Clearing & Parsing Your Data - Profiling, Cleaning, & Normalizing Your Data
GenAI systems amplify data quality issues—duplicates, inconsistencies, and malformed fields quickly degrade outputs if data is not rigorously prepared before use.
To win, your GenAI solutions need data that is profiled, cleaned, normalized, and reliably prepared at scale.
When data preparation is ad hoc or manual, teams struggle with:
- Hidden quality issues: Raw data contains inconsistencies, gaps, and anomalies that undermine GenAI outputs.
- Inconsistent formats: Variations in fields, schemas, and structures make data unreliable for AI workflows.
- Fragile preparation processes: Manual or one-off cleaning steps fail to scale or repeat consistently.
Poorly prepared data will degrade GenAI quality, increase rework, and erode trust in AI outputs.
In this hands-on workshop, your team applies systematic techniques to profile, clean, normalize, and automate data preparation for GenAI use.
- Profile raw data to surface quality issues and risks.
- Clean and normalize datasets into consistent, usable forms.
- Standardize formats and fields across sources.
- Resolve duplicates and anomalies that affect GenAI behavior.
- Automate preparation steps to support repeatable data flows.
Profiling Raw Data for Quality Issues
Cleaning and Normalizing Datasets
Standardizing Formats and Fields
Resolving Duplicates and Anomalies
Automating Data Preparation Pipelines
- Identify data quality issues before they impact GenAI outputs.
- Produce normalized datasets suitable for GenAI workflows.
- Apply consistent standards across data sources.
- Reduce noise caused by duplicates and anomalies.
- Establish repeatable, automated data preparation processes.
Who Should Attend:
Solution Essentials
Virtual or in-person
4 hours
Intermediate
Data profiling tools, transformation frameworks, and automated pipeline examples