AI is data-thirsty, but privacy is non-negotiable. Mage Data removes the friction, delivering production-ready, compliant data to your models without the legacy lead times.
THE MARKET REALITY
AI leaders outpace laggards by capturing 2x the revenue growth and 40% more cost savings. But there is a catch: AI is data-thirsty. The reason 50% of GenAI projects fail isn’t the technology—it’s the Data Friction Gap. When your AI initiatives hit the wall of legacy provisioning and privacy constraints, you pay a "30% AI Tax" in lost time and infrastructure bloat. To win, you must solve the underlying data architecture.
Incremental Annual Value Potential in Global Banking contingent on solving the underlying data architecture.
Source: McKinseyStagnating in legacy data infrastructures. Baseline growth and margin parity at risk.
EXPECTED GAINS IN 2026
Navigating privacy frameworks traps data in production silos
For manual IT database clones, destroying agile dev cycles
Caused by creating physical copies of databases
Legacy masking breaks demographic logic, ruining statistical utility for AI needs
THE ROOT CAUSE
Traditional data handling creates invisible bottlenecks across your lifecycle — slowing teams, inflating costs, and starving AI models of usable data.
Our platform eliminates friction at the source. From intelligent discovery to context-aware delivery, get the data you need without the risk.
Deliver governed, production-ready data at speed—without compromising security.
AI-driven data discovery classifies and tags sensitive data across heterogeneous environments using 80+ global and local classifications
Rapid, lightweight database clones provisioned within minutes eliminating the cost and time of physical copies
Delivers data that is virtually irreversible to hackers, yet statistically perfectly accurate for your AI models - demographics and formats remain 100% intact
Instant Return On Investment through infrastructure savings and operational agility.
PER PRODUCTION DATA PIPELINE
Remove friction across your data lifecycle and unlock speed, efficiency, and compliance at scale.
Core banking data is densely interlinked across customers, accounts, transactions and cards, so a usable slice has to preserve referential integrity. Almost all of it is regulated, so no copy can reach a test environment without de-identification first. Mage Data resolves both at once: subsetting that keeps every relationship intact, and masking driven by the same discovery results — so the slice is usable and compliant on day one.
Full-copy refreshes are commonly measured in days, because of database size and the manual masking that follows. Mage Data's automated subsetting and rule-driven masking bring that to hours; database virtualization brings it to minutes, because every new environment is served from one masked snapshot rather than rebuilt from scratch.
Yes, provided masking preserves formats and check digits and stays consistent across systems. Mage Data's format-preserving methods generate card numbers, IBANs and account identifiers that pass Luhn and structural validation, and map the same source value to the same masked value in every system — so validation logic, payment flows and downstream reconciliation behave exactly as in production.
Mage Data's database virtualization creates many virtual copies from a single masked snapshot, storing only the changes each team makes. Storage typically falls by 80 to 90 percent and a new copy provisions in minutes. Because masking happens when the snapshot is created rather than afterwards, every copy is compliant the moment it exists — no re-masking, no rediscovery.
Eliminate data friction, accelerate your AI initiatives, and gain full control of your data with Mage Data’s comprehensive platform. Move faster, stay compliant, and win in the market.
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