Data integrity inside every pipeline
DataConnect combines hybrid integration with AI-powered data quality so teams can automatically assess, remediate, and monitor data as it flows through pipelines, before issues reach analytics, operations, or AI.
Most enterprise AI runs on data that was never good enough
Disconnected systems, manual rules, and reactive processes leave data teams chasing problems instead of enabling analytics and AI. Every new data source, schema change, and pipeline increases the effort required to deliver trusted data at the speed the business demands.
Data fragmented across systems
Data is spread across cloud, on-premises, and hybrid architectures, making it difficult to maintain consistent quality and trust.
Quality checked too late
Many organizations don’t discover quality issues until they reach dashboards, analytics, or AI, when remediation is more costly and disruptive.
Manual data quality can’t keep up
As data volumes grow, schemas evolve, and new sources are added, manual profiling, rule maintenance, and remediation become difficult to scale.
Data quality starts inside the pipeline
Traditional data quality begins after data has already moved. Issues that surface in dashboards or AI outputs started earlier, in pipelines, and they’re more expensive to fix once they’ve spread. DataConnect continuously assesses, remediates, and monitors data as it flows through pipelines, giving teams the visibility to identify issues early and address them before they spread downstream.
Everything you need for ongoing data integrity
DataConnect combines hybrid integration, AI-assisted data quality, and pipeline observability in a single platform.
Bring your data together
Connect hybrid systems without replacing existing investments.
Integrate cloud, on-premises, SaaS, modern data warehouses, and legacy systems through a single platform with 200+ pre-built connectors and Apache Spark processing. Visual pipelines and enterprise orchestration move data wherever it needs to go without replacing existing investments.
Improve data integrity
Identify and remediate issues before they impact analytics, operations, or AI.
AI-assisted assessment profiles data against 1,200+ semantic type definitions to identify anomalies, classify fields, and generate quality rules automatically. Teams review AI recommendations alongside the actual data, then apply remediation with a single click rather than writing rules by hand.
Trust the data behind every decision
Monitor data quality from source to consumption.
The Data Quality Index tracks dataset fitness over time. Schema drift detection, threshold alerts, and pipeline observability surface emerging issues early so teams can act before problems reach downstream analytics or AI.
See Data Integrity in Action
From raw data to trusted pipelines in four steps
DataConnect combines hybrid integration, AI-assisted remediation, and continuous monitoring to deliver trusted data across the enterprise.
Bring data together
Connect cloud, on-premises, SaaS, databases, and legacy systems through a hybrid integration platform without replacing existing investments.
Understand data quality
Profile data, detect anomalies, assess quality, and identify where issues originate and what they affect.
Improve data integrity
Apply AI-assisted recommendations, standardize data, and automate corrections before issues reach analytics, operations, or AI.
Maintain data integrity over time
Track data quality, schema drift, lineage, and pipeline observability to identify emerging issues early and verify remediation efforts are working.
See DataConnect in action
Five data problems DataConnect is built for
Prepare trusted data for AI
AI amplifies the quality of the data it receives. DataConnect profiles, remediates, and standardizes data before it reaches AI models, reducing the risk of unreliable outputs and improving the accuracy of AI-driven decisions at scale.
Build analytics on data you trust
Quality issues in dashboards start as quality issues in pipelines. DataConnect catches and resolves problems upstream so business metrics, reports, and KPIs reflect accurate data from the moment they’re published.
Connect data across hybrid environments
Move data across cloud, on-premises, SaaS, and legacy systems through a single integration platform while assessing and improving quality as it flows. 200+ pre-built connectors cover modern platforms and legacy technologies, including mainframe and AS/400 environments, that cloud-native tools don’t reach.
Reduce manual data quality work
DataConnect builds your data quality solution based on what it finds in the scan. Teams review and approve AI-generated rules rather than writing them by hand, and remediation runs automatically once approved. Less time on rules means more time on new data initiatives.
