Data Scientist
Explore and prepare data
What You Do Today
You pull data from warehouses, lakes, and APIs, then spend significant time cleaning, transforming, and engineering features — handling missing values, encoding categoricals, and creating derived variables.
AI That Applies
AI automates much of data profiling, suggests feature engineering based on data types, and generates cleaning pipelines from natural language descriptions of desired transformations.
Technologies
How It Works
The system ingests natural language descriptions of desired transformations as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — cleaning pipelines from natural language descriptions of desired transformations — surfaces in the existing workflow where the practitioner can review and act on it.
What Changes
The tedious 80% of data prep — profiling, type conversion, null handling — gets automated, letting you focus on creative feature engineering.
What Stays
Knowing which features will actually matter, understanding domain-specific data quirks, and recognizing when data quality issues invalidate your approach.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for explore and prepare data, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long explore and prepare data takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your data engineering lead
“What data do we already have that could improve how we handle explore and prepare data?”
They control the data pipelines that feed your analysis
your VP or director of analytics
“Who on our team has the deepest experience with explore and prepare data, and what tools are they already using?”
They're deciding the team's AI tool adoption strategy
your data governance lead
“If we brought in AI tools for explore and prepare data, what would we measure before and after to know it actually helped?”
AI-generated insights need the same quality standards as manual analysis
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.