Assess & improve data quality for AI & analytics
Deliver accurate, reliable insights by strengthening data trust, reducing ecosystem complexity, and scaling data quality to support faster, more confident AI and analytics outcomes.
Unified trust in data
Strengthen trust and accelerate AI with consistent, scalable data quality.
Data quality is the foundation of every reliable AI and analytics outcome. As data grows more distributed and complex, maintaining trust becomes harder and the consequences of bad data become more costly.
With a consistent, scalable approach to discovering, assessing, and improving data quality, teams can move faster, reduce risk, and deliver insights with confidence.
„If we can provide our teams with quality information, they’re in a better position to make sound investment decisions.”„
Geoff Smith, Head of Data Services
New Zealand Superannuation Fund
Stop guessing and start trusting your data
Discover & assess
Find and understand the right data across your ecosystem, then automatically measure its quality and readiness for AI & analytics, so teams know what’s trusted, what’s at risk, and what to fix first.
Improve quality
Fix data issues at the source using centralized, reusable rules that eliminate manual cleanup and inconsistency.
Automate & scale
Apply data quality and observability everywhere data lives to confidently scale AI and analytics without rework.
Proven results
Reduce complexity
Make all your data discoverable, understandable, and trustworthy for AI & analytics.
A leading UK financial services organization, with over 4,200 advisers, reduced compliance risk, improved reporting accuracy, and raised data literacy by establishing clean, trusted customer data for analytics and reporting.
Improve data trust
Strengthen every AI and analytics initiative with reliable data.
A major Midwest bank with over 600k members turned data from a firefighting liability into a strategic asset, ensuring trusted data & analytics underpins all strategic decisions and customer touchpoints.
Safely scale for growth
Standardize and automate quality so it keeps up with AI.
New Zealand Superfund, a leading sovereign wealth fund, scaled third-party data ingestion and achieved 100% compliance in governance criteria for their core investment data, scaling high quality data to fuel confident decisions and stronger regulatory compliance.
AI Readiness Assessment
The AI Readiness Assessment reveals where poor data quality, governance, and compliance put your AI initiatives at risk. Its structured five-phase framework helps you prioritize improvements and unlock successful AI.
More resources
Frequently Asked Questions
How does Precisely help assess and improve data quality for AI?
Precisely unifies profiling, scoring, rules, and remediation so you can evaluate data quality, fix issues at the source, and monitor key datasets over time. This turns data quality from ad hoc checks into a repeatable process that consistently feeds AI and analytics with trusted data.
Can Precisely fit into our existing workflows and AI-enabled tools?
Yes. Precisely complements existing data platforms, pipelines, BI tools, and AI environments. APIs help teams integrate catalog and data quality capabilities into current workflows, while MCP-enabled access helps AI agents, copilots, and LLM-enabled experiences invoke trusted Suite capabilities through MCP-compatible clients.
Does this replace our existing data tools or BI platforms?
No. Precisely complements your existing stack. It focuses on discovering, assessing, and improving data quality and governance so the tools you already use for integration, BI, and AI models run on more reliable inputs, with fewer surprises and less rework.
Will this work across our hybrid or multi-cloud data environment?
Yes. Precisely is designed to work across modern data platforms, including cloud data warehouses, lakehouses, streaming pipelines, and on-prem systems. You get a consistent view of quality, lineage, and readiness, even when data lives in different technologies or business domains.