Oracle data risks: Is your data AI ready and reliable?

In an era when artificial intelligence is transforming every industry, your Oracle data has never been more valuable or more vulnerable. Data trust is no longer optional. It’s foundational.

What makes Oracle data integrity so challenging?

Oracle environments are often a patchwork of legacy EBS, modern Fusion SaaS modules, and non-Oracle systems like Salesforce, SAP, or Snowflake. These fragmented ecosystems:

• Obscure data lineage
• Complicate transformations and mappings
• Mask integration errors until it’s too late

Misaligned records across Oracle HCM and ERP can derail everything from payroll analytics to SOX-compliant reporting.

And manual testing? It’s no match for the pace of change. As Will Berry, Tricentis Senior Director, aptly said:

“Manual testing is like catching rain with a colander. You might catch something, but most of it slips through.”

When bad data gets expensive

Poor data quality isn’t just an IT issue. It’s a business risk. According to Gartner, bad data costs organizations an average of $15 million per year. But the true cost shows up in:

• Failed audits and compliance violations: Especially under SOX, PCI DSS, and GDPR.
• Broken AI/ML pipelines: Leading to biased predictions or inaccurate automation.
• Customer attrition: 66% of consumers say they’ll stop trusting a brand after a single data mishap.

Consider the Capital One case, where systemic data deficiencies led to $100 million in fines and long-term reputational harm. Better data integrity practices could have prevented the fallout.

3 signs your Oracle data isn’t AI ready

1. You rely on manual “stare and compare” testing
   This approach simply can’t scale. Modern Oracle pipelines, especially with frequent quarterly patches, require automated, continuous testing.
2. Your data validation stops at the database layer
   Errors often arise in transformations between layers or at the application UI level. If you’re not validating Oracle data from ingestion to analytics, you’re missing blind spots.
3. You lack visibility across systems
   Integrations between Fusion ERP, third-party apps, and cloud data warehouses create invisible fault lines. Without end-to-end lineage tracing, even a minor change can have ripple effects.

Building resilience with Tricentis Data Integrity

To ensure your Oracle data is AI-ready, your organization needs an end-to-end data testing strategy that:

• Catches regressions early after patches, integrations, or migrations
• Tests across all layers of data, API, and UI
• Works across Oracle and non-Oracle sources for cloud, on-prem, structured, and unstructured data

Tricentis Data Integrity provides this comprehensive framework. Its model-based automation empowers teams to create tests quickly, scale coverage, and pinpoint root causes.

Data is the fuel, trust is the engine

Your Oracle systems are only as smart as the data that powers them. As AI adoption accelerates, the organizations that invest in data integrity will be the ones that lead, not just in innovation, but in compliance, customer trust, and competitive edge.

Before you double down on Oracle AI capabilities, ask yourself:
Is your data truly AI-ready? Or are you building it on a foundation of risk?

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