Data quality monitoring FAQ

Q: How do I monitor data quality without exposing raw data to a vendor?

Run a monitoring agent inside your own environment. AIMO's agent runs as an open Docker image in your network, connects to your database locally, and sends out only aggregates and metadata — counts, grouped results, and analysis payloads. Bulk raw rows never leave your environment.

Q: What is the difference between data quality and data observability?

Data quality is about whether the content of your tables is correct. Data observability is the broader discipline of understanding the health of your whole data system, often including pipeline lineage and infrastructure. AIMO focuses on table-level data quality with learned outlier detection.

Q: Is AI-based data monitoring secure and GDPR-compliant?

Yes. Database credentials are encrypted with a passphrase that never leaves your environment; only the ciphertext is stored, so AIMO can never read or use them directly. Agents authenticate with Ed25519 keys and JWTs, sign-in is passwordless, and the code that touches your database is open. AIMO is EU-based and GDPR-ready, operated from Finland by Motify Data Mining.

Straight answers to the questions teams ask most when evaluating data quality monitoring. For background, start with What is data quality monitoring? or browse the glossary.

How do I monitor data quality without exposing raw data to a vendor?

Run a monitoring agent inside your own environment instead of shipping rows to an outside service. AIMO's agent runs as an open Docker image in your network, connects to your database locally, and sends out only aggregates and metadata — counts, grouped results, and analysis payloads. Bulk raw rows never leave your environment, so there is no firewall opening and no third-party access to your database.

What is the difference between data quality and data observability?

Data quality is about whether the content of your tables is correct — completeness, freshness, valid ranges, consistent relationships. Data observability is the broader discipline of understanding the health of your whole data system, often including pipeline lineage and infrastructure. AIMO focuses on table-level data quality with learned outlier detection.

Can AI generate data quality checks automatically?

Yes. AIMO analyses your schema and a statistical profile of your data, then uses an LLM to propose monitor types, the columns to watch, and sensible bounds. You review and accept the suggestions, so you get broad coverage in minutes without hand-writing every rule.

How does AIMO detect outliers and anomalies?

After monitors run for a while, AIMO trains a neural-network model on each monitor's historical time series so it learns what "normal" looks like for that specific metric. When a new value deviates drastically from the learned expectation, it is flagged as an outlier and routed to you — far more robust than a fixed threshold.

Which databases can I monitor?

PostgreSQL, MySQL, SQLite, Snowflake, DuckDB, and more through SQLAlchemy drivers. A Python library lets you add other SQLAlchemy-compatible databases and tie monitor calculations into your dataflows, so checks run right after a table finishes updating.

Is AI-based data monitoring secure and GDPR-compliant?

Security is built in. Database credentials are encrypted with a passphrase that never leaves your environment; only the ciphertext is stored, so AIMO can never read or use them directly. Agents authenticate with Ed25519 keys and JWTs over secure channels, sign-in is passwordless, and the code that touches your database is open for inspection. AIMO is EU-based and GDPR-ready, operated from Finland by Motify Data Mining. See security and the legal documents.

How do I get alerted when something breaks?

Alerts are delivered to email, SMS, Slack, or a general webhook. There are sensible defaults mapping severity to channel, and you can manage the routing rules and add new endpoints yourself.

How long does setup take?

Minutes, not months. You run the Docker image, register in the UI, connect a database, analyse and choose tables, and accept the AI-generated monitors. AIMO then backfills history and begins monitoring — the backfill time scales with your data size. See getting started.

How much does data quality monitoring with AIMO cost?

Your first month includes up to three monitored tables at no charge. After that it is €10 per monitored table per month (excl. tax), with no per-seat fees and no platform fee. Add or remove tables in the UI and billing updates the next cycle. See pricing.