review than anyone else’s

Just as coin collectors use loupes, lighting, and experience to spot anomalies, we need structured tools to see what’s hiding in plain sight in our development logs.

Why the 1937 Washington Quarter DDO FS-101 is a Data Discovery Metaphor

The FS-101’s doubled die error is a perfect analogy for rare data events. Think of it as:

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• An anomaly in user behavior logs

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• An outlier in customer churn signals

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• A hidden performance bottleneck in microservices

Coin experts know two truths that apply directly to data work: You miss what you don’t examine. And you can’t examine what isn’t organized.

Building the Data Infrastructure for “Cherrypicking” Insights

Want to find your data version of the 1937 DDO? Start with these foundations:

1. Data Warehousing: Your Centralized “Coin Collection”

Ever tried finding a specific coin when they’re all in different boxes, mixed with no labels? That’s what working with fragmented data feels like.

A modern data warehouse (Snowflake, BigQuery, Redshift) becomes your single source of truth – like a well-labeled coin album where you can compare items side by side.

Start here: Bring together all your development data – CI/CD logs, code commits, feature flags, user events – into one place. Try a schema like this:


-- Simple schema for cross-system analysis
CREATE TABLE dev_events (
event_id STRING,
event_type STRING, -- 'commit', 'deploy', 'error'
repo STRING,
author STRING,
timestamp TIMESTAMP,
issue_id STRING,
environment STRING,
duration_ms INT,
error_message STRING
);


Now you can connect the dots between systems, just like a pro collector would cross-reference auction records, grading reports, and provenance.

2. ETL Pipelines: Automating the “Second Pass Through the Room”

The best collectors don’t just glance once. They return with better lighting, different angles, fresh eyes. In our world, this means automated data transformation and enrichment.

Your ETL pipeline should:

• Pull raw logs from GitHub, Jira, Datadog
• Tag with context (team ownership, sprint phase)
• Flag anomalies (like commits 200% larger than usual)
• Feed clean data to your warehouse

Tools like Airbyte, Fivetran, or custom scripts make this scalable. Here’s a quick way to spot risky commits:


# Simple outlier detection for commits
from statsmodels import robust
import pandas as pd

def flag_large_commits(df):
df['commit_size_z'] = robust.zscore(df['files_changed'])
return df[df['commit_size_z'] > 3] # Flag the top 0.3%


From Data to Decision: BI Tools for Spotting the “Doubling”

With clean data in place, now comes the fun part – actually finding those rare insights.

3. Tableau: The “Magnifying Glass” for Anomaly Detection

Tableau shines for visual exploration. Build dashboards that reveal:

• Code churn vs. defect rate (high values together? Technical debt warning)
• Deployment frequency by team (who’s moving fast? Who’s stuck?)
• Production errors (any correlation with recent deployments?)

Pro tip: Use Tableau’s set actions to let teams drill into suspicious clusters – like examining that doubled “G” in “GOD” under magnification.

4. Power BI: The “Auction Table” for Real-Time Decisions

Power BI works great for real-time monitoring and alerts. Try dashboards that show:

• CI/CD pipeline status (failed builds in last 24h)
• Feature adoption vs. support tickets (a spike might mean usability issues)
• Error rate alerts (like “Sentry errors up 300% in service Y”)

Use DAX to track key metrics:


-- Simple deployment success rate
SuccessRate =
DIVIDE(
COUNTROWS(FILTER('Deployments', 'Deployments'[Status] = "success")),
COUNTROWS('Deployments')
)


Developer Analytics: The “PCGS Grading” of Your Codebase

Just as PCGS grades give coins credibility, developer analytics gives us objective measures of our code and teams. Track what matters:

• Lead time for changes (how fast do commits reach users?)
• PR review time (who’s slowing things down?)
• Bug recurrence rate (are fixes sticking?)

5. Automate “Cherrypick” Alerts

Set up systems to find the exceptions automatically. For example:

• “Dev committed 50+ files in 60 minutes” → potential risk

“Microservice failed 3 deployments in a row” → time to investigate

• “User engagement dropped 30% post-release” → check for correlation

Tools like GitHub Insights, CodeClimate, or custom SQL queries help. Try this to find “hot files”:


-- Find files getting constant changes (potential tech debt)
SELECT filename, COUNT(*) AS edit_count
FROM git_events
WHERE TIMESTAMP > NOW() - INTERVAL '7 days'
GROUP BY filename
HAVING COUNT(*) > 5
ORDER BY edit_count DESC;


Case Study: The “Junk Silver Bin” of Debugging Data

A developer once found a valuable 1896-S Barber Quarter in a $1 face value lot. In software, our “junk data” includes logs, error reports, and support tickets – yet this is often where we find the most value.

Real example: A support ticket says “login is slow.” Instead of dismissing it as noise, join it with:

• Auth service logs (timing, error patterns)
• Database query performance
• Recent deployment history (was anything pushed just before?)

This is root cause analysis with data – like using magnification to check if that “doubled die” is real or just a scratch.

Conclusion: Make Every Pass Through the Data a “Second Look”

The 1937 DDO wasn’t found on the first glance. It took:

• A system (data warehouse + ETL)
• Tools (loupe → Tableau/Power BI)
• Process (second pass → automated alerts)
• Mindset (expect the rare → watch for outliers)

As data professionals, our job is to build that system. Organize the data, automate discovery, and visualize what matters. Whether tracking code quality, user behavior, or system performance, the approach stays the same: Look for the doubling. Find the rare event. Then act.

That next big insight? It’s probably hiding in your logs, waiting for someone to look closely enough. Time to start digging.

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