Infoverity builds the data quality programs that make enterprise data trustworthy at the source. The same trust now decides whether your AI use cases scale or stall. Less time defending the numbers. More time using them.
It creates daily friction, wasted spend, and compliance risk because no one can consistently prove what’s correct. AI changes the math. BI may tolerate gaps, but generative AI amplifies them into confident, wrong outputs at scale. In AI systems, poor data quality doesn’t degrade gracefully. It compounds.
Why Infoverity. Data quality isn’t a standalone project for us, it’s built into every MDM, governance, integration and AI program we deliver. We’ve delivered 1,000+ programs, including environments with 70M+ governed records and sub-300ms API validation under continuous synchronization.
Infoverity’s expertise has greatly changed the landscape of data at SSM.
"We have not only mastered 4 domains in less than 18 months, we have also been able to create a strategic data roadmap for the next 2 years. The team has helped us build a best-in-class MDM and Data Governance platform, while continuing to accelerate our development of an integrated, self-service data ecosystem for SSM to utilize. We would not be where we are in our data journey without Infoverity as a strategic thought and implementation partner."
We do not stop at dashboards. We define the quality metrics that matter to your business, the thresholds that trigger action, and the stewardship process that keeps the numbers improving over time. Quality stops being an IT report nobody reads.
Deduplication. Address validation. Format standardization. Reference data alignment. We have run these processes against tens of millions of records, including programs that validated more than 200,000 customer addresses in a single pass to prevent missing information downstream.
Generative AI amplifies bad data faster than any system before it. We build quality programs around the four dimensions that hit AI hardest: completeness, consistency, timeliness, and relationship accuracy. So the model gets the data it can actually reason with.
We work across the major data quality, MDM, and integration platforms and recommend what fits your architecture, not what fits our margin. That independence is what makes the strategy credible and the implementation defensible to your board of directors.
Four patterns we see again and again. Each one starts in the data and ends in a business outcome the board will read.
When every report ends in a debate over which version of the data is correct, decisions slow and trust erodes. We diagnose where discrepancies enter the data, fix the root cause in the source and master layers, and stand up the monitoring that keeps the numbers defensible.
Duplicate records. Missing attributes. Inconsistent definitions across ERP, CRM, and downstream platforms. We have removed 20% of duplicate customer records on one engagement, validated 200K+ addresses on another, and stood up reusable cleansing frameworks that scale to the next acquisition or system migration.
Almost always, it is the data feeding the model. Missing values, stale records, inconsistent entity definitions. We diagnose where the data foundation is breaking your AI use cases and sequence the data quality work in the order your AI roadmap actually needs.
Quality programs fail when they cannot show measurable business outcomes. We define the metrics that connect quality to revenue, cost, compliance, and AI readiness, then build the reporting that keeps them visible. According to McKinsey, firms with high data quality can waste between 5x and 6x less employee time on non-value-added tasks.
Customer engagement was spread across 44 disparate data sources, with limited business control over data quality, integration, and governance — blocking personalised marketing and slowing campaign execution to weeks at a time.
Six integrated capabilities. One firm that delivers all of them, so your AI investments don’t fall into the gap between a data team and an AI team.
A focused engagement that profiles your data, identifies where quality issues are costing the business, prioritizes the domains that matter most, and sequences a remediation plan you can fund.
Programs that remove duplicate records, standardize formats and reference data, validate addresses and contact information, and bring your customer, product, and supplier data into a state the business can rely on.
The operating model that keeps quality from drifting after go-live. Stewardship roles, quality rules, automated profiling, and the dashboards that surface issues before they reach the business.
Quality programs scoped specifically around the AI use cases on your roadmap. We measure the dimensions that matter most to your models, monitor for drift patterns that degrade AI outputs, and tie quality work directly to AI ROI.
Validation, enrichment, and exception handling built into the pipelines that move data between systems. Quality enforced where the data flows, not just where it lives.
Governance is what keeps data quality trustworthy after go-live and what makes AI outputs auditable when the question comes. We design the operating model, roles, and policies that hold up under regulatory pressure.
Ready to stop debating the numbers and start using them?
Speak with an Infoverity advisor. In 30 minutes we will walk through where your data quality stands today, the analytics and AI it needs to support, and the most direct path between the two.
No obligation — a 30-minute conversation to understand where you are and what your data and AI goals require.
