• Daily Success Snacks
  • Posts
  • Why Data Scientists Are Still Cleaning Data in 2026 (And How to Finally Stop It)

Why Data Scientists Are Still Cleaning Data in 2026 (And How to Finally Stop It)

If your team keeps cleaning the same dataset over and over, the problem isn’t the data. It’s the system.

Author

Glenda Carnate
March 05, 2026

Read time: 2.5 minutes

A data scientist uses a notebook for a modeling project. When they start the modeling part of the project, they have already cleaned up and transformed the data by fixing timestamps, removing duplicates, and standardizing IDs.

Two weeks later, another teammate is using the same dataset, performing the same cleaning procedures. Both of them are spending hours recreating the same cleaning tasks on the same dataset.

The problem does not lie in having messy data. The problem is that data cleaning processes are stored in notebooks rather than in centralized systems.

5 Harsh Realities of Why Data Scientists Get Trapped Cleaning Data

1. If You Are Cleaning the Same Data Twice, You Do Not Have A Pipeline
This is referred to as manual work disguised as analytics.
The Solution: You Build a Bronze > Silver > Gold Pipeline

  • Bronze (Raw Data, Ingested)

  • Silver (Cleaned Tables)

  • Gold (Feature For Modeling)

You Do Not Model Until The Silver Exists.

2. Data Cleaning Is Often An Ownership Failure
Data Science Teams are quietly fixing source issues therefore, the real problem is not being resolved.
The Solution: Create Data Contracts with a clear definition of the Schemas, required columns, and NULL limitations.

3. Untested Data = Messy Data
Most pipelines have no validation.
The Solution: Create Automated Data Quality tests such as Schema Checks, Row Counts, NULL Rates and Duplicate Checks.

4. Dirty Labels = Poor Models
Training labels are more important than Algorithms.
The Solution: Audit labels using Sampling Review and Annotator Agreement Check.

5. Notebook Cleaning Will No Longer Scale
Fixing by hand locks the team into repetitive work.
The Solution: Convert Cleaning Logic to Versioning Pipeline and Clean Transformations to be reused.

💡Key Takeaway: 

Data scientists should not focus on dataset cleaning but rather on generating insights and building models based on them.

If you clean a dataset multiple times, it is not the dataset that is the issue... it is the underlying architecture.

👉 LIKE this if you're still spending the majority of your time cleaning data.

👉 SUBSCRIBE now for frequent practical insights about data engineering and modern analytics and how to build better data systems.

👉 Follow Glenda Carnate for weekly breakdowns on building better data systems.

👉 COMMENT with the most frustrating problem your team has when it comes to dealing with data.

👉 SHARE this with a data scientist who is frustrated by cleaning the same dataset multiple times.

Reply

or to participate.