Ataccama for data modernization

Modernize your data.
Trust it from day one.

Modernizing your data is a big investment. Ataccama helps you protect it. Start clean, stay trusted, and deliver AI-ready data.

Talk to an expert

Leading enterprises choose Ataccama for data trust

A migration moves your data.
It doesn't make it trusted.

Moving data to the cloud is a major milestone. But when poor quality, missing context,
and inconsistent definitions move with the data, AI risk increases, analytics suffer,
and modernization slows down.

ROI

Data modernization
rework is costly

Migrating to Snowflake or Databricks is expensive enough. Bad data only adds more cost through rework, downstream issues, and endless cleanup. Premium compute gets wasted, engineering teams stay stuck in remediation, and modernization ROI starts slipping away.

Scale

The old way of managing
data no longer scales

At cloud scale, manual remediation can't keep up with rising data volumes and AI workloads. Quality rules and definitions stay buried in technical tools, leaving the business without a shared language for trust and data engineering teams stuck owning every issue.

AI & BI

AI & analytics stall
without trusted data

AI is only as reliable as the data behind it. Without trusted, governed, and traceable data, AI models drift, reports get challenged, and regulatory risk grows.

Solution

Build a data trust layer into modernization from day one

Ataccama ONE unifies data quality, observability, catalog, lineage, and reference data in one agentic data trust platform. Validate data before it lands in the cloud, monitor it across every layer, and certify it for AI and analytics. Same rules, same glossary, defined once, enforced everywhere: from legacy systems to the cloud lakehouse.

Data trust layer for your
cloud lakehouse and beyond

Explore how each module of the Ataccama ONE platform helps you build data trust
across your entire data estate and accelerate the flow of trusted data
through the medallion architecture.

The trust layer for your cloud lakehouse and beyond

AI and analytics are only as reliable as the data underneath them. Ataccama enforces quality, governance and observability at every layer of your architecture, inside and outside of your cloud lakehouse, so only trusted data flows forward. At the gold layer, the Data Trust Index certifies datasets as production-ready. Ataccama exposes all trust signals via MCP, so Cortex agents and other AI tools act on certified data.

Data quality executed natively

Ataccama lets business teams define governed DQ rules once and execute them everywhere: at your core data systems, before data enters your cloud lakehouse, and natively inside it. Data Quality Gates enforce validation in pipelines, isolating bad records before they reach bronze, silver and gold layers. Continuous monitoring catches issues early, stewardship workflows route them to the right people to remediate.

Data observability

Ataccama proactively monitors data flowing into your cloud lakehouse, across ingestion pipelines and within the platform itself, so teams catch and resolve issues before they reach downstream consumers. Anomaly detection (volume, freshness, schema drift, data distribution), alerting, and integrated lineage provide full context from root cause to business impact, reducing MTTR and keeping data reliable at scale.

Augmented data lineage from source to consumption

Ataccama delivers end-to-end visibility into data flows across and beyond your cloud lakehouse, from source to consumption. Built-in data quality insights, anomaly alerts, and business context let teams instantly see where and why issues occur, so they can resolve them faster. The result: faster troubleshooting and a trustworthy audit trail.

Enterprise data governance

Ataccama ONE documents data assets inside and outside your cloud lakehouse. The ONE AI agent, your digital data steward, enriches them with governance and business context, validates quality, and certifies them using the Data Trust Index. A built-in business glossary links technical metadata, business terms, and data quality rules, keeping definitions and quality standards consistent across the data estate. Stewardship workflows help you route issues and track resolutions.

Reference data management

Source systems are full of inconsistencies in how they record the same things: country codes, currency codes, product categories, and others. Migrating to Snowflake or Databricks carries those inconsistencies forward. Reference data management gives you a governed set of data values to validate against, and harmonizes those values across your cloud lakehouse and source systems, so reports, analytics, and AI agents all operate on the trusted data.

Explore ONE AI capabilities

From clean ingestion to AI your teams can trust — see how Ataccama ONE
helps at every stage of the data lifecycle.

ONE AI Agent: Let ONE AI Agent scale data trust

Data catalog: Establish a shared business glossary

Data observability: Detect anomalies upstream

Start clean

Shift data quality to the left. Profile source systems, apply governed quality rules, and detect schema or volume changes before they break downstream pipelines. Stop bad data before it spreads across your modern stack.

Key capabilities:

Stay trusted

Validate data natively in your cloud platform with governed data quality rules and against trusted reference data. Isolate invalid records before they propagate downstream, then automate monitoring, root-cause analysis, and remediation with full business context, ticketed in Jira and ServiceNow.

Key capabilities:

Data quality: Monitor quality continuously

Data observability: Monitor data assets & pipelines

Data quality & observability: Operationalize remediation

Reference data management: Distribute trusted reference data

Data catalog: Publish certified data products

Data quality: Track data quality in dashboards

Data lineage: Visualize augmented lineage across systems

Deliver trusted AI & BI

Give your business teams and AI agents trusted signals they can act on. Surface quality scores, business context, and Data Trust Index insights, so everyone understands what data is ready to use.

Key capabilities:

Enterprises modernize faster
with Ataccama

From post-merger chaos
to trusted, AI-ready data

A major merger left the bank with duplicate systems, inconsistent Critical Data Elements, and fragmented quality logic spread across Informatica, SAS, and Excel. As FDIC reporting and AI initiatives accelerated, Ataccama became the data trust layer on Snowflake, continuously validating CDEs and routing remediation directly to business stewards instead of central IT.

Impact:

55+

Data sources profiled.

11,000+

CDEs governed.

Business units covered.

15,000+

Quality rules.

97%

Quality pass rate.

Cortex AI use cases.

FDIC reporting

Shifted from
reactive fire drills
to a managed, audit-
ready operation.

Turning enterprise reporting
into a trusted operation

After consolidating Finance, HR, Supply Chain, and Customer Analytics in Snowflake, the organization still struggled with inconsistent definitions, fragmented validation logic, and recurring reconciliation errors. Ataccama unified validation across domains, centralized issue management, and aligned teams around shared definitions and governed data quality metrics.

Impact:

800+ hours

Reclaimed monthly.

-25%

Reporting delays.

-30%

Reconciliation errors.

-40%

Reporting discrepancies.

Enterprise
-wide trust

In Snowflake data
across business
domains.

Scaling Medicare reporting
without slowing innovation

More than 250 million medical records flowed annually from hundreds of provider organizations to support HEDIS reporting and Medicare Advantage Star ratings, but manual trust checks couldn’t keep pace. As the insurer migrated from Teradata to Snowflake, Ataccama embedded data quality directly into ingestion pipelines, continuously detecting and remediating issues before they impacted regulated reporting.

Impact:

250M+

Records validated.

150+

Orchestrated workflows.

Defensible
confidence

In Snowflake data
across business
domains.

Faster
deployments

Without compromising
engineering and
reporting integrity.