# Source: https://www.gigaspaces.com/data-terms/augmented-data-quality

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Conventional RAG Falls Short with Enterprise Databases

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2024/07/solutons.png) Massimo Pezzini, Gartner Analyst Emeritus  ](https://go.gigaspaces.com/5-top-use-cases-for-driving-business-with-data-hub-architecture)

5 Top Use Cases For Driving Business With Data Hub Architecture 

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      * [Budget Management](https://www.gigaspaces.com/use-cases/educational-organizations-budget-resources)
      * [Sales Operations](https://www.gigaspaces.com/use-cases/retail-boost-sales)
      * [Service Providers](https://www.gigaspaces.com/use-cases/contract-control-cost-savings)
      * [Utilities Management](https://www.gigaspaces.com/use-cases/cut-sla-penalties)
      * [Restaurant Management](https://www.gigaspaces.com/use-cases/power-smarter-restaurant-growth-erag)
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      * [Pharma](https://www.gigaspaces.com/case-studies/transforming-patient-outcomes)
      * [Education](https://www.gigaspaces.com/case-studies/achieving-educational-budget-control)
      * [Retail](https://www.gigaspaces.com/case-studies/retail-intelligence-with-erag)
      * [Shipping](https://www.gigaspaces.com/case-studies/redefining-global-logistics)
      * [Energy](https://www.gigaspaces.com/case-studies/boosting-operations-reducing-penalties)
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Monkey See, AI Do - All about CUA

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Taking the AI leap from RAG to TAG

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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/Product-menu-thumb.jpg)](https://go.gigaspaces.com/webinar/why-conventional-rag-falls-short)

Conventional RAG Falls Short with Enterprise Databases

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      * [API Scaling](https://www.gigaspaces.com/use-cases/application-acceleration)
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    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2024/07/solutons.png) Massimo Pezzini, Gartner Analyst Emeritus  ](https://go.gigaspaces.com/5-top-use-cases-for-driving-business-with-data-hub-architecture)

5 Top Use Cases For Driving Business With Data Hub Architecture 

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      * [Multiple Data Sources](https://www.gigaspaces.com/how-it-works/supported-data-sources)
      * [Proactive AI Governance](https://www.gigaspaces.com/how-it-works/proactive-ai-governance)
    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/How-it-works-menu.jpg)](https://go.gigaspaces.com/effective-ai-governance-wp)

Ensure GenAI compliance and governance 

[Read the Whitepaper![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://go.gigaspaces.com/effective-ai-governance-wp)

  * [Success Stories![](https://www.gigaspaces.com/wp-content/uploads/2022/11/Path-16484.svg)](https://www.gigaspaces.com/case-studies)

    * By Use Case

      * [Procurement](https://www.gigaspaces.com/use-cases/procurement-supply-chain)
      * [Operations](https://www.gigaspaces.com/use-cases/enhancing-pharmaceutical-operations)
      * [Budget Management](https://www.gigaspaces.com/use-cases/educational-organizations-budget-resources)
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      * [Utilities Management](https://www.gigaspaces.com/use-cases/cut-sla-penalties)
      * [Restaurant Management](https://www.gigaspaces.com/use-cases/power-smarter-restaurant-growth-erag)
    * By Industry

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      * [Pharma](https://www.gigaspaces.com/case-studies/transforming-patient-outcomes)
      * [Education](https://www.gigaspaces.com/case-studies/achieving-educational-budget-control)
      * [Retail](https://www.gigaspaces.com/case-studies/retail-intelligence-with-erag)
      * [Shipping](https://www.gigaspaces.com/case-studies/redefining-global-logistics)
      * [Energy](https://www.gigaspaces.com/case-studies/boosting-operations-reducing-penalties)
      * [Hospitality](https://www.gigaspaces.com/case-studies/restaurant-increase-profit-erag)
    * [![vid-icon](https://www.gigaspaces.com/wp-content/uploads/2025/02/Company-menu-thumb.jpg)](https://go.gigaspaces.com/webinarod/ecurely-unlocking-structured-data-for-enterprise-ai-0)

Monkey See, AI Do - All about CUA

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Taking the AI leap from RAG to TAG

[Read the Blog![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://www.gigaspaces.com/blog/from-rag-to-tag)

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GigaSpaces, IBM & AWS make AI safer

[Read How![icon](/wp-content/uploads/2023/04/Group-30743.png)](https://www.gigaspaces.com/news/gigaspaces-partners-with-ibm-and-aws-to-deliver-accurate-safe-genai-with-dbs)