Replace fragmented approaches
Most organizations run multiple data quality tools with inconsistent approaches and no unified view of data health. DataConnect provides a single workflow for profiling, remediation, monitoring, and pipeline observability across hybrid environments, reducing tooling overhead and delivering a consistent data integrity standard.
Designed for modern data integration and quality
Integrate, assess, remediate, and monitor data with hybrid integration, AI-assisted automation, and continuous visibility from source to consumption.
Assess data quality with AI assistance
Automatically assess data quality using 1,200+ semantic type definitions to profile data, identify anomalies, classify fields, and recommend quality rules. Assessment results help teams prioritize remediation based on the issues that matter most.
Fix data issues with AI-generated rules in one click
Improve data quality with AI-assisted remediation recommendations and automated rule generation that reduce manual effort, improve consistency, and help resolve issues before they affect downstream systems.
Monitor data quality before problems spread
Maintain continuous visibility into data quality with the Data Quality Index, schema drift detection, and pipeline observability to identify emerging issues early and measure remediation progress.
Connect hybrid data without compromising quality
Connect cloud, on-premises, SaaS, and legacy systems with 200+ connectors and Apache Spark processing while applying data quality as data moves between systems.
See data quality in context
End-to-end lineage shows where data originates, how it moves through pipelines, and where quality issues first appear. Integration with the Actian Data Intelligence Platform extends visibility across catalog, enterprise data observability, and analytics, so teams can understand the full downstream impact of quality issues.
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Assess data quality with AI assistance
Automatically assess data quality using 1,200+ semantic type definitions to profile data, identify anomalies, classify fields, and recommend quality rules. Assessment results help teams prioritize remediation based on the issues that matter most.
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Fix data issues with AI-generated rules in one click
Improve data quality with AI-assisted remediation recommendations and automated rule generation that reduce manual effort, improve consistency, and help resolve issues before they affect downstream systems.
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Monitor data quality before problems spread
Maintain continuous visibility into data quality with the Data Quality Index, schema drift detection, and pipeline observability to identify emerging issues early and measure remediation progress.
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Connect hybrid data without compromising quality
Connect cloud, on-premises, SaaS, and legacy systems with 200+ connectors and Apache Spark processing while applying data quality as data moves between systems.
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See data quality in context
End-to-end lineage shows where data originates, how it moves through pipelines, and where quality issues first appear. Integration with the Actian Data Intelligence Platform extends visibility across catalog, enterprise data observability, and analytics, so teams can understand the full downstream impact of quality issues.
Part of Actian's full data value chain for the AI era
Quality in the pipeline
Assessment, remediation, and monitoring run as data moves through integration workflows. Teams address issues where they originate, before problems compound in downstream systems.
Full record context
When DataConnect flags a quality issue, teams see the complete record in context, not just the affected field. That context makes it faster to understand business impact, validate a fix, and build rules that actually work.
Bring your own LLM
DataConnect authenticates against any AI service. Teams choose their own models, manage token costs by job type, and maintain full traceability into how AI recommendations are generated. No lock-in.
Foundation for data intelligence
DataConnect serves as the integration and quality layer for the Actian Data Intelligence Platform, delivering the trusted data that catalog, enterprise data observability, and analytics capabilities depend on.
See DataConnect in Action
Book a 30-minute demo. We show you how DataConnect profiles, remediates, and monitors data across a real hybrid environment.
What you’ll see:
- AI-assisted profiling that scans your data and generates quality rules automatically.
- Intelligent remediation with full record context, so teams can validate every fix before it runs.
- The Data Quality Index tracks data health in real time.
- Hybrid connectivity across cloud, on-premises, SaaS, and legacy systems.
- Bring Your Own LLM: authenticate against any AI service and manage costs by job.
- How DataConnect fits into the broader Actian Data Intelligence Platform.
AI-assisted profiling that scans your data and generates quality rules automatically.Book a Live Demo of DataConnect
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