Data Quality For Governed AI & Model Training

Are you getting the best out of your data and AI?

Expectation

37.3%

Expected growth rate of AI between 2023 and 2030

72%

of execs see AI as the main business advantage

20%

of EBIT generated from AI by high-performers in AI

Reality

Poor data quality undermining model performace

Heavy data cleansing overhead

Lack of governance around data

Data drift going unnoticed

50-80%

of data scientists time goes on repetitive data collection and cleansing

To be exceptional in AI, you need to be exceptional in data quality

End-to-end data quality
for ML model development

Problem
discovery

Data
collection

Data
cleansing

EDA & model
development

Deployment
& monitoring

SPEED UP DATA COLLECTION

Publish trusted data sets to a central data catalog and put data quality checks in place.

Easily find data sets, understand contents, data quality, and owners of data.

ELIMINATE REPETITIVE DATA CLEANSING

Monitor critical data assets and source data. Catch and remediate issues early, and maintain baseline data quality for ML training data.

Prevent recurring issues from happening with data validation placed at entry points.

Cleanse data efficiently when necessary with a clear view of invalid records.

CATCH DATA DRIFT EARLY

Implement continuous data quality checks for monitoring incoming model data.

Get alerts on DQ issues, anomalies detected by AI, freshness issues, and PII occurring where it shouldn’t.

Investigate issues with data lineage rich in metadata overlays and get to the root cause faster.

See it in action

Deploy more models that work as you intended

20-50% more

model deployments per year

20-50% less

time to resolve issues
and data drift

1-5% better

model precision

Work with the lead innovator in data quality

Unified platform for data discovery and quality

Document, discover, and assess the quality of the data you need in one tool via connected and efficient workflows.

Scalable data quality

Onboard new data sources faster with AI-based data classification and quality checks assigned automatically. Reuse data quality rules and never code repetitive checks again.

Other vendors are transactional in their behavior. With Ataccama, there’s a genuine belief of shared responsibility of success that we feel within T-Mobile.

Daniel West Data Management Lead, T-Mobile

See our customers

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