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  * Products
    * Our Products
      * [eRAG](https://www.gigaspaces.com/products/erag)
        * [GenAI Catalyst](https://www.gigaspaces.com/products/genai-catalyst)
        * [Instant Data](https://www.gigaspaces.com/products/instant-data)
        * [Respond Proactively](https://www.gigaspaces.com/products/proactive-ai)
        * [Act Autonomously](https://www.gigaspaces.com/products/data-autonomy)
      * [Smart DIH](https://www.gigaspaces.com/products/smart-digital-integration-hub)
      * [XAP](https://www.gigaspaces.com/products/xap-imdg)
    * Solutions for
      * [Pharma](https://www.gigaspaces.com/products/erag/pharma)
      * [Procurement](https://www.gigaspaces.com/products/erag/procurement)
  * Solutions
    * [Digital Innovation Over Legacy Systems](https://www.gigaspaces.com/use-cases/digital-innovation-acceleration)
    * [Integration Data Hub](https://www.gigaspaces.com/technology/integration-data-hub)
    * [API Scaling](https://www.gigaspaces.com/use-cases/application-acceleration)
    * [Hybrid/Multi-cloud Integration](https://www.gigaspaces.com/use-cases/hybrid-or-multi-cloud-integration)
    * [Customer 360](https://www.gigaspaces.com/use-cases/customer-360)
    * [Retail](https://www.gigaspaces.com/retail-and-ecommerce-2)
    * [Financial Services](https://www.gigaspaces.com/financial-services)
    * [Insurance Companies](https://www.gigaspaces.com/industry-solutions/insurance)
  * How it Works
    * [eRAG Technology Overview](https://www.gigaspaces.com/how-it-works/erag-how-it-works)
      * [AI-Ready, IT-Friendly](https://www.gigaspaces.com/products/ai-ready)
      * [Semantic Reasoning](https://www.gigaspaces.com/how-it-works/semantic-reasoning)
      * [Questions to SQL Queries](https://www.gigaspaces.com/how-it-works/questions-into-sql-queries)
      * [Asked & Answered in Natural Language](https://www.gigaspaces.com/how-it-works/asked-answered-natural-language)
      * [Multiple Data Sources](https://www.gigaspaces.com/how-it-works/supported-data-sources)
      * [Governance](https://www.gigaspaces.com/how-it-works/proactive-ai-governance)
  * [Success Stories](https://www.gigaspaces.com/case-studies)
    * By Use Case
      * [Procurement](https://www.gigaspaces.com/use-cases/procurement-supply-chain)
      * [Operations](https://www.gigaspaces.com/use-cases/enhancing-pharmaceutical-operations)
      * [Budget Management](https://www.gigaspaces.com/use-cases/educational-organizations-budget-resources)
      * [Sales Operations](https://www.gigaspaces.com/use-cases/retail-boost-sales)
      * [Service Providers](https://www.gigaspaces.com/use-cases/contract-control-cost-savings)
      * [Utilities Management](https://www.gigaspaces.com/use-cases/cut-sla-penalties)
      * [Restaurant Management](https://www.gigaspaces.com/use-cases/power-smarter-restaurant-growth-erag)
    * By Industry
      * [Logistics](https://www.gigaspaces.com/case-studies/optimizing-supply-chain-with-ai-powered-insights)
      * [Pharma](https://www.gigaspaces.com/case-studies/transforming-patient-outcomes)
      * [Education](https://www.gigaspaces.com/case-studies/achieving-educational-budget-control)
      * [Retail](https://www.gigaspaces.com/case-studies/retail-intelligence-with-erag)
      * [Shipping](https://www.gigaspaces.com/case-studies/redefining-global-logistics)
      * [Energy](https://www.gigaspaces.com/case-studies/boosting-operations-reducing-penalties)
      * [Hospitality](https://www.gigaspaces.com/case-studies/restaurant-increase-profit-erag)
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# Augmented Data Quality

[More Glossary Terms](https://www.gigaspaces.com/data-terms)

In the age of big data, keeping data clean is not just for IT teams, it’s a job for machines now. Automation and AI help us check, fix, and improve data. This is called augmented data quality. It uses smart tools to make data better, faster, and ready to use.

## What is Augmented Data Quality?

Augmented data quality involves the use of artificial intelligence (AI), machine learning (ML), and deep analytics to automate and optimize data quality processes. [Traditional data quality](https://www.gigaspaces.com/news/the-next-wave-of-ops-advances-on-the-data-center) initiatives are usually mundane, rule-driven operations involving tasks such as deduplication, standardization, and validation. Augmented approaches, on the other hand, use AI to learn from data, identify patterns, recommend improvements, and even take corrective action autonomously. 

