AI TRAINING
Data Quality Essentials for AI Projects
Equip your team to profile, clean, validate, and monitor data so AI projects deliver reliable results.
What it covers
This course covers the most common data quality failures that derail AI initiatives and provides hands-on techniques to prevent them. Participants learn to profile datasets, identify and resolve common issues such as missing values, duplicates, schema drift, and label noise, and build validation pipelines. The programme combines conceptual frameworks with practical tooling (Great Expectations, dbt tests, Pandas Profiling) to embed quality checks at every stage of the data lifecycle. Delivered as a blended workshop or self-paced module, it targets data analysts and engineers working on or alongside AI/ML pipelines.
What you'll be able to do
- Run a full data quality profile on a new dataset and document its key risks before model training begins
- Identify and remediate at least five common data quality issues (nulls, duplicates, outliers, schema drift, label noise) using Python or SQL
- Write automated data validation tests using Great Expectations or dbt that can be integrated into a CI/CD pipeline
- Design a data quality monitoring dashboard that alerts the team when upstream data deviates from expected distributions
- Conduct a root-cause analysis linking a drop in model performance to a specific upstream data quality issue
Topics covered
- Data profiling: distributions, cardinality, nulls, and outlier detection
- Common AI-specific data quality failure modes (label noise, feature leakage, schema drift)
- Data cleaning strategies: imputation, deduplication, standardisation
- Validation frameworks: writing and automating data quality tests with Great Expectations or dbt
- Data lineage and documentation practices for auditability
- Monitoring data quality in production pipelines
- Root-cause analysis when a model degrades due to data issues
- Building a data quality scorecard for ongoing reporting
Delivery
Typically delivered as two or three half-day sessions (in-person or remote) with a 60/40 split between hands-on labs and concept sessions. Participants work on a shared sample dataset as well as optionally their own organisational data. Materials include slide decks, Jupyter notebooks, a Great Expectations starter configuration, and a data quality scorecard template. A follow-up async review session is recommended two weeks post-training.
What makes it work
- Assigning a named data quality owner per dataset or pipeline domain
- Integrating automated validation tests into CI/CD so failures block bad data from reaching models
- Establishing a shared data quality scorecard reviewed in regular team standups
- Starting with the highest-impact datasets feeding live AI models rather than attempting to fix everything at once
Common mistakes
- Treating data quality as a one-time pre-processing step rather than a continuous pipeline concern
- Focusing only on completeness (nulls) while ignoring distributional drift and label quality
- Building quality checks in notebooks instead of automating them as part of the data pipeline
- Skipping data quality documentation, making it impossible to audit why a model behaved unexpectedly in production
When NOT to take this
If the organisation has not yet identified a specific AI use case or does not have data engineers responsible for pipelines, this training is premature — a broader data strategy or data infrastructure workshop should come first.
Providers to consider
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.