Data quality for governed AI & model training

Deploy more models and increase reliability. Eliminate all bottlenecks, free up data scientists’ time, and ensure effective collaboration with data engineering.

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out of your data and AI?

Expectation

37% 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

  • risk

    Poor data quality undermining model performace

  • compliance

    Lack of governance around data

  • data-chaos

    Heavy data cleansing overhead

  • transformation

    Data drift going unnoticed

  • cleansing

    Repetitive data collection and cleansing

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

See the end-to-end data quality process for ML model development

Data collection
Data collection

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.

Data cleansing
Data cleansing

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.

Deployment & monitoring
Deployment & monitoring

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

  • SQL table generation

  • DQ rule generation

  • Apply DQ rules

  • CI description

  • Natual language to SQL

  • AI DQ rules

  • Chat with Documentation

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.

unify

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.

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What our customers say

Daniel West
t-mobile

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
Explore all the
Ataccama
features
Learn more about our capabilities for data quality for governed AI
Data Quality Dashboard

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