DataOps for Trusted Data and Reliable AI
Combine AI-assisted data quality, observability, and MDM to detect issues early and deliver trusted data for business decisions and AI applications.
Combine AI-assisted data quality, observability, and MDM to detect issues early and deliver trusted data for business decisions and AI applications.
Trusted by consumer brands
What is changing in data
of data teams say the business finds data issues first, all or most of the time
of AI projects unsupported by AI-ready data will be abandoned through 2026, Gartner predicts
of organizations lack, or are unsure they have, the right data management practices for AI
Sources: 1.Monte Carlo and CDO Magazine, March 2026. 2. dbt Labs.
Use cases
Monitor data freshness, volume, schema changes and drift. Trace the impact through lineage and alert the right owners when something breaks.
Data ObservabilityLineageModeling
Duplicate customer records across CRM, ERP and billing fragment customer views and distort spend, loyalty and credit analysis. Match records using evidence, apply survivorship rules, and create one trusted record across systems.
Master Data Management
Define quality rules with domain owners across dimensions, run them as automated checks, and track results through scorecards from individual rules to the enterprise level.
Data Quality & ObservabilityFoundation StudioAI & Data Governance
Check source freshness, document versions, vector-index drift and data quality before AI retrieves information or takes action. Flag unreliable answers and pause agent actions when critical data checks or SLAs fail.
AI & Data GovernanceMachine LearningAgentic Governance
Connect legal entities through versioned hierarchies, enforce role-based access, and maintain an auditable record of every match, merge and override.
Governance Operating ModelAI & Data GovernanceGovernance Assessment
The path
1Platform for DataOps
We combine data quality, observability, MDM and governance with reusable IP accelerators to make data reliable, traceable and ready for analytics and AI.
1Platform IPBusiness transformation
AI foundationsData decisions
Pipeline reliability
Duplicate records
Quality rules
AI readiness
ERPFoundation Studio
80–90%of your data foundation pre-built
MDM Studio
4 core capabilitiespre-built for mastering and managing trusted records
GRC Pulse
100% traceabilityContinuous control assurance
Unified sales, supply chain, marketing and HR data to enable faster business decisions.
Standardizing product data to scale catalog operations.
Every failed check becomes an owned, tracked and closed issue, and every closed issue makes the next one less likely.
Critical data and business owners come first. Technology follows.
Data owners approve every rule and sign every match threshold.
Every match shows its evidence. Every merge is reversible.
1Platform built in, or Databricks, Snowflake, Microsoft Fabric, dbt tests, Great Expectations and Soda.
SOC 2 Type II and ISO 27001. Checks run inside your platform.
Partner
Partner
Partner
FAQ
We assess critical data domains, profile data quality, and identify recurring failures across pipelines. This helps prioritise issues based on business impact, data dependencies, and the effort required to resolve them.
Yes. The approach is designed to work with your existing data ecosystem. We configure data quality checks, observability, and master data capabilities around your current platforms and integrate with the systems your teams already use.
We establish domain ownership, define quality rules and thresholds, and set up monitoring, alert routing, remediation workflows, and runbooks. Issues can then be traced to their source, assigned to accountable owners, and verified after resolution.
We establish a baseline and track measures such as data quality scores, pipeline freshness, incident recurrence, resolution time, SLA adherence, and the adoption of mastered records across downstream systems. The measures are aligned with the business outcomes agreed at the start of the engagement.
Bring your data challenges. We’ll help identify the gaps and where to start.
Senior Vice President
Assistant Vice President