Is Your Data Good Enough for AI?

A Checklist for Data Quality KPIs and Roadblocks

You can’t build intelligent AI systems on unreliable data. Nearly 59% of organizations don’t measure data quality, and it costs them an average of $12.9 million a year. So how do you assess your data’s readiness?  

Siloed systems, manual errors, and legacy architectures can contribute to poor data quality and jeopardize AI/ML outcomes. Using them without preparing the data for AI could result in poor results and decision-making. Let us understand how to define and measure data quality using KPIs while identifying roadblocks and practical solutions. 

What is Data Quality & Why Does It Matter?

Data quality refers to how well data reflects real-world entities and supports its intended use. To accurately measure this, enterprises need to monitor both long-term and operational metrics. 

The two categories of KPIs are: 

  • Common KPIs assess ongoing data health. 
  • Incident-Based KPIs: Focus on errors, pipeline disruptions, and remediation. 

High data quality leads to trustworthy analytics, reliable AI models, and regulatory compliance. Poor quality can lead to decisions based on incorrect data, fines, and reputational damage. 

Types of KPIs to Measure Data Quality

Each KPI type serves a different purpose: 

Use Case 

Common KPIs 

Incident-Based KPIs 

Long-term quality monitoring 

 

 

Real-time issue tracking 

 

 

Data governance & compliance 

 

 

AI/ML model training input validation 

 

 

Pipeline health & error remediation 

 

 

SLA adherence & operational analytics 

 

 

Enterprise reporting/dashboards 

 

 

 

KPIs for Data Quality

Common KPIs 

1. Accessibility & Timeliness 

Accessibility ensures that users can retrieve data when needed. Timeliness ensures that the data is delivered quickly enough to remain actionable for real-time alerts or scheduled reports. 

What It Measures 

Targets 

Risk 

Availability, access speed, query responsiveness, and data delivery timelines 

  • ≥99.9% system uptime,  
  • ≥95% of queries within SLA,  
  • 100% of access granted within 24 hours,  
  • <2s latency for real-time data, 
  •  ≥99% batch completion 

Delays in access or delivery slow decision-making, stall analytics, and reduce AI system effectiveness 

 

2. Completeness & Relevancy 

Completeness ensures that all necessary data is present, while relevancy checks that the data is meaningful and serves its intended business purpose. 

What It Measures 

Targets 

Risk 

Presence of required fields and business alignment of datasets 

  • ≥95% of mandatory fields completed, ≥70% usage in reports,  
  • ≥4.5/5 stakeholder satisfaction 

Missing or irrelevant data causes bias, drives up costs, and leads to model drift or misaligned insights 

 

3. Consistency 

Consistency ensures that data values remain uniform across systems and over time, like maintaining the same product code or customer ID in every system. 

What It Measures 

Targets 

Risk 

Data alignment across platforms and time 

≥99% field consistency & no temporal errors (e.g., age decreases) 

Prevents integration failures, reporting mismatches, and AI model instability 

 

4. Accuracy & Validity 

Accuracy ensures the data correctly reflects reality. Validity ensures the data adheres to defined formats, schema constraints, and business rules. 

What It Measures 

Targets 

Risk 

Real-world correctness and rule-based conformity 

  • ≥98% match to trusted sources,  
  • ≥95% expert agreement,  
  • ≥99% schema compliance 

Errors in accuracy or format compromise compliance, create rework and degrade AI performance 

 

5. Precision 

Precision measures how consistent and repeatable data annotations or labels are across multiple reviewers or systems. 

What It Measures 

Targets 

Risk 

Inter-rater agreement and reproducibility of labels 

≥90% agreement across repeated annotations 

Inconsistent labeling introduces bias and weakens training data for ML models 

 

6. Uniqueness 

Uniqueness ensures that each record appears only once in a dataset with no duplicates or redundancy. 

What It Measures 

Targets 

Risk 

Duplicate record detection and prevention 

≥99% unique records and ≤1% duplicates 

Duplicates inflate costs, skew analytics, and compromise model reliability 

 

Incident-Based KPIs  

1. Total Number of Data Incidents 

This tracks how often data quality issues are detected across systems, providing a snapshot of overall data stability. 

What It Measures 

Targets 

Why It Matters 

Frequency and severity of data quality issues flagged 

<5 critical incidents per day or <50 per month, categorized by severity 

High incident rates mean quality failures that can corrupt downstream analytics and AI pipelines 

 

2. Time to Detection (Median Time to Detect) 

Time to detection measures how long it takes to identify a data issue after it occurs. 

What It Measures 

Targets 

Why It Matters 

The time between issue occurrence and detection 

  • ≤2 hours median, 
  •  ≥95% detected within SLA 

Delayed detection lets faulty data circulate across reports and models, increasing business risk and rework 

 

3. Time to Resolution (Mean Time to Repair, MTTR) 

This metric captures how quickly data issues are resolved once detected. 

What It Measures 

Targets 

Why It Matters 

Duration from issue detection to resolution 

  • <4 hours average,  
  • ≥90% resolved within defined SLA 

Prolonged resolution disrupts pipelines, breaches SLAs, and introduces lag in analytics and AI model updates 

 

4. Data Asset Uptime 

Data asset uptime monitors how consistently key datasets or pipelines remain operational and free from critical errors. 

What It Measures 

Targets 

Why It Matters 

Dataset/pipeline availability and error-free periods 

≥99% uptime for priority assets, tracked over consecutive error-free days 

Downtime affects analytics delivery, disrupts decision workflows, and undermines model accuracy in production 

 

5. Data Usage/Importance Score 

This score ranks datasets based on how frequently they are used, their business importance, and the number of dependent systems or teams. 

What It Measures 

Targets 

Why It Matters 

Composite of usage frequency, value, and downstream impact 

Weighted score based on query volume, consumers, and revenue linkages 

Helps prioritize monitoring and quality improvements for datasets with the most business or AI impact 

Common Roadblocks to Data Quality KPIs & How to Fix Them

Data Silos & Integration Complexity 

  • Impact: Mismatches, redundancies, and gaps across systems hurt data consistency, uniqueness, and accuracy. This leads to flawed analytics and unstable AI pipelines. 
  • Fix: Implement unified data pipelines, centralized schemas, and cross-platform metadata standards to streamline ingestion and ensure consistent definitions. 

Lack of Automated Observability 

  • Impact: Without monitoring, issues like missing fields, schema drift, or delayed batches go undetected, directly affecting timeliness, completeness, and operational SLAs. 
  • Fix: Use automated quality checks, real-time dashboards, and anomaly detection tools to flag and resolve issues before they impact downstream systems. 

Privacy & Compliance Risk 

  • Impact: Invalid or incomplete records risk breaching regulations like GDPR and HIPAA, exposing the business to fines and legal consequences. 
  • Fix: Embed data governance policies, implement validation rules, and enforce audit trails across pipelines. Align practices with frameworks such as ISO 8000-63. 

Bias & Fairness in AI Training 

  • Impact: Skewed or non-representative datasets result in biased models, harming accuracy, precision, and public trust. 
  • Fix: Conduct bias audits, use balanced training data, and integrate HITL validation (Human-in-the-Loop) workflows with SME oversight for critical data. 

Using Data that Delivers for Your Enterprise AI

Strong data quality is important for effective analytics, compliance, and reliable AI. As data volume and AI adoption grow, scalable and ethical data practices continue to become a necessity.  

Start now by evaluating your data quality! Connect with an Innodata expert to explore how our data solutions ensure your enterprise is AI-ready. 

Innodata Inc.

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