Rather than relying solely on human intervention, organizations utilizing AI-enforced data quality tools are able to identify errors before they happen, suggest fixes, and maintain quality in fast-moving, high-data volumes. It is a highly critical aspect of augmented data integration and augmented data management strategy. 

## How Augmented Data Quality Works

Essentially, [augmented data quality](https://www.gigaspaces.com/blog/from-rag-to-tag) marries machine learning with human judgment. AI and ML are used to examine vast amounts of structured and unstructured data for anomalies, inconsistencies, or missing values that could undermine data integrity.

Here’s how it typically functions: 

**Profiling:** The system assesses collections of information to understand structure, completeness, and possible quality issues. 

**Anomaly Detection:** Statistical patterns identify outliers or anomalous patterns that could be errors.

**Rule Recommendation:** Based on experience and expert domain knowledge, the framework can suggest or automatically generate data quality rules. 

**Remediation:** The system will notify data stewards or take actions such as cleaning, enriching, or deduplicating data. 

**Continuous Monitoring:** Enhanced systems provide real-time assessment to ensure levels of quality are maintained as data evolves.

These systems have a propensity to interface favorably with existing data infrastructures, thus enabling smarter data integration efforts as clean, trustworthy data is able to pass throughout the entire pipeline.

## The Benefits of Augmented Data Quality

Organizations that adopt augmented data quality tools unlock several strategic advantages:

**Scalability and Speed**

The manual data quality processes cannot scale in the current data infrastructure. The AI-based tools can clean and process data in real-time even for petabyte-scale data.

**Improved Accuracy**

Machine learning algorithms can detect small faults and complex patterns that the traditional rule-based system might miss, thus providing [much better accuracy](https://www.gigaspaces.com/blog/rag-technology-increasing-genai-accuracy) and completeness of the data.

**Operational Efficiency**

Through automating routine tasks such as data matching, rule definition, and anomaly detection, teams are free to focus on more valuable activities such as data strategy and governance.

**Faster Time to Insight**

Reliable, high-quality data means business intelligence and analytics platforms deliver more trustworthy insights, faster. This accelerates decision-making and innovation.

**Cost Reduction**

Cleaner data leads to fewer downstream issues, such as failed analytics projects, misinformed decisions, or compliance violations, saving time and money.

## Key Features to Look For in Augmented Data Quality Tools

If you're evaluating AI-augmented data quality platforms, here are some essential features to consider:

  * **Automated Rule Generation** : The ability to recommend or auto-generate data quality rules using ML.
  * **Natural Language Interfaces** : Easy-to-use interfaces that allow business users to interact with data quality functions without deep technical knowledge. 


  * **Self-Learning Algorithms:** Improves its accuracy and recommendations over time depending on data behaviour and feedback from users. 


  * **Data Lineage and Traceability:** Features that capture changes and help users understand how and why data was modified. 


  * **Collaboration Tools:** Workflow support that allows data stewards, analysts, and engineers to collaborate seamlessly. ****


  * **Real-Time Monitoring:** The ability to constantly monitor and alert on data quality issues as new data arrives. 


  * **Integration Capabilities:** Support for popular data lakes, warehouses, and integration tools to fit into modern data architectures.



## Best Practices for Augmented Data Quality

To maximize the value of augmented data quality solutions, organizations should adopt these best practices:

**Start with Clear Objectives**

Define what “good quality data” looks like in your business context—completeness, accuracy, timeliness, etc. This guides model training and rule creation.

**Prioritize High-Impact Data**

Focus on the data assets that influence key decisions, customer experiences, or compliance risks. Augmented tools can help prioritize issues based on business impact.

**Involve Domain Experts**

AI can suggest rules, but it is human expertise that validates them. Drive collaboration between data scientists, business analysts, and IT teams.

**Enable Feedback Loops**

The best AI systems learn from user actions. Provide mechanisms for users to approve, reject, or refine rule recommendations to improve future performance.

**Measure and Monitor Continuously**

Establish KPIs for data quality (such as error rates, time to fix issues) and track them over time. Augmented platforms should offer dashboards and reports for ongoing visibility.

**Ensure Governance Alignment**

Embed data quality efforts into your broader data governance framework. Augmented solutions should support compliance, auditability, and metadata management.

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