AI PRODUCT INTELLIGENCE • DATA

Data Quality Intelligence AI™

From Data Problems → Business Impact → Remediation

Data Quality Intelligence AI dashboard
Start Data Quality Analysis See How It Works

Don't just find bad data. Understand it.

Data Quality Intelligence AI™ profiles your data, identifies meaningful quality problems, explains their potential business impact, and turns findings into prioritized remediation actions.

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Discover & Profile

Understand structure, completeness, patterns, duplicates, data types and anomalies across your dataset.

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AI Issue Intelligence

Group related quality issues, identify patterns and explain likely root causes instead of presenting an overwhelming error list.

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Business Impact

Connect significant data problems to affected reporting, processes, analytics and decision-making areas.

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AI Recommendations

Receive prioritized recommendations with practical actions and a clear reason for each recommendation.

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Guided Remediation

Move from issue identification to a step-by-step remediation plan and track progress toward the target state.

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Target State

Define measurable quality objectives and monitor improvement rather than stopping at a single quality score.

One intelligence journey

The product follows a simple customer journey from raw data to actionable intelligence.

ConnectData source
ProfileUnderstand
DetectFind issues
UnderstandImpact & causes
RecommendPrioritize actions
RemediateImprove & track

Data & AI Intelligence Workspace

Upload your dataset, choose how you want quality issues handled, and generate an intelligent quality assessment and cleansing plan. Large customer files are designed to be stored in Azure rather than consuming the website hosting space.

1. Project & Dataset

Preparing upload… 0%
Workspace access: Each account is intended for one active device/session at a time. Team or multi-user access is managed separately through account access controls.
Data protection principle: Your original uploaded dataset remains unchanged. Cleansing actions create a separate output so the original data can be retained for comparison and audit.
Storage architecture: Website UI on Hostinger → secure upload service → Azure Storage for customer data → Data Quality AI engine.

2. Null Value Handling

What should we do with null values?
Replacement value
In the production engine, replacement can also be configured by column and data type.

3. Duplicate Value Handling

What should we do with duplicates?
Replacement value
Production cleansing will use column-level duplicate rules rather than blindly overwriting records.

4. Output Format

Ready. Quality metrics are 0% until a dataset is uploaded and analyzed.

AI Quality Intelligence

Overall Quality0%
Completeness0%
Validity0%
Consistency0%
Uniqueness0%
AI Decision Summary
  • Profile the uploaded dataset before applying cleansing actions.
  • Keep customer data unchanged unless the user explicitly selects a remediation action.
  • Generate a separate cleaned output when remediation is requested.
Recommended Intelligence Flow
Upload → Profile → Detect → Explain → Ask → Clean → Validate → Export
Customer control: Data Quality Intelligence AI does not silently delete or overwrite data. The customer explicitly chooses the treatment of nulls and duplicates before cleansing.