🚀 A practical data efficiency tip for software developers & tech leaders:

✅ Treat your data like you treat your code!

Instead of waiting for data problems to surface in production — or worse, in your ML models or analytics — you can catch them earlier by integrating data checks into your development workflow.

Here’s how:
💡 Add data validation tests to your CI/CD pipeline — just like unit tests for code.
💡 Define and enforce data contracts (expected schemas & rules) between teams or systems.
💡 Run automated change impact analysis when modifying data pipelines to see what breaks before deploying.

By shifting these checks left — into your CI/CD pipeline — you avoid expensive downstream failures, reduce debugging time, and deliver more reliable ML and analytics outcomes.

Start small: pick one critical dataset or pipeline and add basic schema validation to your PR checks. You’ll thank yourself later.

💻📊 #SoftwareDevelopment #DataValidation #CI/CD #TechLeadership #ML #Analytics

"Wow 😮 I can finally see what my change affects!"
But 30 seconds later: "Wait... do I need to validate ALL of these models?"

This pattern showed up in hundreds of user interviews. See how we resolve it: https://reccehq.com/blog/Building-Impact-Radius-1/

#datacorrectness #datavalidation #datatests

How do you review data changes in PRs? 📊

A) Auto-diff everything
B) Explore impact then validate
C) Manual spot checks
D) No review

We're seeing a big shift from A → B, see https://datarecce.io/blog/recce-vs-datafold/
But curious what's working or not

#DataEngineering #DataValidation #dbt

"Do I have to validate all downstream models?" 🤯 This question haunts every data engineer at 11pm before a deploy.

We're obsessed with this problem which led us to build "Impact Radius"

🧵 See our journey https://reccehq.com/blog/Building-Impact-Radius-1/

#datacorrectness #datavalidation #datatests

Benefits of "automate everything" data validation , but hidden cost 💸
1️⃣ Compute Spend
2️⃣ Alert Fatigue
3️⃣ Team Trust

Compare automation-first vs human-in-the-loop: https://datarecce.io/blog/recce-vs-datafold/

#DataEngineering #DataValidation #dbt #DataCosts

Is high-quality data the same as correct data?
No, data can pass every test, but still be wrong 😱

✅ Schema checks
✅ Null constraints
🚫 No correctness validation

Recce introduces a workflow built around data correctness

Find and fix silent errors:
https://reccehq.com/blog/high-quality-data-can-still-be-wrong/

#dataquality #datavalidation #dataengineering

If you know anything about data validation, you must know how vital it is to maintain the accuracy and integrity of data.

See here - https://techchilli.com/artificial-intelligence/pandera-in-python/

#Pandera #Python #DataValidation #TechChilli #DataScience

Choose Recce and Datafold?

Datafold if:
→ large-scale data
→ automated CI/CD coverage all

Recce if:
→ focus on dev-time validation
→ prefer lightweight, open-source flexibility

Full comparison: https://datarecce.io/blog/recce-vs-datafold/

#DataEngineering #DataValidation #dbt #BuyersGuide

Auto-diff every model on every PR? Tempting.
But you’ll get ⚠️ dozens of alerts, most irrelevant.

CI without context = alert spam.

Real-world data work needs more than diffs: what changed, why, and what to do.

Human judgment matters.
Recce helps automate with opinions.

👉 https://datarecce.io/blog/more-than-data-diff/

#dataengineering #datadiff #analyticsengineering #datavalidation

Your data passed all tests but your CEO still questions the quarterly report.
Why the data is “correct” to you but not to your CEO?

#datacorrectness is contextual and temporal.

If it's subjective, why are we still building validation like it's objective?

#datavalidation